Navigating the impact of generative AI in English language education

Yanling  Cai

doi:10.59400/fes2143

Navigating the impact of generative AI in English language education

Yanling Cai University International College, Macau University of Science and Technology, Macau 999078, China

Article ID: 2143

Keywords: generative artificial intelligence (GenAI); ChatGPT; academic integrity; digital literacy; ethical AI guidelines

Abstract

The incorporation of generative AI, especially tools like ChatGPT, into English language teaching signifies a profound transformation in educational strategies. This paper examines the dual impact of GenAI on teaching and learning, emphasizing both its potential benefits and inherent challenges. GenAI provides tailored learning opportunities, flexibility, and increased independence for students, thereby enriching the educational experience. However, it also poses significant challenges, including issues of academic integrity, the risk of reducing human interaction in learning, and the need for strong digital skills. The study highlights the irreplaceable value of interpersonal engagement and empathetic understanding in human-teacher interactions, which remain central to effective education. Key factors such as ease of use, frequent usage intentions, acceptance, and information verification intentions are identified as crucial for the responsible adoption of GenAI tools like ChatGPT and DeepSeek. The paper also underscores teachers’ cautious perceptions of AI, advocating for a focus on critical thinking and problem-solving skills rather than mere cognitive replacement. This paper emphasizes the importance of ethical guidelines, digital literacy, and the preservation of human-centric teaching practices; it presents a roadmap for the responsible integration of AI in education, ensuring that technological advancements enhance rather than undermine the educational experience.

Published

2025-07-21

How to Cite

Cai, Y. (2025). Navigating the impact of generative AI in English language education. Forum for Education Studies, 3(3), 2143. https://doi.org/10.59400/fes2143

Download Citation

Issue

Vol. 3 No. 3 (2025)

Section

Brief Report

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

[1]Du J, Alm A. The Impact of ChatGPT on English for Academic Purposes (EAP) Students’ Language Learning Experience: A Self-Determination Theory Perspective. Education Sciences. 2024; 14: 726. doi: 10.3390/educsci14070726

[2]Acosta-Enriquez BG, Ballesteros MAA, Jordan OH, et al. Analysis of college students’ attitudes toward the use of ChatGPT in their academic activities: Effect of intent to use, verification of information and responsible use. BMC Psychology. 2024; 12: 255. doi: 10.1186/s40359-024-01764-z

[3]Noroozi O, Soleimani S, Farrokhnia M, Banihashem SK. Generative AI in education: Pedagogical, theoretical, and methodological perspectives. International Journal of Technology in Education (IJTE). 2024; 7(3): 373–385.

[4]Mabuan RA. ChatGPT and ELT: Exploring teachers’ voices. International Journal of Technology in Education (IJTE). 2024; 7(1): 128–153.

[5]Farrokhnia M, Banihashem SK, Noroozi O, Wals A. A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International. 2023; 1(15).

[6]Banihashem SK, Kerman NT, Noroozi O, et al. Feedback sources in essay writing: Peer-generated or AI-generated feedback? International Journal of Educational Technology in Higher Education. 2024; 21(1): 23.

[7]Lee AVY. Staying ahead with generative artificial intelligence for learning: navigating challenges and opportunities with 5Ts and 3Rs. Asia Pacific Journal of Education, 2024; 44(1): 81–93. doi: 10.1080/02188791.2024.2305171

[8]Kaplan-Rakowski R, Grotewold K, Hartwick P, Papin K. Generative AI and teachers’ perspectives on its implementation in education. Journal of Interactive Learning Research. 2023; 34: 313–338.

[9]Guo K, Wang D. To resist it or to embrace it? Examining ChatGPT’s potential to support teacher feedback in EFL writing. Educational Information Technology. 2023; 29: 8435–8463.

[10]Cukurova M. The interplay of learning, analytics and artificial intelligence in education: A vision for hybrid intelligence. British Journal of Educational Technology. 2024; 1–20. doi: 10.1111/bjet.13514

[11]Wang K, Chai CS, Liang JC, Sang G. Exploring teachers’ behavioural intentions to design artificial intelligence-assisted learning in Chinese K–12 education. Technology, Pedagogy and Education. 2024; 33(5): 629–645. doi: 10.1080/1475939X.2024.2369241

[12]Baek C, Tate T, Warschauer M. “ChatGPT seems too good to be true”: College students’ use and perceptions of generative AI. Computers and Education: Artificial Intelligence. 2024; 7: 100294. doi: 10.1016/j.caeai.2024.100294

[13]Ma S, Lei L. The factors influencing teacher education students’ willingness to adopt artificial intelligence technology for information-based teaching. Asia Pacific Journal of Education. 2024; 44(1): 94–111.

[14]Albayati H. Investigating undergraduate students’ perceptions and awareness of using ChatGPT as a regular assistance tool: A user acceptance perspective study. Computers and Education: Artificial Intelligence. 2024; 6: 100203. doi: 10.1016/j.caeai.2024.100203

[15]Har F. Teaching English as a Second Language in the Midst of a Paradigm Shift: An Exploration of Students’ and Teachers’ Perception of ChatGPT. Teaching English as a Second Language in the Midst of a Paradigm Shift: An Exploration of Students’ and Teachers’ Perception of ChatGPT. Springer; 2023.

[16]Adnan M, Tondeur J, Scherer R, Siddiq F. Profiling teacher educators: ready to prepare the next generation for educational technology use? Technology, Pedagogy and Education. 2024; 33(4): 527–544. doi: 10.1080/1475939X.2024.2322481

[17]Wang N, Wang X, Su Y. Critical analysis of the technological affordances, challenges and future directions of Generative AI in education: A systematic review. Asia Pacific Journal of Education. 2024; 44(1): 139–155. doi: 10.1080/02188791.2024.2305156

[18]ElSayary A. An investigation of teachers’ perceptions of using ChatGPT as a supporting tool for teaching and learning in the digital era. Journal of Computer Assisted Learning. 2024; 40(3): 931–945. doi: 10.1111/jcal.12926

Editor-in-Chief

Dr. Surapati Pramanik

Nandalal Ghosh B.T. College, India

eISSN

3029-2956

Publication Frequency

Quarterly

Indexing

Polish Scientific Bibliography

About the Publisher

Academic Publishing insists on taking academic exchange and publication as the main line, carrying out comprehensive management based on science and technology, and fully exploring excellent international publishing resources. Within 5 years, it will form a strategic framework and scale with science (S), technology (T), medicine (M), education (E), and humanities and arts (H) as the main publishing fields. Academic Publishing is headquartered in Singapore and based in Malaysia, with the United States and China providing the main scientific and academic resources. At the same time, it has established long-term good cooperative relations with other publishing companies, scientific research communities, and academic organizations in more than a dozen countries and regions. Academic Publishing uses English and Chinese as its main publishing languages, mainly publishing books, journals, and conference papers in print and online. The vast majority of publications follow the international open access policy, providing stable and long-term quality and professional publications. With the joint efforts of the expert team and our professional editorial team, our publications will gradually be indexed by international databases in stages to provide convenient and professional retrieval for various scholars. At the same time, manuscripts we accept will be subject to the peer review principle, and cutting-edge and innovative research articles will be preferentially accepted for peer reference and discussion. All kinds of our publications are welcome for peer to contribute, access, and download.

more

Volume Arrangement

2025

2024

2023

Featured Articles

Diachronic analysis of Spanish scientific production on teaching methodologies in Primary Education: A scientometric and conceptual perspective (2000–2023)

The evaluation of science is essential to ensuring the quality, validity, and reliability of scientific results. Science needs to undergo a review process to ensure the rigorousness of scientific output. This evaluation provides a solid basis for political and economic decisions related to the design and execution of research projects, the establishment of new lines of research, or the identification of areas of specialization. This paper analyses diachronically the Spanish scientific production related to the implementation and development of teaching methodologies in primary education and indexed in the Scopus and Web of Science databases during the period 2000–2023. This analysis is carried out on the one hand, from a scientometric perspective, based on the analysis of indicators such as diachronic production, the journals with the highest scientific productivity, and the most productive institutions, and, on the other hand, from a conceptual perspective, trying to define its relationship with other areas of education. The general results of this study reveal two clear stages: the first, up to 2010, with little scientific production; and the second, from 2011 onwards, characterised by a general growth. The relationship between this field and others such as initial teacher training, ICT, and didactics is also evident.

The relationship between teachers’ self-efficacy and classroom management practices in secondary schools

This study explores the intricate relationship between teacher self-efficacy and classroom management practices in secondary schools in the Mansehra district of Pakistan. Teacher self-efficacy, defined as the belief in one’s ability to manage and influence classroom environments effectively, has been identified as a critical factor influencing both teaching performance and student outcomes. The research employed a mixed-method approach, gathering data from 62 teachers and 310 students using both online surveys (via Google Forms) and physical questionnaires to ensure a diverse and inclusive participant pool. Data analysis was conducted using two complementary tools: SPSS and Smart PLS. SPSS was used for descriptive statistics and inferential analyses, such as t-tests, chi-square tests, and measures of central tendency, to offer an overview of group differences and relationships between variables. Meanwhile, Smart PLS was employed for Partial Least Squares Structural Equation Modeling (PLS-SEM), a technique suited for complex models and smaller sample sizes. This method allowed for the analysis of both direct and indirect relationships between the study variables—teacher self-efficacy, teaching practices, and classroom management. The findings reveal a significant positive correlation between teacher self-efficacy and classroom management practices. Additionally, teaching practices were found to mediate this relationship, indicating that higher levels of self-efficacy not only directly improve classroom management but also enhance teaching performance, which in turn contributes to better-managed classrooms. These results suggest that interventions aimed at enhancing teacher self-efficacy can have far-reaching effects on educational outcomes. The study highlights the need for focused teacher development programs that foster self-efficacy, thereby improving classroom management, student engagement, and overall academic success.

Artificial Intelligence in education: A comprehensive study

This comprehensive study delves into the multifaceted role of AI in education, exploring its applications, benefits, challenges, and future implications. The purpose of the study is to show how AI in education helps educators identify gaps in student knowledge and provide targeted feedback to improve learning outcomes. As a methodology, the library method and the study and review of various documents have been used in this research. The study examines the diverse range of AI technologies employed in educational settings, including intelligent tutoring systems, personalized learning platforms, educational chatbots, and virtual reality simulations. Furthermore, the study delves into the numerous benefits that AI brings to education. It highlights how AI-powered analytics and data-driven insights enable educators to gain deeper insights into student learning patterns, identify areas for improvement, and tailor instructional strategies accordingly. Additionally, AI-driven tools promote inclusivity by providing personalized support to learners with diverse needs and learning styles. Despite its transformative potential, the study also acknowledges the challenges and ethical considerations associated with integrating AI into education. Data privacy, algorithmic bias, and the digital divide are examined in detail, emphasizing the importance of responsible AI deployment and ethical guidelines. Looking ahead, the study explores the future implications of AI in education and the evolving role of educators in AI-enabled classrooms. It discusses how AI technologies will continue to evolve, offering new opportunities for collaborative learning, skill development, and lifelong education. In conclusion, this comprehensive study underscores the profound impact of AI on education and the need for thoughtful implementation strategies that prioritize equity, inclusivity, and ethical considerations. By harnessing the potential of AI, education systems can better prepare learners for the challenges and opportunities of the future.

Enhancing adult learner success in higher education through decision tree models: A machine learning approach

This article explores the use of machine learning, specifically Classification and Regression Trees (CART), to address the unique challenges faced by adult learners in higher education. These learners confront socio-cultural, economic, and institutional hurdles, such as stereotypes, financial constraints, and systemic inefficiencies. The study utilizes decision tree models to evaluate their effectiveness in predicting graduation outcomes, which helps in formulating tailored educational strategies. The research analyzed a comprehensive dataset spanning the academic years 2013–2014 to 2021–2022, evaluating the predictive accuracy of CART models using precision, recall, and F1 score. Findings indicate that attendance, age, and Pell Grant eligibility are key predictors of academic success, demonstrating the strong capability of the model across various educational metrics. This highlights the potential of machine learning (ML) to improve data-driven decision-making in educational settings. The results affirm the effectiveness of Decision Tree (DT) models in meeting the educational needs of adult learners and underscore the need for institutions to adapt their strategies to provide more inclusive and supportive environments. This study advocates for a shift towards nuanced, data-driven approaches in higher education, emphasizing the development of strategies that address the distinct challenges of adult learners, aiming to enhance inclusivity and support within the sector.