A Mini Research Report
Submitted to:
Far Western University
Research Innovation and Development Center
Far Western University
Mahendranagar, Kanchanpur
July, 2025
Submitted by:
Dammar Singh Saud
Assistant Professor of English Language Education
Darchula Multiple Campus
Far Western University, Nepal
Declaration
I, Mr. Dammar Singh Saud hereby testify that the research work included in this mini research titled “Opportunities and Challenges of Using Generative AI in English Language Teaching” is my original work. No institution has accepted this research report or part of it to provide an award, degree or any other academic objective. I have duly used the sources obtained due to secondary sources where necessary and all the source of information and literature utilized have been referenced in the reference list of the report. It is only my responsibility to ensure the originality of the whole content of this research work.
Signature of the Candidate: ____________________
Date of Issue: ____________________
Acknowledgement
I would like to thank the Research Innovation and Development Center (RIDC), Far Western University, who gave me the mini research grant and offered the institutional support to conduct this study titled “Opportunities and Challenges of Integrating Generative AI in English Language Teaching”. RIDC has been able to help in the initiation, conduction and the completion of this research through their financial and academic support. I am also grateful to the university leadership to have created a culture of academic investigation and innovation at its constituent campuses.
I would like to give a heart-felt gratitude to the four English language teacher educators who generously shared their experience of using AI in their teaching practices in this study. Their willingness to share their lived experiences and reflective insights were invaluable data that influenced the path and the results of this study. I also appreciate my fellow Darchula Multiple Campus staff members who supported me in this academic pursuit, provided constructive criticism, and cooperated with me during this pursuit.
Finally, I owe a lot to my family and friends because their support in the form of encouragement, patience, and moral support allowed me to take this work seriously. Although there were many people who directly or indirectly helped me in this research, I fully take any limitations that might be present in the report. Personally, I hope that this paper could be an effective addition to the new research on the assimilation of generative AI in English Language Teaching.
Dammar Singh Saud
Assistant Professor of English Language Education
Darchula Multiple Campus
Far Western University, Nepal
July, 2025
Abstract
With the rapid development of generative AI (Artificial Intelligence), the practices of English Language Teaching (ELT) is being transformed, providing new opportunities in teaching, interacting, and personalizing learning. With AI tools, like ChatGPT, Google Gemini, and Microsoft Copilot, becoming prominent in the educational environment, the question of how they should be viewed in terms of pedagogy becomes more and more urgent. The mini research entitled “Opportunities and Challenges of Integrating Generative AI in English Language Teaching”, explores the experiences of using generative AI in teaching experienced by university-level English teacher educators in Nepal.
The research is based on the sociocultural theory and the connectivism which uses the hermeneutic phenomenological research approach to examine the lived experiences of four English language teacher educators. The study reveals the dual reality of the role of AI in ELT by using semi-structured interviewing and a thematic analysis. To the extent, generative AI presents a great number of prospects: it allows tailored learning, i.e. it provides instructional material with individual needs of the learner in mind, allows diverse and interactive language training, allows learners with marginalized or distant needs to learn, and assists teachers in teaching inclusively. Conversely, the results identify such challenges as the fear of the inaccuracy and inappropriateness of AI-generated content, ethical concerns about the use of data and its bias, insufficient training of teachers, and cost-effectiveness of technology adoption.
The participants underlined the significance of a considerate integration, in which AI supplements, instead of substitutes human teaching. The paper has drawn the conclusion that although generative AI can truly revolutionize ELT, its implementation requires responsible use, institutional backing, ethical management, and continuous professional growth to be successful. The results inform the growing scholarly and policy-focused discussion of educational technology and provide usage-based suggestions to educators, policymakers, and developers to embrace the potential of AI in pedagogically valid and equitable manners.
Keywords: Personalized Learning, Language Practice, Accessibility, Teacher Training, AI Integration, Pedagogical Innovations
Table of Contents
1.8 Delimitations of the Study
3.4 Research Site, Participants, and Sampling Design
4.1.2 Enhanced Language Practice
4.1.3 Accessibility and Inclusivity
4.1.4 Student Engagement and Motivation
4.1.5 Challenges in AI Integration
4.2.1 Personalized Learning: A Double-Edged Sword
4.2.2 Enhanced Language Practice: Opportunities and Limitations
4.2.3 Accessibility and Inclusivity: Expanding Horizons
4.2.4 Resource Creation and Efficiency: Enhancing Teacher Capabilities
4.2.5 Student Engagement and Motivation: Leveraging AI for Interactive Learning
4.2.6 Ethical and Privacy Considerations: Navigating the Complex Landscape
4.2.7 Teacher Training and Professional Development: Building Capacity for AI Integration
4.2.8 Addressing Financial Barriers: Sustainable Implementation Strategies
4.2.9 Future Directions: Continuous Research and Development
Summary, Conclusion and Recommendations
Semi-Structured Interview Schedule
Chapter I
Introduction
Generative artificial intelligence (AI) is transforming English Language Teaching (ELT) and offers opportunities of a personalized learning experience and accessibility. In this study, the researcher explores the experiences and the journey of the English language teacher educators in the university level in Nepal with respect to adopting generative AI in their teaching. The chapter gives the background, problem statement, justification, objectives, research questions, significance and limitation of the research, thereby offering a platform on which to proceed with the exploration of lived experiences of English teacher educators in this new emerging technological space.
1.1 Background of the Study
The high rate of progress and development of the generative artificial intelligence technologies have had a significant influence on many sectors including education. ChatGPT by OpenAI and other large language models are generative AI tools that have shown potentially new possibilities in the English Language Teaching sector, such as personalizing the learning experience, making students more engaged, and learning materials (Huang et al., 2023; Rusmiyanto et al., 2023). Human-like text, imitation of conversations, and adaptability to individual needs of learners are also things that such tools can produce, and that is why they are particularly appealing to language teachers interested in increasing the efficiency and effectiveness of teaching.
The promise of generative AI to be consistent has been noted by theorists as consistent with familiar theories of learning. In particular, as an example, the sociocultural theory proposed by Vygotsky (1978) puts much emphasis on mediated learning and the Zone of Proximal Development (ZPD), which presupposes that AI can be employed as a scaffold, enabling the development of learners with references to prompt and individualized feedback and communication. Similarly, according to the theory of connectivism created by Siemens (2005), AI is a learning node that enables the learner to navigate, get access to and construct the knowledge through the digital networks.
Nevertheless, there are some obstacles to the implementation of generative AI in ELT. It has been noted that AI-generated content is not as precise or appropriate as it seems, that there are ethical concerns about the data privacy and bias of the algorithm, and that teachers should be trained to use such tools (Dai and Liu, 2024; Hockly, 2023; Mukhamedov, 2024). Unequal access to AI-powered education is also constrained by financial limitations and digital disparities (Crompton et al., 2024; Sharifuddin and Hashim, 2024).
Although recent years have seen an increase in the literature on AI in the educational field, a lack of phenomenological studies covering the first-person experience of English language teachers using these technologies is studied. In this study, thus, the aims to address this gap by exploring the perceptions and experiences of university-level English teacher educators in their view of the opportunities and challenges of incorporating generative AI into their pedagogical initiatives.
1.2 Statement of the Problem
Although the technologies of generative AI have been promising in terms of innovations in the English Language Teaching field, the discussion of its relevance to the pedagogical processes is still complicated and disputable. The growing popularity of such tools as ChatGPT and other similar language models begs some key questions regarding how these technologies transform the way teaching is conducted, what students learn, and how teachers and students interact. Despite some publications that have been devoted to the theoretical advantages of AI in the context of individualized learning, language practice, and accessibility (Kostka and Toncelli, 2023; Ozdere, 2023), there is scant literature on the practical aspects of reality and lived experience of teachers who are already working directly with such tools in their classrooms.
The increasing fear is that due to the lack of proper pedagogical models and teacher education, as well as ethical standards, generative AI integration can result in such problems as content delivery errors, ethical and privacy concerns, teacher addiction, and unequal access to technology due to the digital divide (Hockly, 2023; Mukhamedov, 2024). With these complications, there is an immediate necessity to learn not only the benefits but also the limitations to which educators are exposed when implementing generative AI in ELT. This research paper fills this gap by exploring the actual life experience of university-level English teacher educators who have used generative AI in their instructional settings.
1.3 Rationale of the Study
The growing adoption of generative AI in the educational environment creates the need to critically analyze the effects of AI on the educational process. Despite the extensive theoretical debates concerning the possibilities of AI, the lack of phenomenological studies exploring the real-life experiences of teachers using these technologies is also very evident. The purpose of this study is to fill this gap and explore the lived experiences of university-level teacher educators of English who have integrated generative AI into their teaching. The knowledge obtained in the course of this study can become a beneficial source of information on the practice of AI implementation in English language teaching that can be used to develop an efficient plan of introducing AI implementation and define the issues that should be resolved. This study aims to help educators and policymakers decide on the introduction of AI in education in an informed way by closing the gap between theoretical possibilities and real practice.
1.4 Research Objectives
The primary aim of this study is to explore and analyze the integration of generative AI in English Language Teaching among university-level English teacher educators. The study is guided by the following specific objectives:
- Explore the lived experiences of university-level English teacher educators in integrating generative AI tools into their teaching.
- Explore the challenges and opportunities faced by these educators in implementing generative AI within their teaching practices.
1.5 Research Questions
The study is guided by the following research questions:
- How do university-level English teacher educators describe and interpret their lived experiences by integrating generative AI into their English Language Teaching methodologies?
- What challenges and opportunities do university-level English teacher educators encounter when implementing generative AI tools in their teaching practices?
1.6 Significance of the Study
The work has also academic and practical research concerning the dynamic environment of English Language Teaching. As the use of generative AI in the educational process increases, it has become worth possessing an idea of the pedagogical, ethical, and institutional implications of the application. Even though the theoretical literature has been discussing in detail the transformative power of AI in terms of personalized learning, increased access, and language practice (Ozdere, 2023; Rusmiyanto et al., 2023), little is known about how the teachers practically navigate the implementation of AI in real classrooms.
Further insights into the lived experiences of the English teacher educators on the university level can be gained with the help of this study that employs a hermeneutic phenomenological approach. It provides valuable insights into the enabling conditions and the barriers associated with the application of generative AI to ELT thus filling a critical gap in the empirical literature. The findings can inform policy makers, teacher education programs, and instructional planners who wish to employ AI technology in language learning effectively and without infringing ethical principles.
Furthermore, there are certain practical implications of this study on the areas of professional development, curriculum planning and distribution of digital resources. The research contributes to a moderate and reasonable trend to integrate generative AI through the articulation of opportunities and difficulties to make it grounded in institutional facts and pedagogical consequences.
1.7 Organization of the Study
This research is divided into 5 chapters. The initial chapter presents the research introduction by stating the background, statement of the problem, objectives, research questions, significance and delimitations of the research. In the second chapter, the literature review, both theoretical and empirical, is reviewed and the theoretic framework and the gap in the research are outlined. The third chapter addresses the research methodology, such as research design, research methods, research tools, sampling, data collection process, methods of data analysis, and ethical issues involved in the research. The fourth chapter introduces and discusses the data gathered among participants, in which the findings are discussed and interpreted in details. The fifth and the last chapter is the conclusion of the study as it summarizes the research, key findings and offers practical recommendations to educators, policymakers and future researchers in regards to the findings of the study.
1.8 Delimitations of the Study
This paper was intentionally constrained to a limited sample of four educational English language teacher educators at the university level and associated with Far Western University, Nepal, who had previous experience working with generative AI in their lesson delivery. The research design was hermeneutic phenomenological methodology whereby the researcher concentrates on explaining the lived experiences of participants instead of projecting the findings to the wider population. It was also restricted to the application of generative AI tools like ChatGPT and other language models, but not any other AI applications like predictive analytics or learning management systems.
In addition, the research focused on the contexts of higher learning and left to investigate the application of generative AI in school-based ELT. Emphasis was made on qualitative information that was collected by use of semi-structured interviews without including classroom observations, student views and experimentation. Such limitations were required to keep the study deep and manageable within the available time and institutional constraints of a mini research project.
1.9 Limitations of the Study
Although the research is very insightful, it may have its some shortcomings. To begin with, the sample is small and thus the results cannot be generalized. Even though the qualitative data collected were abundant, the experiences of four educators might not be representative of the varying contexts and pedagogical realities of ELT professionals across Nepal or even the rest of the world. Second, the self-reported data by interviewing cause the chances of bias because the participants could have recalled or interpreted their experiences selectively.
Third, the fast-changing nature of generative AI technologies implies that the results might become obsolete due to the development of tools and practices. Lastly, the study does not allow the triangulation of the study of perceptions with learning outcomes because of the absence of student input or observational data. Nevertheless, the study makes valuable and timely contributions to the language education AI discussion, especially in under-studied settings such as Nepal.
Chapter II
Literature Review
Generative AI and overall Artificial Intelligence is quickly changing the face of English Language Teaching through the provision of a new range of opportunities in personalized instruction, increased accessibility and increased engagement with the learner. Since the application of these technologies in educational settings is expanding, a critical review of the theoretical basis, empirical evidence, and policy frameworks underpinning AI use in the ELT field is essential. In this chapter, a literature review of the pertinent literature has been provided, reviewing the main theories like the sociocultural theory, connectivism, and examining the empirical studies of the advantages and issues of AI-based tools and evaluating the policy responses to address the challenges of equitable and efficient AI-based tools usage. The research gap is pointed out in particular in this review because the lack of practical experience of teachers in relation to using AI is observed, and therefore, more information about the impact of generative AI on the teaching process is needed. The current research interest is informed by this background of the lived experience of the English teacher educator who is interested in AI-enhanced pedagogy.
2.1 Theoretical Review
Artificial Intelligence and generative AI in particular are becoming particularly popular as a disruptive technology in English Language Teaching that may redefine the educational technologies, teaching methods, and the learning results (Alshahrani, 2023; Roshanaei et al., 2023). The sociocultural theory offered by Vygotsky (1978) to focus on the process of learning as a social mediation occurring in the Zone of Proximal Development (ZPD) is in the center of the theoretical foundations of this work. In this regard, AI tools can be seen as intermediaries and partners that give individual scaffolding through offering tailored feedback and support and enable learners to grow beyond their existing independent capabilities (Kostka and Toncelli, 2023; Ozdere, 2023).
Both the sociocultural theory and the Connectivism, which was proposed by Siemens (2005)-can be adopted to present a modern understanding of learning as the construction and navigation of networks in which knowledge gets collected in the shape of diverse connections. Generative AI can be applied to this theory as it acts as a learning node and relates learners to diverse language, cultural, and contextual knowledge areas. This engagement broadens the language competence of learners through exposing them to real materials, diverse dialects, and socio-cultural subtleties thereby establishing a more comprehensive process of language acquisition.
Nevertheless, theoretical views disclose underlying contradictions. Although the opportunities of AI in personalized learning are rather encouraging, the success of the application strictly depends on the quality of algorithms and the use of context (Dai and Liu, 2024). Moreover, sociocultural theory emphasises human interaction, and the existing AI technologies continue to be inefficient in the face of the complexity and nuances of natural language, including idiomatic and pragmatic nuances (Hockly, 2023), which is where the discrepancy between theoretical possibilities and technical achievement lies.
2.2 Empirical Review
The empirical literature widely supports the idea of AI as the means to personalize and democratize the process of language learning. Kostka and Toncelli (2023) show that AI-based platforms evolve based on performance of the learners, tailoring teaching materials and speed, and hence increasing the engagement and effectiveness of learners. On the same note, Ozdere (2023) also mentions a better level of motivation and autonomy when learners engage in communication with the AI systems, which adapt to their personal requirements, which is also aligned with the ZPD principle of Vygotsky.
The themes that become critical in the literature are accessibility and inclusiveness. The ability of AI to overcome geographic and socio-economic distances enables students in rural or underserved areas to get access to good ELT resources (Sharifuddin and Hashim, 2024). Furthermore, the accessibility capabilities of AI can assist learners with disabilities, providing them with individual accommodations, which will promote inclusion (Yang, 2024). Nevertheless, Crompton et al. (2024) warn that the lack of equal access to digital infrastructure and AI technologies may widen the current disparity in education, which is an indication that the system-wide problems should be considered equally to technological development.
In spite of this promise, the literature that explores the lived experiences of the educators who have introduced generative AI in ELT classrooms is significantly deficient. The majority of empirical studies are dedicated to technological abilities and student performance instead of the attitudes, difficulties, and change strategies of teachers (Dai and Liu, 2024; Hockly, 2023). This laxity restricts awareness of the practical implications of AI and constrains the development of informed policy and practice.
On the policy level, other countries, and educational institutions have started to make AI literacy and digital pedagogies a part of teacher professional development systems (UNESCO, 2023). These policies promote implementing educators with the tools and capabilities to use AI to its advantage, focus on ethical utilization, data protection, and inclusivity. Nevertheless, the implementation of policies is usually slower than the technological progress, and most areas lack infrastructure, funding, and training (Sharifuddin and Hashim, 2024).
In Nepal and other developing situations, formal guidelines are slowly acknowledging the use of AI in education, but there are no real-life principles on how to integrate AI in ELT. The policy gap makes the standardization of best practices and mitigation of digital divides challenging and requires more local and context-specific policy responses.
2.3 Theoretical Framework
The concept of social interaction in cognitive development formed the basis of this study as the sociocultural theory of learning proposed by Lev Vygotsky. Vygotsky (1978) believes that learning happens in the interaction with more knowledgeable people and the environment. The interactive and adaptive features of generative AI can offer scaffolding that helps the learner to work in his/her zone of proximal development. The ZPD is used to denote the number of tasks that learners can perform with the help of more knowledgeable persons yet cannot achieve on their own. The use of AI tools with personalized feedback and adaptive learning environments fits the theory of Vygotsky through the support offered to learners as they advance their skills beyond their current level of capability.
Generative AI can also support the learning process through the creation of context-relevant examples, prompting conversations, and providing immediate feedback, which corresponds to the focus of Vygotsky on interactive learning. An AI tool may be used as an example: it is possible to simulate a conversation with a native speaker and give learners a chance to exercise their language skills in an engaging and dynamic manner. This form of interactive learning process aids the cognitive process and assists the learners to establish language proficiency.
It is also informed by the connectivism, a learning theory proposed by George Siemens (2005) that presents the influence of the digital technology in the learning processes. Connectivism supposes that there is the existence of knowledge in the form of networks of relations, and learning is the navigation and the expansion of such networks. Generative AI contributes to this framework by opening a range of linguistic contributions and cultural contexts and allowing learners to identify with the sources of knowledge.
Connectivism gives attention to the need to establish and traverse networks of information and networks. The AI tools can facilitate this process by providing learners with an access to a large number of linguistic inputs, and cultural contexts, in order to enable the learners to relate to a variety of sources of knowledge. As an example, AI can also expose learners to other accents, dialects, and cultural orientations that may add variety to their language learning experience and assist them in building a more holistic view of the language.
2.4 Research Gap
Despite the literature offering the theoretical potential of AI and certain empirical advantages to ELT, a notable gap where the qualitative, phenomenological research is concerned remains in the literature, covering the experiences of English teacher educators utilizing generative AI in their teaching routine. Most of the research studies brush up on learner-centered outcomes or technical analysis in the absence of factors, perception and pedagogy transformation that teachers experience.
Moreover, the structural barriers, such as infrastructural inequities, digital literacy barriers and policy barriers are not appropriately addressed and this limits the overall understanding of AI usage in different learning institutions especially in third world economies such as Nepal. Such an oversight limits the development of subtle, situational responsive approaches to successful AI adoption in ELT.
The proposed study will address these gaps by exploring experience of English teacher educators on a university level in integrating generative AI and present critical information about advantages, challenges and practical considerations required to inform future research, policy and practice.
Chapter III
Research Methodology
This chapter outlines the research methodology employed to explore the lived experiences of university-level English teacher educators who integrate generative AI into their teaching practices. The study is grounded in a hermeneutic phenomenological approach that prioritizes meaning-making and the interpretation of lived experiences.
3.1 Research Design
The research design is a hermeneutic phenomenological research design that is based on the philosophical tradition of Heidegger and operationalized in terms of the methodological contributions of van Manen (1990). Hermeneutic phenomenology is not a description of the experiences but more about the interpretive process whereby people make sense of their lived realities. It is preoccupied with the finding of the meanings of ordinary practices and interactions particularly within the subjective experience. Such a research design is particularly appropriated to comprehend how English language teacher educators of university level perceive, experience and understand their role as participants of generative artificial intelligence in the teaching of English.
Through the hermeneutic lens, we can get a deep insight into what the teachers perceive their teaching experience to be when reflecting on the emerging AI-based tools, such as ChatGPT or other large language models. Rather than attempting to discover generalized results, the design adds to the deep and context-specific information on the moving interaction between technological adoption and pedagogical modification. In the line of the emphasis on lived experience as offered by van Manen (1990), the research is supposed to illuminate the multidimensions of the inner world of teachers, who find it difficult to comprehend the possibilities and limitations of the application of generative AI in their practice. The research explores the various senses of the AI integration as an instructional and as a pedagogical disruptor using the prism of rich narratives and meanings of the participants.
In addition, the research design is consistent with the constructivist and interpretivism paradigms in that the reality is socially constructed during the experience, interaction, and context (Scotland, 2012). Such paradigms can support the focus on subjectivity and personal sense-making that are the core of learning about how teachers conceptualize and respond to the potentials and limitations of AI technologies. The hermeneutic phenomenological design will allow the research to generate in-depth understanding of the transformative implications of a generative AI in teaching, learning and professional identity in ELT through the prism of interpretation and reflexivity.
3.2 Research Method
This research work falls within the interpretivist research paradigm in which it places more emphasis on the interpretation of human experiences and meanings that people accord such experiences within their social contexts (Creswell, 2012). Interpretivism challenges the positivist assumptions by acknowledging the fact that the reality is constituted socially and that there are different realities whose existence depends on the perceptions and interaction of people. In this connection, the specified paradigm is extremely applicable to the purpose of the suggested study that tries to explore the experience of English teacher teachers, whose teaching involves the application of generative AI, which is subjective and subtle. It is interested in the meaning-making of participants involved in the integration of AI in their respective individual professional, cultural and institutional settings.
A qualitative research method is used because it provides a rich and contextualized explanation, which is flexible and deep in data collection and data analysis (Creswell, 2014). The qualitative investigation assists in the investigation of more complex phenomena that cannot be measured easily such as individual perceptions, emotions and thoughts. This methodology choice enables the researcher to represent lived experiences of teachers, which express the degrees of meaning that transcend the account of the veneer. The focus group discussions, interviews, and reflective narratives allow the participants to explain their experiences, their challenges, and coping strategies when using generative AI tools, hence, providing an in-depth account of this new phenomenon in pedagogy.
More specifically, the research utilizes the philosophical tradition of Heidegger and Gadamer to employ hermeneutic phenomenology as the research methodology (Laverty, 2003). Hermeneutic phenomenology is focused on the lived experience of people in order to discover the way in which meaning and meaning creation are comprehended in a particular situation (van Manen, 2014). This methodology is most appropriate in the study because it focuses on the interpretive aspect of experience and not description because it recognizes that knowledge is a co-construction between the researcher and the participant. Through repeated processes of reflection and interpretation, the research will lead to the revelation of the nature of the experiences of the teachers with generative AI, both subjective and intersubjective aspects of teaching and learning in digitally mediated settings (Scotland, 2012).
3.3 Research Tool
The primary instrument of data collection in this study is semi-structured interviews as it is quite flexible and structured simultaneously. Semi-structured interviews provide a guideline of questions to be asked with the assistance of pre-determined questions, yet participants can explain their ideas and experiences in their own words (Kallio et al., 2016). The approach particularly comes in handy in the qualitative research that requires the in-depth exploration of the complex and subjective nature of the phenomena, such as the experience of educators working with generative AI. Semi-structured interviews enable the researcher to make alterations to the questions based on the interview and clarify vague responses and explore emergent themes throughout the interview (DiCicco-Bloom and Crabtree, 2006).
The crucial difference of a semi-structured interviews is that they might generate rich, descriptive and reflective, accounts on the participants and hence, they might give the depth of the lived experiences (Heidegger, (2005). This strategy will facilitate the flow of information and personal experiences on the incorporation of AI in the teaching process both factual and personal views of the educators. The interviews contribute to a feeling of trust that fosters candor and richness of observations since the participants are empowered to narrate what they have learned in a conversational manner, which is important in phenomenological inquiry. More importantly, this instrument can be used to understand unexpected or hidden issues which the structured questionnaires might overlook, and therefore, enhance the integrity and completeness of the data collected (Peoples, 2020).
The interviews proved to be effective, and reliable, because of the development of an interview guide, based on the research questions, and theoretical frameworks of the study, which provided it with consistency and, at the same time, flexibility. The questions were designed in a manner that they made the participants reflect on their experience with generative AI, its benefits, difficulties and pedagogical incongruities. Follow-up questions could also add to the understanding and demystify the meanings, which is also associated with the hermeneutic phenomenological emphasis on the meaning-making and interpretation (Laverty, 2003; Van Manen, 1997). This cautious and critical application of the use of semi-structured interviews ensures that the study draws sensitive insights that are important to the meaning of AI in the contemporary English language teaching.
3.4 Research Site, Participants, and Sampling Design
The present research was conducted in Sudurpaschim, Nepal, where the target population was the English teacher educators in the university setting, who actively take part in integrating the use of generative AI tools in their teaching practices. The sample size was purposive since it was chosen by taking four participants because they would provide useful and pertinent information within the objectives of the research (Palinkas et al., 2015). These interviewees are pseudo-named Ram, Laxman, Sita and Gita and they were chosen because of their experience and their level of understanding and their exposure to the generative AI applications in English Language Teaching. One of the most potent in the qualitative research, purposive sampling ensures the selection of information-rich cases to offer depth and situational specificity, which is the primary concern of the phenomenological inquiry (Etikan, Musa, and Alkassim, 2016). These particular teachers will be the focus of the study in a bid to obtain the fine and authentic experiences that reflect the complexity of AI integration in ELT within Nepal tertiary education context.
3.5 Method of Data Collection
The primary way of data collection was the individual semi-structured interviews (60-90 minutes). This method allowed the participants to engage in a deep discussion and allowed them the space to express their thoughts, concerns, and experiences with regard to the use of gen AI in their teaching processes (Kallio et al., 2016). All the interviews were recorded and the informed consent of the participants was obtained to tape the interviews to ensure that the information obtained was accurate and complete in nature and was transcribed in its original form to aid in carrying out a rigorous analysis (Rubin and Rubin, 2012). The application of open-ended questions assisted to encourage the participants to share their experiences freely, enabled to create an environment of dialogue and promote profound and thoughtful interactions according to the interpretivist and phenomenological orientation of the study. The plan was needed in the way that the voices of the participants might become the target of the inquiry and, therefore, the thorough analysis of how the generative AI tools might influence pedagogical decisions and professional identities could be carried out.
3.6 Data Analysis Procedure
The theme analysis was applied to the collected data and was also based on the six-step systematic model as provided by Braun and Clarke (2006). This was started by familiarization whereby the researcher read and read some transcripts so as to have an overall view of the data. The second stage entailed the creation of preliminary codes to derive meaningful features in the data as far as the research questions were concerned. The codes were then collated into potential themes, in order to reflect the patterns of shared meaning within the story of the participants. The review and refining of themes in such a way that they were reflective of the corpus of data, and not repetitive, was the next step. The advanced themes were further detailed and designated to give nature and appropriateness to the research objectives. Finally, a sensible and informative report was created, and the themes were correlated to the theoretical frameworks and research goals. This was a rigorous approach of thematic analysis that ensured the validity of the results and high degree of orientational focus on the subject of participants.
3.7 Quality Standards
To foster the reliability and rigor of this qualitative study, standards were established which were systematically involved in the manners of carrying out the research (Lincoln and Guba, 1985). The credibility of the results was also maximized through member checking because the members were able to review and verify that the transcribed information was accurate and that the findings reflected their opinions (Birt et al., 2016). In addition to this, peer debriefing activities were conducted among academic colleagues in order to combat potential biases and enhance the degree of analytical rigor. To address transferability, the rich and thick description of research setting, participants and data collection process were provided in such a way that the readers could make a decision on how much they would be able to generalize the results to other settings (Shenton, 2004). The reliability was ensured by producing a comprehensive audit trail that records every methodological choice, data handling procedures and analytical process that can be made transparent and replicated. Lastly, confirmability was maintained through reflexivity in the sense that the researcher constantly examined his own biases and assumptions and by basing all interpretations on the accounts of participants and verbatim quotes. Together, these measures guaranteed that the results of the study are strong, compelling, and reliable.
3.8 Ethical Considerations
Ethical integrity was of utmost concern in this study and there was strict observance of accepted ethical standards of qualitative inquiry (Orb, Eisenhauer, and Wynaden, 2001). Informed consent was taken before the collection of data where all the participants were informed, clearly, about the study objectives, procedures, risks, and benefits before they could make their informed consent including their right to participate and withdraw without any penalty. Data was stored on secure limited access using pseudonyms to maintain the anonymity of the participants and the digital and physical data was stored safely. The study ensured that the involvement was purely voluntary and that the involved participants were not coerced into speaking out and being pressurized and influenced. These ethics protections were able to uphold the ethics of respect, autonomy, and beneficence and established a trusting and respectful research environment.
Chapter IV
Findings and Discussion
The findings of the study indicate that generative AI is a game-changer in the English Language Teaching field, as the study shows that it offers the pedagogy with customized lessons, varied language input, and democratized education. Integration however, comes with challenges such as accuracy, ethical issues, teacher training, and costs. These are summarised as they relate in this section to ensure that the method of AI as presented here is fair and the value that is inherent in the traditional modes of work is acknowledged and has consequences on sustaining quality assurance and professional growth among the educators.
4.1 Findings
This section highlights the most important results of the interviews with the English language teacher educators at the university level about their experience with incorporating the generative AI in the English Language Teaching. The review shows that a number of prominent themes such as individualized learning, improved language practice, accessibility and inclusiveness, student engagement and motivation, and issues around AI implementation emerge. These results indicate the potentials and the challenges that AI poses to the pedagogical setting, and they capture the intricate attitude of educators towards it and its application.
4.1.1 Personalized Learning
The results of the interviews reveal that the use of generative AI elevates the level of customized learning in the English Language Teaching. The interviewees discussed the fact that the learning process can be customized to the needs of individual learners, which is accomplished with the help of AI technologies, bringing us to a more personalized learning process. Ram claimed that the AI changes the lessons basing on the performance of students and their needs, stating, “The AI analyzes the information on students, including quiz grades and engagement rates and adjusts the lessons according to their requirements”. To illustrate this, when a student has a weakness in grammar, then the AI will provide him/her with more practice and explanation in that area. This enables the AI to provide personalized feedback that covers the individual learning deficiencies and scales the learning to the mastery level of the student.
Ram also noted that the instruction can be personalized in response to the adaptive learning directions of AI. He replied, “I have an artificial intelligence tool that creates learning paths that are unique to the learner. The AI is capable of creating resources of all types, including videos, audio recordings, interactive tasks, and the AI adjusts the content to the student’s learning style”. It enables students to engage the material in whatever way best suits their own learning style, making the learning experience more productive. Another reason why AI can help in correcting and treating learning problems, Laxman also mentioned, is that the AI system recognizes patterns in student performance, including pronunciation errors or certain grammar problems. It further suggests specific exercises to solve these issues and make students solve the difficulties more successfully.
Sita remarked that the personal feedback systems of AI are quite useful. She said, “the AI gives instant feedback on tasks and activities, and this will allow students to know their errors immediately and correct them. This real-time feedback plays a vital role in reinforcing learning and enhancing language skills. The live feedback allows one to keep learning and puts into practice student development”. Sita also highlighted the importance of AI in goal setting by stating that, “AI can assist students to set and monitor their own learning goals depending on their performance. As an illustration, in case a student has a problem with learning vocabulary, then the AI could provide certain objectives and materials to enhance the vocabulary level”. This feature allows the students to actively participate in the learning process.
Gita has named AI when she was talking about differentiated instruction. She observed that, students within a diverse classroom are not at the same level of proficiency and may have different learning needs. AI can also distinguish the instruction by providing varying levels of difficulty in tasks and providing more resources to students in need of additional assistance. This guarantees the support of all students and proper challenge, which makes the learning process more equal. Gita also remarked on AI and its ability to provide opportunities of personalized practice, stating, “the AI creates practice exercises and quizzes that are specific to the progress of a particular student. As an example, when a student has high reading comprehension but low writing, the AI can give him/her extra writing practice to make this better”. This is a focused practice that enables the students to acquire skills in the areas that they need the most.
4.1.2 Enhanced Language Practice
The application of generative AI in language practice became one of the main themes of the descriptions by the participants which demonstrated the innovative and interactive possibilities of the AI tools to improve the skills of students in the language. Ram explained that AI tools allow speaking practice because they simulate conversations, and he mentioned, “Students can have AI-driven conversations that replicate real-life interactions. The AI fixes their spelling and grammar and offers the ideas on how to phrase the words in a more natural way”. This exercise will enable students to gain confidence and speak better in a less stressful setting. This skill to have simulated conversations enables students to perfect their speaking skills and become confident in an encouraging environment.
Another benefit of AI mentioned by Ram is in this writing practice, where the AI provides immediate feedback on the written assignments, identifying mistakes and possible improvements. It also has writing prompts and exercises that enable students to work on their writing abilities. This is a mechanism of immediate feedback that allows the student to make corrections in time and improve his/her ability to articulate ideas in an effective manner. This kind of specific feedback helps to perfect the writing skills of students and general communication skills.
Laxman noted that language practice opportunities are varied with AI tools, and it is mentioned, “AI can recreate various contexts and scenarios to practice language, starting with a casual conversation and finishing with a formal discussion”. Such exposure to the variety of language applications assists students to be more flexible and capable of a variety of communicative contexts. The diversity of practice situations that AI supports improves the skill of students to apply language to various contexts. Also, Laxman noted that AI will be used in acquiring vocabulary, stating, “Interactive exercises and contextual examples can be used to introduce new vocabulary with the help of AI”. New vocabulary can be taught to the students in meaningful contexts and this is likely to improve their retention and application of the vocabulary. This is a contextual learning strategy which helps to develop a more effective and richer vocabulary.
Sita also highlighted the interactive features of AI-driven language practice, indicating that AI-driven games and simulations make language practice more interesting among students. As an example, it is possible to engage students in role-playing activities during which they have to use language creatively and spontaneously. These interactive activities do not only make language practice interesting; they also motivate the students to apply language in creative and practical aspects. Sita also spoke about the capabilities of AI to provide individualized practice sessions, which she explained to be, “The AI can provide practice sessions to individual students that are personalized based on what the student needs the most practice. This individualized methodology will make sure that the students get specific practice to focus on particular areas of weakness”. Gita further emphasized the way AI helps in collaborative language practice, saying that students can engage with AI on group work or discussions. The AI is able to support group activity by giving prompt and directing the discussion. The practice with collaboration promotes interaction and communication, which are key elements of the successful language development. Also, Gita mentioned that AI can be applied to give the students the real language experience, and she said, “The AI tools can also expose students to the use of the authentic language by means of simulation and interaction with a virtual character”. This exposure can assist the students to become conversant with real-life language application and culture that would enable them to apply the language in practical life situations.
4.1.3 Accessibility and Inclusivity
Generative AI will have significant benefits in developing learning materials and improving teaching effectiveness, which the participants of the study describe. Ram emphasized the effectiveness of AI in the creation of educational content and mentioned, “AI can be used to create lesson plans, quizzes, and interactive activities, which are fast and easy to create. This is very time-saving when compared to the production of these materials manually. The AI can help educators spend more time on direct teaching and interacting with students by accelerating the process of development of these resources, thereby enhancing the overall experience in the teaching process”.
Ram has also highlighted the role of AI in grading and provision of feedback. The grading of assignments can be automated with the assistance of AI and be rich in feedback. This will decrease the amount of work required of the administration and enable me to devote more time to direct teaching and interaction with students. Automation of grading and feedback not only reduces the administrative burden of the teachers, it also makes sure that students obtain timely and constructive assistance thus making teaching process more efficient.
Laxman highlighted the breadth of AI as a tool of producing a variety of educational resources noting that, AI can produce a wide-ranging array of educational tools, such as interactive exercises, multimedia presentations, etc. Such diversity contributes to the engagement of lessons and supports various learning styles. The fact that AI has the capacity to generate different kinds of materials improves the quality of instruction and makes students interested. Also, Laxman stated the ability of AI to adjust resources to various degrees of difficulty, stating, “AI tools can create resources of varying difficulty, so that every student can be presented with a suitable challenge. An example is that the AI can produce beginner, intermediate, and advanced versions of a reading comprehension exercise. This flexibility will make sure that instructional resources are provided to students of different levels of competence”.
Sita addressed the use of AI to support the development of creative resources and said, AI could provide innovative and creative suggestions to lesson activities, including a multimedia project or an interactive game. This assists me in creating more interesting and productive lessons. These imaginative materials can help bring a higher level of student interest and pleasure. Another aspect that Sita emphasized is the efficiency of administrative work, which is promoted by AI, she pointed out, “AI can be used to perform the routine administrative work, including student progress and scheduling. This helps me to have more time to concentrate in instructional aspects and student support. AI will enable educators to focus more on teaching and helping their students by automating administrative processes”.
Gita explained the usefulness of AI tools in the process of creating resources together, saying, “AI can be useful in collaborative development of resources, as it provides an opportunity to various teachers to contribute and share materials. This cooperation improves the quality and diversity of education resources. Shared resource building gives rise to a more varied and enriched collection of materials to the students”. Moreover, Gita listed the possibility of continuous enhancement of learning materials by AI, saying, “AI can be used to analyze the feedback provided and performance data by students to optimize and improve educational materials”. This makes sure that the resources are applicable and efficient. The unceasing improvement of resources makes them more effective and helps them to remain in touch with the changing needs of students.
4.1.4 Student Engagement and Motivation
Generative AI enables student interaction and engagement, as well as motivation, significantly as it can provide students with interactive and personal learning experiences. Respondents told about some of the ways in which AI tools make the learning process more engaging and motivating. Ram noted that in AI tools gamification characteristics are highly effective in motivating students. He observed that AI tools, which take the form of games (through rewards and challenges) are more appealing to students during the course of learning. When they perceive learning as a game, they get more motivated to be involved and finish tasks. The learning process can be made more pleasant and enjoyable by incorporating the game-like elements of AI that boosts motivation.
Another point that Ram raised was AI use in providing individualized encouragement. He clarified, “AI will be able to provide personalized feedback and compliment to students depending on their performance. Such positive feedback is used to promote their trust and ensure them that they are motivated to keep learning. Individualized positive feedback and praise will lead to a higher motivation and self-efficacy in students making them stay determined towards their learning goals”.
Laxman underlined the use of AI tools to support a greater engagement of the students in the forms of interactive activities. He commented, Interactive AI tools, including virtual simulations and role-playing games, motivate students to be active learners in a lesson. They are more active when they are able to share the material and play around with one another. These interactive components encourage active learning and increase the overall student engagement”. Another role of AI that Laxman addressed is its use in goal-setting and goal-achievement, where he says, “AI devices can serve students in setting and monitoring learning objectives. AI shows students’ motivation and keeps them focused on their goals by giving them progress updates and celebrating their accomplishments. This strategy helps students to be motivated and feel that they are achieving”.
Sita shared about the way AI-based tools can make learning more enjoyable. She mentioned, “AI tools which are versed with multimedia products, including videos and animations, make the lesson more interactive and enjoyable among students. They will find it easier to remain active and engaged as the material is delivered in a dynamic and interactive way. Multimedia elements are used to increase the interest and motivation of the students”. Also, Sita mentioned the advantage of instant feedback and rewards given by AI. AI tools may provide immediate feedback on the work of the students and reward their work. This instant appreciation strengthens good behaviour and keeps the students going with the same effort. Feedbacks and rewards are very crucial in enhancing the learning process and keeping one motivated.
According to Gita, AI applications help with personalized learning paths, which implies that, as Gita points out, “AI can create individualized learning paths by student, depending on what they are interested in and how they progress. This individuality will make learning more intimate and fun to the students. Individualization of the learning pathways may be employed to make things interesting and pertinent”. The other point brought up by Gita is that AI could be used to make learning a positive experience because in her article, she mentioned, “AI tools can be used to provide a positive and encouraging learning environment through the delivery of positive feedback and highlighting of student achievements”. Students can feel special and motivated by such positive environment. General motivation and engagement of the student is a component in the establishment of such environment.
4.1.5 Challenges in AI Integration
Although AI has many benefits, its integration in English Language Teaching faces a number of challenges. The participants pointed out the different challenges associated with the implementation of AI, such as accuracy, ethics, teacher training, and cost. Ram also mentioned that he was worried about the validity of AI-generated information and pointed out, “AI sometimes is inaccurate or unintelligent, and this may deceive students. Ensuring that AI-generated content is accurate and that the corrective guidance is given where necessary is critical. To retain the quality of education and avoid confusion of students, it is necessary to ensure that outputs of AI can be trusted”.
Another ethical concern raised by Ram is that, there are privacy concerns of how AI tools collect and use student data. We should make sure that the information of the students is secured and applied in a good way. Critical to a responsible use of AI in education is the need to address any concern that could arise on issues like privacy and security of data. Likewise, Sita noted that there should be definite ethical principles, claiming, “We need definite ethical principles to use AI in education in order to solve such problems of AI usage as data privacy and the bias of algorithms. The creation of these guidelines would make the use of AI responsible and transparent”.
Laxman talked about how teachers should be trained to successfully use AI tools in their teaching. They might not be able to use AI in their lessons without appropriate training and take full advantage of its potential. To prepare educators to successfully integrate AI, professional development is also essential. Resistance to technology adoption was also mentioned, saying that some teachers might be reluctant to embrace AI because they are not familiar with the technology or because they worry about how it will affect their instructions. It is impossible to overcome this resistance without considering their concerns and offering them sufficient support.
Gita touched on the financial implication of implementing AI, noting, “Implementation of AI technologies can be expensive and all the institutions cannot afford it. The implementation of AI tools and its possible advancement can be constrained by financial limits”. Another aspect that she drew to the issue is sustainable investment; she mentioned, “The AI tools demand continuous investment, both in maintenance and updates. These are the long-term costs that institutions should consider in order to make the AI tools effective in the future. Financial planning needs to be made sustainable so that more people can access AI technologies in education and use them effectively”.
Generative AI has significant advantages in terms of English Language Teaching, which is offering customized learning opportunities, a stronger practice of language, and making education more accessible and inclusive. Nevertheless, challenges related to the implementation of AI include accuracy, ethical, teacher training, and cost also. The necessary response to each of these issues is a balanced response that will offer the benefits of AI alongside the conventional teaching techniques, continual quality enforcement, and all-embracing professional growth among educators.
4.2 Discussions
The discussion section will look at the ways to apply the generative AI in ELT, its benefits in the sphere of personalized learning and resource creation, and some of the concerns, including accuracy and ethics. The respondents observed that there is a necessity to balance the approach and capitalize on AI potential overcoming the integration challenges.
Implementing AI in English Language Teaching is a multi-faceted environment, which consists of both immense opportunities and challenges. According to Ram, AI is a resource, not a substitute of educators. It can make our teaching better, but it cannot substitute the human touch, which means that AI should be used to supplement human teachers and not replace them. The literature supports this view, implying that although AI can significantly contribute to the learning and teaching process, the most successful educational results are obtained with the help of the interrelation of human knowledge and technological advances (Daulay and Ginting, 2024).
4.2.1 Personalized Learning: A Double-Edged Sword
The ability of generative AI to provide personalized learning is a revolutionary change in English Language Teaching. Ram explained, AI tools can tailor instruction to individual student needs by examining real-time student data, including quiz performance, enrollment rates, and learning trends, thus increasing engagement and promoting better learning results. The pedagogical promise is reflected in Ram when he writes that with the help of the AI, I am able to customize lessons to the needs of individual students. It works much like the sociocultural theory proposed by Vygotsky (1978), particularly the Zone of Proximal Development (ZPD) in which scaffolding is used to cushion the learners so that they can work more than they are currently in a position to do. In this aspect, AI is a scaffold that is dynamic, meaning that the teaching content and the pace can be modified based on the requirements of the learner, so they can refine certain areas of their knowledge such as grammar or vocabulary. Such responsiveness can transform the previous models of classrooms into made less exclusive classrooms which are learner-centred.
However, personalized learning using AI also has some implementation problems. The single huge problem is related to the quality and validity of the AI produced content. It sometimes produces wrong or senseless output, which is warning of the cognitive and pedagogical harm of excessive reliance on the automated processes as Ram cautions. Generative AI tools lack semantic understanding to give contextually appropriate feedback (especially in linguistically complex scenarios) in comparison to the discernment of human teachers, who can infer context (Idham, Rauf and Rajab, 2024). Mistakes in AI responses can not only mislead learners, but also cement assumptions unless they are immediately rectified. Hence, the effective implementation of AI in ELT requires proper quality control systems and constant monitoring by teachers to guarantee the educational value of AI-generated texts.
The ethical nature of AI-based personalization, in particular, the data privacy and algorithm bias, is no less important. Massive data also supports AI systems and this may be biased socially, culturally or even linguistically disadvantaging certain groups of students. Such biases may be reproduced without challenge, via individualized feedback and learning trajectories, and indirectly entrench educational injustices. Therefore, teachers and designers should be cautious of AI tools in terms of transparency, fairness, and cultural sensitivity. The integration of AI on ethical grounds must be based on the transparent standards and regulatory frameworks that can value the well-being of students, the correctness of the content, and the teacher as a mediator and interpreter of technology-based teaching. Here personalized learning with generative AI is not only an opportunity but a duty as to insist on a serious pedagogical design, ethical attention, and reflective pedagogical practice.
4.2.2 Enhanced Language Practice: Opportunities and Limitations
Generative AI has brought new opportunities to improve language practice in English Language Teaching, especially by its capacity to have a real-life conversation, offer instant feedback, and multimodal materials. The characteristics are in line with the connectivism theory of learning developed by Siemens (2005) that assumes that learning takes place in the form of networks of information, relationships and also digital tools. AI-based applications can build upon interactive and immersive experiences where students play roles, or have conversations or pronunciation exercises without fear of judgment. Sita can explain how such tools can make people feel psychologically comfortable when she says that the AI is a safe environment to train speaking without fear of being judged. This non-threatening environment assists the students to play with language, risk, and achieve communicative competence, some of the components of an effective language acquisition as well as observed by Fathi et al. (2024) and Abdurazakova, (2024).
Despite all these benefits, the disadvantages of the AI-mediated language practice are to be paid appropriate attention. The artificial intelligence feedback, in particular, grammar proofreading and pronunciation evaluation, may not match the contextual and cultural sensitivity of human tutors (Zhang, & Tur, 2024). Language acquisition is not simply a matter of, at the very least, correctness, but one where sensitivity to idiomatic usage, tone, register, pragmatics, et cetera must be considered, which current AI models still struggle to effectively resolve. Off-context or wrong feedback may lead to fossilized mistakes or misunderstandings especially in learners who mostly rely on AI to correct and guide them.
Additionally, AI can provide an extensive number of resources, but the instructional quality of the provided materials greatly depends on the construction and quality of the algorithms provided behind it, which may vary across platforms. Besides, the power of AI to support various learning styles, as observed by Laxman about its capacity to support visual and written styles also demonstrates a greater pedagogical obligation. Although this flexibility is an obvious strength, it requires constant monitoring and updating of AI systems to be relevant and effective. Language is dynamic and consequently, should be the tools of teaching it. Thus, the introduction of AI in language practice cannot be considered an independent solution but rather an addition to the human teaching. The teachers should actively mediate the use of AI and teach the students to focus on the feedback and place it in perspective. It is only in this way, with the collaboration of technology and teacher that the full potential of AI in language practice may be achieved without affecting the depth and quality of instruction.
4.2.3 Accessibility and Inclusivity: Expanding Horizons
Generative AI has the transformative power to foster access and inclusivity regarding English Language Teaching by filling the gaps in geographic, economic, or physical restrictions. According to Alshaikhi et al. (2024), AI has the potential to transform education into a more democratic system through offering students with quality learning materials in remote and underserved regions. The limitless scope and expandability of A.I.-enhanced education is the one that Ram captures with commenting that AI technologies can access remote learners who could not otherwise access quality education. These functions are one of the main breakthroughs in the process of addressing educational differences particularly in low resource environments where teachers and physical learning materials are likely to be scarce.
Together with geographic inclusivity, AI serves to support learners with different needs in education, particularly learners with disabilities. Features such as speech-to-text, text-to-speech, captioning, and real-time translation can help to make learning more accessible. It turns out that text-to-speech and speech-to-text features of AI become extremely helpful to learning-disabled students (Sita), which proves the notion of AI as capable of supporting various learning styles and disabilities (Zou et al., 2020). These tools reflect ideas of Universal Design of Learning that promotes flexible learning instructions where all learners can be accommodated. Interventions developed using AIs will provide a more equal learning environment since the traditional students whose access to learning has been limited in the past will not be left behind.
However, the digital divide and the lack of technological preparedness disparities must be addressed to make AI potential in terms of access and inclusiveness a reality. When Gita says that she needs to ensure that everyone among the students is able to have the technologies they require and armed with the digital ability to use it, it shows how challenging it would be to come up with an equal system to apply AI. Unequal access to devices, to the internet and to digital literacy, and in disadvantaged areas in particular, remains a reality. Without particular infrastructural advancement and widespread preparation of the teachers as well as the students, AI-based solutions will only continue the current disparities but not decrease them. In this way, AI integration must be a process that is inclusive and made to be accompanied by the policy work that places an emphasis on both the digital equity and the institutional support systems.
4.2.4 Resource Creation and Efficiency: Enhancing Teacher Capabilities
Radical opportunities generated by generative AI allow to streamline the teaching process in English Language Teaching and this is particularly applicable in the field of resources creation and management. The time-consuming tools will be automated with AI that can help plan lessons, create quizzes and other interactive learning activities, providing a teacher with an opportunity to focus more on pedagogy and student conversations. This automation as Koraishi (2023) stated does not only assist in reducing workload, but also results in the process of improved instructional planning. This alteration is reflected in the experience which Laxman gets: The AI has saved me a lot of time, designing lesson plans and quizzes. I can now spend more time dealing with the students and less time dealing with the administration. This transformation of the routine work into the meaningful involvement enables the educators to put more efforts in the cognitive and affective learning process of their students.
Among other things, generative AI can make the instructional content more efficient and creative. Its ability to produce new prompts, multimedia activity and interactive formats can transform the practices happening in the classroom. One observation which Gita makes is that the AI can generate some creative writing prompts, and interactive exercises that she would never have thought of. It maintains the lessons fresh and interesting to the students, it shows how AI can become an agent of pedagogical change. This imaginative enhancement is especially relevant to continuing student motivation and meeting the student learning styles. The AI will enhance the learning process in the classroom, as it diversifies the content that students will be exposed to and promotes differentiated instructions.
Nevertheless, even though AI is more productive and creative, it should be introduced under conscious pedagogical control. Since, as Ram claimed, AI is a tool, not a substitute of a teacher, the human factor is the key to a successful learning process. AI does not have a sense of context, emotional intelligence, and adaptive judgment, which would be required to provide individualized feedback, manage classroom dynamics, and develop students as a holistic individual (Novawan, Walker and Ikeda, 2024). Thus, AI is not meant to replace teachers but can be viewed as a supplement, which complements teacher functionality without compromising professional expertise or the relationship pedagogy. Proper integration will be in place when the educators are able to exercise agency on the AI tools, in such a way that the products reflect the educational goals and ethical standards.
4.2.5 Student Engagement and Motivation: Leveraging AI for Interactive Learning
Generative AI has also transformed the aspect of student engagement in the English Language Teaching since it makes the process more interactive, engaging, and personal. Considering the combination with the gamified classroom activities, the possibility to engage in interactive storytelling, and more flexible creative writing activities stimulates more engagement and fun. It is evident that AIs can transform learning, which is passive, into an active exploration, as observed by Rizvi (2023). The realization of Ram that the students will be more motivated to participate in the lessons that include the features of AI like games and interactive storytelling. The entertaining, practical nature of AI can facilitate the emotional-cognitive engagement of students to the learning process, as they see learning as fun, and they are more inclined to stick with it.
Besides the entertainment aspect, generative AI can also support compliance with motivation by providing a personalized feedback and frequent encouragement according to the progress of a specific learner. According to the observation of Laxman, the AI gives students positive reinforcement, which makes them confident in themselves and stimulates them to continue the learning process, which is one of the most important elements of intrinsic drive. This corresponds with the sociocultural theory that was designed by Vygotsky (1978) which gives prominence to the supportive feedback and interaction during cognitive development. The fact that AI can replicate this kind of responsive scaffolding can contribute to the more personalized and empowering learning process, particularly in the situations when the students, who otherwise were not necessarily eager to engage or express themselves in the conventional classroom setting.
However, despite all these advantages, the problem of interpersonal and working skills development should not be ignored when implementing AI in staying engaged. Gita is right when she says that AI could make the learning process more interactive, but on the same hand, we need to ensure that students get to learn social and cooperative skills through their interaction with peers and educators. This is a reminder that AI should be incorporated as a complement rather than a substitute or human interaction. Although AI encourages personalized learning, the human approach is needed to develop empathy, communication, and group problem-solving skills. An equitable pedagogical approach to combine AI-driven interactivity with social interaction in real-time is thus an essential factor to achieve both engagement and overall learner growth (Creely, 2024).
4.2.6 Ethical and Privacy Considerations: Navigating the Complex Landscape
Ethical and privacy aspects of incorporating generative AI in English Language Teaching are becoming more urgent as digital technologies gain more and more influence on the learning setting. The issue of data security, informed consent, and accountability to algorithms has become the major problematic concerns in the implementation of responsible AI usage. The worry of Gita, We should make sure that the student data is secure and in responsible use. Severe privacy concerns that must be investigated define the urgency of the protection of the personal information of the students. Akgun and Greenhow (2022) are like-minded and demand open data governance and institutional policies. Without powerful regulation tools, the broad application of AI may expose the learners to the risk of having their data abused, being spied on, and denied a chance to use their learning data themselves.
To rectify such problems, learning institutions ought to develop detailed moral principles to regulate AI usage. These principles to be followed should include principles of openness in algorithmic decision-making, informed consent to gather and use information, and remedies in the event of malicious or discriminatory outcomes of AI systems. Additionally, teachers must be invested with moral literacy and training which they should have so that they can manage the complexities of the AI implementation in a responsible way (Luo, Huang and Ke, 2023). Teacher professional development also needs to be morally ready since teachers occupy one of the central positions in mediating AI usage in the classroom and protecting student rights.
The other interesting concern is the chance of the algorithmic bias which can encourage the existence of the social and cultural inequities unwillingly. Training AI systems with biased data may result in biased, inaccurate, or culturally insensitive results. The statement by Sita, We can not just trust AI and believe that it will work out, but we must always verify its results and ensure that they are correct and appropriate to our students, reveals that human control and quality control is required at all times. Teachers have to analyze AI-generated materials carefully in order to make sure they correspond to pedagogical objectives and promote the values of equity and inclusion. Finally, the incorporation of AI that is ethical must go beyond technical protection, but it must also entail a critical pedagogical approach that will put justice, transparency, and human dignity at the center of education.
4.2.7 Teacher Training and Professional Development: Building Capacity for AI Integration
The level of generative AI integration in English Language Teaching will largely depend on the readiness of teachers, which explains the importance of teacher training and professional development. Since AI tools continue to infiltrate the educational landscape, it is not only the role of teachers to learn how these tools operate, but also to use them pedagogically. Al-Zyoud (2020) states that a successful integration of AI necessitates that educators should be aware of the fundamentals of AI, be able to implement these tools into instructional design, and resolve the arising technical challenges. This lack could be a hindrance to the successful adoption of AI in the classroom, and one of the pillars of the educational change is professional development.
Respondents of this study continued repeating that they require on-going rather than intermittent professional development. Laxman is of the opinion that professional development is not a one-time activity. We must always be in endless training and encouragement so that we do not get left behind as the new changes in AI technology are coming in and this means that there has to be the institutional backing in the form of continued teacher capacity building. This perspective corresponds to the existing literature that requires an uninterrupted dialogue, reflection and adaptive learning of the teacher in rapidly changing technology conditions (Hartono et al., 2023). The use of AI devices will become more popular and sophisticated, leaving teachers without a consistent support and training.
There are also technical issues along with the pedagogical adoption of AI. The causes of cognitive and emotional impediments to unfamiliarity are why not all of my colleagues are eager to use AI as Sita believes that some of her colleagues are not aware of how it functions or how they can incorporate it in their lessons. The professional development programs that are good must therefore also aim to fill the gap between the pedagogical and technological know-how. This will require establishing training that will not only demonstrate AI features, but it will also put them within the context of a language learning system. Both operation and instructions: Teacher development programs can ensure that teachers feel confident and capable enough to implement AI in a manner that will allow student learning process to be interesting and inspirational.
4.2.8 Addressing Financial Barriers: Sustainable Implementation Strategies
There are high-cost implications on the implementation of the generative AI in English Language Teaching, particularly to institutions with small budgets. One of the most prevalent concerns in the current literature is mentioned by Gita when she notes that the implementation of AI technologies may be quite expensive and not all institutions are able to afford it. Shaw et al. (2019) indicate that the initial high costs of investment (such as the acquisition of AI tools, infrastructure, and technical support) are typically the determining factors limiting the wide adoption of AI in the education sector. These financial constraints are particularly acute in the resource-constrained environment, where competing demands may lead to a shift in technological innovation on the list of priorities in favor of more pressing needs.
Some of the strategic methods of surmounting such financial constraints are but not limited to acquisition of grants and collaborations with technology providers. The implication of the suggestion by Sita that is, Grants and partnerships with tech companies can be applied in order to cut a portion of the financial burden, appears to be practical solutions that institutions can apply in subsidizing AI-related expenditure. Collaborations with education technology firms, governments, and development organizations will be able to fund and impart expertise, and AI will prove significantly cheaper and implementable. Such partnerships also can be used to professional growth and improve infrastructure so that investments are not considered as a one-time purchase but can support the larger educational ecosystem.
However, as Ram pointed out rightly, it is not merely about the initial cost but also constant updates and maintenance which also implies that you have to plan long term. AI technologies need to be regularly updated, technical support, and, probably, faculty retraining will be required to make them acceptable. Its implementation needs to be sustainable, which, in its turn, needs to be meticulously planned financially with a consideration to recurring costs and resource distribution (Kasneci et al., 2023). The policymakers and institutional leaders ought to be visionary to integrate budget projections, cost-benefit assessment and scaling evaluation in order to ensure that adoption of AI in ELT is not only possible but also sustainable over time.
4.2.9 Future Directions: Continuous Research and Development
As the findings of the paper have shown, the need to continue research and development is urgent now to simplify the use of generative AI in the English Language Teaching. With the high speed of technological innovations, we can be absolutely sure that an educational institution will always explore how best to combine AI tools with pedagogical goals. According to Gill et al. (2022), the dynamism nature of AI implies that its utility and suitability in the context of different teaching and learning processes should be reviewed and refreshed in the context of iterative approaches. This ongoing research is critical towards streamlining the AI systems, addressing the arising concerns, and ensuring the technology will be open to the changing demands of educators and learners.
It can be considered by studying the long-term impacts of the integration of AI on student performance, agency of teachers, and equity in education overall, which is one of the ways to develop the research critically. Its early adoption can enhance the engagement, personalization, and accessibility, yet there is very little empirical evidence on the long-term outcomes. To illuminate the effect of generative AI on autonomy, motivation, and academic performance of learners over a long period, longitudinal research would be beneficial. It is also important to study how AI affects the role of teacher, teaching practices and teacher identity. The understanding of these dimensions will assist educators, and policymakers to make sound decisions in relation to the integration of AI in such a way that it does not substitute the current practice of education but rather supplements it.
Besides, as AI emerges as an indispensable component of learning institutions, the issue of ethics and privacy must be retained in the focus of the further evolution. Potential breaches of student data, the discrimination of algorithms, and lack of openness in AI judgments have created a necessity to proactively design policies and enforce technological security. Future studies should thus examine ways on how to incorporate ethical systems into AI systems and to arm teachers with the knowledge to address such issues. With the ethical, evidence-based, and context-sensitive practices in the forefront, the ELT community is able to deduce that the application of generative AI is relevant to fair and viable educational progress.
Chapter V
Summary, Conclusion and Recommendations
This is the last chapter where the research results are synthesized, conclusions are made of the analysis, and some practical recommendations are given to teachers, policymakers, and researchers. The chapter is a reflection on the contributions of the study, the implications, and the future directions of practice and inquiry based on the discovery of the lived experiences of university-level English teacher educators working with generative AI in English Language Teaching.
5.1 Summary
This paper explored lived experiences of four university-level English Language Teacher educators in Sudurpaschim, Nepal who have incorporated generative Artificial Intelligence tools in their English Language Teaching practice. The study utilized a hermeneutic phenomenological approach to investigate the manner in which these instructors make sense of and negotiate opportunities and challenges presented by generative AI in their pedagogical settings. Theoretically based on the sociocultural theory of Vygotsky (1978) and connectivism of Siemens (2005), the study highlighted the social and the technological aspects that define the modern language learning. The data gathered were analyzed using semi-structured interviews and were thematically analyzed in a six-step procedure introduced by Braun and Clarke (2006), resulting in the following themes related to personalized learning, better language practice, accessibility and inclusiveness, student engagement and motivation, and ethical and privacy concerns.
The findings indicated that generative AI has a high potential to assist ELT by offering personalized learning based on the individual needs of learners, enhancing student motivation and motivation, offering greater access to quality education resources and fostering the existence of a diverse array of learner profiles. The respondents explained how AI can dynamically change learning resources depending on the performance in real-time, instant feedback, and recreating real-world language experiences as especially helpful, especially in resource-limited conditions. These affordances helped to increase learner autonomy, greater involvement, and more frequent meaningful language practice.
Although these are some of the positive aspects, the study has revealed that there are a number of major challenges. Respondents noted the issue of inaccuracy, reliability, and cultural inappropriateness of AI-generated content, as there is a risk of misinformation, bias, and content that is not context-sensitive. The fact that AI cannot decipher the nuances of language highlighted the need to continue to monitor and scrutinize teachers. Ethical and privacy concerns proved to be the primary issues, and educators stressed that the responsibility of high data protection, disclosure of AI algorithms, and responsible utilization should be taken to ensure that the human interaction in the classroom setting will decrease.
5.2 Conclusion
The paper ends by finding that generative AI can have a transformative effect in the area of English language teaching but it has to be applied in careful, reflexive, and context-specific ways. Pedagogical model integrating AI solutions with the necessary human element of teaching, i. e. guidance, contextualization, and ethical judgment is needed to uphold pedagogical integrity and foster the well-being of learners. In addition, the unequal access to digital infrastructure and literacy that participants identified reflects that unless specific action is taken to address these systemic inequities, AI may become an unintentionate source of the existing educational inequities. Thus, the focus of any strategy to introduce AI to ELT should be digital inclusion and equitable access.
The readiness of the teacher was one of the most important factors of the success of AI integration. The findings prove the necessity of careful professional development courses that will enhance the technological literacy of teachers, their critical understanding of AI products, and their ability to be flexible in their pedagogy. The ongoing institutional support and reflective practice should be used to empower teachers to negotiate the complexities of AI-assisted language instruction. Finally, the lived experiences of the educators present a good insight into the delicate interaction between technology and pedagogy and contribute to our comprehension of the ways AI transforms the teaching and learning process in the context of actual education.
5.3 Recommendations
On the basis of the study, some recommendations are made. To begin with, teacher education institutions and universities must be more focused on the creation and execution of systematic, context-sensitive, professional development programs that develop AI literacy and pedagogical expertise in English language educators. The programs should ensure that teachers are ready not only to use AI tools but also to analyze the findings and draw conclusions, as well as to ethically integrate these tools into the curriculum.
Second, the policymakers must come up with sound regulatory frameworks, which can address the ethical use of AI in education, namely, data privacy, algorithmic transparency, and bias reduction. Such policies will help in safeguarding the rights of the students and create the credibility in the AI-enhanced learning environments.
Third, the necessity to increase digital infrastructure and equal access to AI technologies should be also improved, especially in underserved and remote localities. The digital divide needs to be bridged so that the benefits of AI-enhanced ELT could be enjoyed by all learners without any limitations because of the socio-economic status or geographical area.
Finally, it is necessary to ensure that consistent research is conducted to monitor and evaluate the long-term outcomes of the implementation of the generative AI in the perspectives of various stakeholders, including teachers and students. This interrogation will lead to gradual transformation in practice, policy and technology to ensure that the emerging role of AI in ELT will be sensitive to educational values and social justice.
References
Abdurazakova, S. (2024, May). Developing ELT in the age of AI: Communicative methods and applications. In Conference Proceedings: Fostering Your Research Spirit (pp. 69-72). https://jainkwellpublishing.com/index.php/conferences/article/download/524/522
Al-Zyoud, H. M. M. (2020). The role of artificial intelligence in teacher professional development. Universal Journal of Educational Research, 8(11B), 6263-6272. https://www.academia.edu/download/65138397/UJER65_19517508.pdf
Alshahrani, A. (2023). The impact of ChatGPT on blended learning: Current trends and future research directions. International Journal of Data and Network Science, 7(4), 2029-2040. http://m.growingscience.com/beta/ijds/6376-the-impact-of-chatgpt-on-blended-learning-current-trends-and-future-research-directions.html
Alshaikhi, T., & Khasawneh, M. A. S. (2024). Investigating the benefits and drawbacks of selected AI-powered instruments employed for foreign language education. Kurdish Studies, 12(2), 2156-2169. http://kurdishstudies.net/menu-script/index.php/KS/article/view/2216
Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431-440. https://link.springer.com/article/10.1007/s43681-021-00096-7
Birt, L., Scott, S., Cavers, D., Campbell, C., & Walter, F. (2016). Member checking: a tool to enhance trustworthiness or merely a nod to validation?. Qualitative Health Research, 26(13), 1802-1811. https://doi.org/10.1177/1049732316654870
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Creswell, J. W. (2012). Educational research: Planning, conducting and evaluating qualitative and quantitative research. Pearson.
Creswell, J. W. (2014). Research design: qualitative, quantitative, and mixed methods approaches. Sage.
Creely, E. (2024). Exploring the Role of Generative AI in Enhancing Language Learning: Opportunities and Challenges. International Journal of Changes in Education. https://www.sciencedirect.com/science/article/pii/S074756322400222X
Crompton, H., Edmett, A., Ichaporia, N., & Burke, D. (2024). AI and English language teaching: Affordances and challenges. British Journal of Educational Technology. https://bera-journals.onlinelibrary.wiley.com/doi/pdfdirect/10.1111/bjet.13460
Dai, K., & Liu, Q. (2024). Leveraging Artificial Intelligence (AI) in English as a Foreign Language (EFL) Classes: Challenges and Opportunities in the Spotlight. Computers in Human Behavior, 108354. https://www.sciencedirect.com/science/article/pii/S074756322400222X
Daulay, S. F., & Ginting, P. (2024). Transforming English Language Teaching Through AI: Evaluation of the Strategies and Potential of Artificial Intelligence Applications in EFL. IDEAS: Journal on English Language Teaching and Learning, Linguistics and Literature, 12(1), 376-395. http://ejournal.iainpalopo.ac.id/index.php/ideas/article/download/4907/2796
DiCicco‐Bloom, B., & Crabtree, B. F. (2006). The qualitative research interview. Medical education, 40(4), 314-321. https://doi.org/10.1111/j.1365-2929.2006.02418.x
Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American journal of theoretical and applied statistics, 5(1), 1-4. https://doi.org/10.11648/j.ajtas.20160501.11
Fathi, J., Rahimi, M., & Derakhshan, A. (2024). Improving EFL learners’ speaking skills and willingness to communicate via artificial intelligence-mediated interactions. System, 121, 103254. https://www.sciencedirect.com/science/article/pii/S0346251X24000368
Gill, S. S., Xu, M., Ottaviani, C., Patros, P., Bahsoon, R., Shaghaghi, A., … & Uhlig, S. (2022). AI for next-generation computing: Emerging trends and future directions. Internet of Things, 19, 100514. https://www.sciencedirect.com/science/article/pii/S254266052200018X
Hartono, W. J., Nurfitri, N., Ridwan, R., Kase, E. B., Lake, F., & Zebua, R. S. Y. (2023). Artificial Intelligence (AI) solutions in English language teaching: Teachers-Students perceptions and experiences. Journal on Education, 6(1), 1452-1461. http://jonedu.org/index.php/joe/article/download/3101/2642
Heidegger, M. (2005). Introduction to Phenomenological Research. Indiana University Press.
Hockly, N. (2023). Artificial intelligence in English language teaching: The good, the bad and the ugly. Relc Journal, 54(2), 445-451. https://journals.sagepub.com/doi/abs/10.1177/00336882231168504
Huang, X., Zou, D., Cheng, G., Chen, X., & Xie, H. (2023). Trends, research issues and applications of artificial intelligence in language education. Educational Technology & Society, 26(1), 112-131. https://www.jstor.org/stable/48707971
Idham, A. Z., Rauf, W., & Rajab, A. (2024). Navigating the Transformative Impact of Artificial Intelligence on English Language Teaching: Exploring Challenges and Opportunities. Jurnal Edukasi Saintifik, 4(1), 8-14. https://jurnal.stkipmb.ac.id/index.php/jes/article/download/620/189
Kallio, H., Pietilä, A. M., Johnson, M., & Kangasniemi, M. (2016). Systematic methodological review: developing a framework for a qualitative semi‐structured interview guide. Journal of advanced nursing, 72(12), 2954-2965. https://doi.org/10.1111/jan.13031
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., … & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and individual differences, 103, 102274. https://osf.io/preprints/edarxiv/5er8f/download
Koraishi, O. (2023). Teaching English in the age of AI: Embracing ChatGPT to optimize EFL materials and assessment. Language Education and Technology, 3(1). https://langedutech.com/letjournal/index.php/let/article/view/48
Kostka, I., & Toncelli, R. (2023). Exploring applications of ChatGPT to English language teaching: Opportunities, challenges, and recommendations. Tesl-Ej, 27(3), n3. https://files.eric.ed.gov/fulltext/EJ1409872.pdf
Lincoln, YS. & Guba, EG. (1985). Naturalistic Inquiry. Sage Publications. http://www.qualres.org/HomeEval-3664.html
Mukhamedov, A. (2024). Navigating opportunities and challenges of AI in enhancing critical thinking in EFL education. Mental Enlightenment Scientific-Methodological Journal, 5(05), 189-197. http://mentaljournal-jspu.uz/index.php/mesmj/article/download/566/596
Laverty, S. M. (2003). Hermeneutic phenomenology and phenomenology: A comparison of historical and methodological considerations. International journal of qualitative methods, 2(3), 21-35. https://doi.org/10.1177/160940690300200303
Luo, R., Huang, L., & Ke, N. (2023, November). A Review of Opportunities and Challenges of Artificial Intelligence Chatbots in Second Language Acquisition. In International Symposium on Emerging Technologies for Education (pp. 70-77). Singapore: Springer Nature Singapore. https://link.springer.com/chapter/10.1007/978-981-97-4246-2_6
Novawan, A., Walker, S. A., & Ikeda, O. (2024). The new face of technology-enhanced language learning (TELL) with artificial intelligence (AI): Teacher perspectives, practices, and challenges. Journal of English in Academic and Professional Communication, 10(1), 1-18. https://publikasi.polije.ac.id/jeapco/article/download/4565/2458
Orb, A., Eisenhauer, L., & Wynaden, D. (2001). Ethics in qualitative research. Journal of nursing scholarship, 33(1), 93-96. https://doi.org/10.1111/j.1547-5069.2001.00093.x
Özdere, M. (2023). The Integration of Artificial Intelligence in English Education: Opportunities and Challenges. Language Education and Technology, 3(2). http://www.langedutech.com/letjournal/index.php/let/article/download/55/47
Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533-544. https://doi.org/10.1007/s10488-013-0528-y
Peoples, K. (2020). How to write a phenomenological dissertation: A step-by-step guide. SAGE Publications.
Rizvi, S. (2023). Revolutionizing student engagement: Artificial intelligence’s impact on specialized learning motivation. International Journal of Advanced Engineering Research and Science, 10(09). https://i.ihspublishing.com/index.php/ijaers/article/view/209
Roshanaei, M., Olivares, H., & Lopez, R. R. (2023). Harnessing AI to foster equity in education: Opportunities, challenges, and emerging strategies. Journal of Intelligent Learning Systems and Applications, 15(04), 123-143. http://research.manuscritpub.com/id/eprint/3491/
Rusmiyanto, R., Huriati, N., Fitriani, N., Tyas, N. K., Rofi’i, A., & Sari, M. N. (2023). The role of artificial intelligence (AI) in developing English language learner’s communication skills. Journal on Education, 6(1), 750-757. https://www.jonedu.org/index.php/joe/article/download/2990/2549
Scotland, J. (2012). Exploring the philosophical underpinnings of research: Relating ontology and epistemology to the methodology and methods of the scientific, interpretive, and critical research paradigms. English language teaching, 5(9), 9-16. https://doi.org/10.5539/elt.v5n9p9
Sharifuddin, N. S., & Hashim, H. (2024). Benefits and Challenges in Implementing Artificial Intelligence in Education (AIED) in ESL Classroom: A Systematic Review (2019-2022). International Journal of Academic Research in Business and Social Sciences, 14(1), 146-164. http://dx.doi.org/10.46886/IJARBSS/v14-i1/9394
Shaw, J., Rudzicz, F., Jamieson, T., & Goldfarb, A. (2019). Artificial intelligence and the implementation challenges. Journal of medical Internet research, 21(7), e13659. https://www.jmir.org/2019/7/e13659/
Shenton, A. K. (2004). Strategies for ensuring trustworthiness in qualitative research projects. Education for Information, 22(2), 63-75. https://doi.org/10.3233/EFI-2004-22201
Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3-10. https://www.semanticscholar.org/paper/Connectivism%3A-A-Learning-Theory-for-the-Digital-Age-Siemens/7c7dd6c900c031b3685c761c72ebafdf3004caed
UNESCO (2023). Generative AI and the future of education. https://doi.org/10.54675/HOXG8740
Van Manen, M. (1990). Researching lived experience: Human science for an action-sensitive pedagogy. State University of New York Press. https://philpapers.org/rec/VANRLE-2
van Manen, M. (2014). Phenomenology of practice: Meaning-giving methods in phenomenological research and writing. Routledge. http://www.maxvanmanen.com/files/2014/10/MvM-Phen-of-Practice-113.pdf
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press. https://www.jstor.org/stable/j.ctvjf9vz4
Yang, A. (2024). Challenges and Opportunities for Foreign Language Teachers in the Era of Artificial Intelligence. International Journal of Education and Humanities, 4(1), 39-50. https://i-jeh.com/index.php/ijeh/article/download/202/122
Zhang, P., & Tur, G. (2024). A systematic review of ChatGPT use in K‐12 education. European Journal of Education, 59(2), e12599. https://onlinelibrary.wiley.com/doi/pdf/10.1111/ejed.12599
Zou, B., Liviero, S., Hao, M., & Wei, C. (2020). Artificial intelligence technology for EAP speaking skills: Student perceptions of opportunities and challenges. Technology and the psychology of second language learners and users, 433-463. https://www.academia.edu/download/85019453/978-3-030-34212-8_17.pdf
Appendix A
Semi-Structured Interview Schedule
Title of the Research:
Opportunities and Challenges of Integrating Generative AI in English Language Teaching
Purpose of the Interview:
This interview aims to explore the lived experiences of university-level English teacher educators regarding the integration of generative AI tools into English Language Teaching. The interview seeks to examine both the opportunities and challenges encountered in the process of implementing generative AI in pedagogical practices.
Estimated Duration:
60–90 minutes
Questions
- Can you briefly introduce yourself, your academic background, and your current teaching responsibilities?
- How long have you been teaching English at the university level?
- What is your familiarity with generative AI tools (e.g., ChatGPT, Bard, Claude, etc.) in the context of ELT?
- How do you define or understand the role of generative AI in English Language Teaching?
- When did you start using generative AI in your teaching, and what motivated you to do so?
- Which AI tools or platforms do you frequently use, and for what purposes (e.g., lesson planning, feedback, assessments)?
- In what ways has generative AI contributed to enhancing your teaching effectiveness?
- Have you noticed improvements in students’ engagement, language practice, or performance as a result of integrating generative AI?
- Can you share specific examples of how AI has supported personalized learning or inclusivity in your classroom?
- How do you think generative AI supports or aligns with pedagogical theories such as sociocultural theory or connectivism?
- What challenges have you encountered while integrating generative AI into your teaching practices?
- Are there concerns related to the accuracy, bias, or appropriateness of AI-generated content?
- How do you address ethical concerns such as academic dishonesty, plagiarism, or over-reliance on AI by students?
- What limitations have you observed in terms of accessibility, digital literacy, or infrastructure in your institution?
- Have you received any formal training or institutional support on using AI tools in education?
- What kind of support or professional development do you think is necessary for teachers to effectively integrate AI into ELT?
- How do you see the future of generative AI in English language education?
- What recommendations would you make to institutions, policymakers, or developers to ensure the effective and ethical use of AI in ELT?
- What advice would you offer to fellow English teachers who are considering integrating AI tools into their teaching?
- Is there anything else you would like to add about your experience with generative AI in English Language Teaching?
Note to Interviewer:
Ensure ethical research practices by obtaining informed consent, maintaining confidentiality, and providing participants the freedom to skip any questions they are uncomfortable with.
Appendix B
Informed Consent Form
Title of the Study:
Opportunities and Challenges of Integrating Generative AI in English Language Teaching
Researcher:
Mr. Dammar Singh Saud
Assistant Professor of English Language Education
Darchula Multiple Campus, Far Western University, Nepal
Grant Support:
Research Innovation and Development Center
Far Western University, Mahendranagar, Kanchanpur
Purpose of the Study:
This study seeks to explore the lived experiences of university-level English language teacher educators who have integrated generative artificial intelligence (AI) tools into their teaching. The study aims to understand the pedagogical opportunities, challenges, and implications of using generative AI in English Language Teaching.
Procedures:
If you agree to participate in this study:
- You will be interviewed by the researcher through a semi-structured interview format.
- The interview will take approximately 60 to 90 minutes.
- With your permission, the interview will be audio recorded for transcription and analysis purposes only.
- You may skip any question you do not wish to answer or stop the interview at any time without any consequence.
Voluntary Participation:
Your participation is entirely voluntary. You may withdraw from the study at any time without any negative consequences or need for justification.
Confidentiality and Data Protection:
- Your identity and personal information will remain strictly confidential.
- A pseudonym will be used in place of your real name in all records and publications.
- Audio recordings and transcripts will be securely stored and accessible only to the researcher.
- Data will be used solely for academic and research purposes.
Potential Risks and Benefits:
There are no foreseeable risks involved in participating in this study. Your participation may contribute to the improvement of English language teaching practices through a better understanding of how generative AI tools are integrated in real educational settings.
Consent Statement:
I have read the information provided above and understand the purpose, procedures, confidentiality terms, and voluntary nature of this research study. I hereby give my consent to participate in the interview.
Participant’s Name: _________________________________________
Signature: ___________________________________________________
Date: _________________________
Researcher’s Name: Mr. Dammar Singh Saud
Signature: ___________________________________________________
Date: _________________________
