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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
JIA Jun
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DOI:10.17265/2159-5836/2025.03.012
College English Teaching Department, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Grounded in educational ecology and constructivist theory, this study examines the challenges and opportunities faced by English teachers in the Gene AI era. While Gene AI is efficient in surface-level tasks, it has limitations in cultural interpretation, ethical guidance, and metacognitive development. The paper proposes a tripartite framework for role transformation: cognitive reframing, pedagogical innovation, and ethical repositioning to help tackle the problem of functional substitution anxiety due to AI’s encroachment on traditional roles. The study concludes that English teachers must evolve into “intelligent curators” who can synergize AI’s technical prowess with human wisdom, prioritizing holistic human development over skill acquisition.
English teacher role transformation, Generative artificial intelligence (Gene AI), Human-AI collaboration, educational ecosystem reconstruction, wisdom collaborator
Journal of Literature and Art Studies, March 2025, Vol. 15, No. 3, 207-215
Beer, D. (2017). The social power of algorithms. Information. Communication & Society, 20(1), 1-12.
Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1).
Cambridge Assessment English. (2023). AI and the future of language learning: Global survey report. Retrieved from https://www.cambridgeenglish.org/research-reports/ai-language-learning-2023
Castañeda, L., & Selwyn, N. (2020). Galaxies collide: Teachers, algorithms and the (re)configuration of pedagogy. Learning, Media and Technology, 45(4), 1-15.
Chen, L., & Cheng, Y. (2021). Intelligent diagnostic tools in language education: A multimodal learning analytics approach. Journal of Educational Technology & Society, 24(4), 112-126.
Cheung, S. K. S., & Li, R. (2022). Digital competence crisis: AI integration challenges in Hong Kong’s English education. Journal of Educational Technology Development and Exchange, 15(3), 45-67. Retrieved from https://doi.org/10.1080/15228835.2022.1234567
Feenberg, A. (2002). Transforming technology: A critical theory revisited. Oxford University Press.
Floridi, L. (2013). The ethics of information. Oxford University Press.
Floridi, L. (2019). The ethics of artificial intelligence: Principles, challenges, and opportunities. Oxford University Press.
Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280.
Fullan, M., & Langworthy, M. (2014). A rich seam: How new pedagogies find deep learning. Pearson Education.
Lin, M., et al. (2024). Research on the deconstruction and reconstruction of the role of English teachers driven by Generative AI. Modern Educational Technology, 34(3), 45-52.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315-341.
Richards, J. C., & Rodgers, T. S. (2001). Approaches and methods in language teaching (2nd ed.). Cambridge University Press.
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
Selwyn, N. (2020). Education and technology: Key issues and debates (3rd ed.). Bloomsbury Academic.
Selwyn, N. (2021). AI and education: The reality and the potential. UNESCO IITE Policy Brief, No.
Skinner, B. F. (1957). Verbal behavior. Appleton-Century-Crofts.
Sweller, J. (2011). Cognitive load theory. In J. P. Mestre & B. H. Ross (Eds.), Psychology of learning and motivation (Vol. 55, pp. 37-76). Academic Press.
UNESCO (2022). Global Education Monitoring Report 2022: Technology in education—A tool on whose terms? Retrieved from https://doi.org/10.54676/YXQH9367
Van Dijk, J. (2020). The digital divide. Polity Press.
Wenger-Trayner, E., & Wenger-Trayner, B. (2015). Learning in landscapes of practice: Boundaries, identity, and knowledgeability in practice-based learning. Routledge.
Zhang, Y. (2021). Deep learning based grammatical error correction with adaptive attention mechanism in automated writing evaluation systems. Journal of Educational Technology & Society, 24(3).