Keywords
Generative Struggle
Educator as Facilitator
Error-Driven Pedagogy
Productive Failure
How to Cite
Abstract
Background: This study develops generative struggle as a pedagogical framework for AI-mediated English language acquisition, addressing the cognitive risks associated with AI-driven task completion and cognitive offloading.
Objective: The study aims to: (1) identify psychological barriers that may influence learners’ engagement with their own cognitive resources during AI-mediated writing tasks, and (2) conceptualise the teacher’s role as an epistemic mentor who promotes generative struggle, divergent thinking, learner agency, and meaningful language development in GenAI-assisted writing environments.
Methods: The study adopts a conceptual and theoretical synthesis approach, integrating perspectives from cognitive psychology, second language acquisition, English language teaching, and educational technology. The inquiry draws on five established traditions: the generation effect, desirable difficulties, writing-to-learn, error-based learning, and cognitive offloading. These perspectives are considered alongside the learner-centred vision of NEP 2020 to examine the relationship between cognitive effort and meaningful language development.
Results: The analysis suggests that excessive reliance on algorithmic efficiency may erode cognitive engagement by bypassing the generative struggle central to language acquisition. The proposed framework therefore prioritises generative struggle and guided self-correction over mere task completion, repositioning the teacher as an epistemic mentor who fosters reflective, metacognitive, and critical engagement beyond the “impressiveness trap.” Rather than rejecting automation, the framework advocates pedagogically mediated AI integration that supports generative learning while limiting cognitive outsourcing. These findings provide a basis for preliminary instructional directions adaptable to learners’ proficiency levels, institutional contexts, and assessment designs.
Conclusion: The paper conceptualizes generative struggle as an integrative construct that synthesizes five established cognitive traditions to explain learning under AI-mediated conditions. The concepts of the AI education loop and the impressiveness trap further illuminate emerging tensions between AI-assisted performance, cognitive engagement, and genuine learning. Together, these concepts provide a conceptual basis for understanding how AI-mediated educational environments can support meaningful learning without displacing the cognitive effort essential to language development.
References
Al-Khresheh, M. H. (2024). Bridging technology and pedagogy from a global lens: Teachers’ perspectives on integrating ChatGPT in English language teaching. Computers and Education: Artificial Intelligence, 6, Article 100218. https://doi.org/10.1016/j.caeai.2024.100218
Anderson, T. (2010). The effects of tiered corrective feedback on Second language academic writing (thesis). The effects of tiered corrective feedback on second language academic writing. University of British Columbia, Vancouver.
Aquino, M. (2026). From Freire’s critical pedagogy to the age of artificial intelligence: Continuities, ruptures, and new educational challenges. Science Insights Education Frontiers, 33(2), 5387–5401. https://eric.ed.gov/?id=EJ1506336
Assis Hornay, P. M. (2021). Cognitive load theory and the English language instruction. ACCENTIA: Journal of English Language and Education, 1(1), 43–55. https://doi.org/10.37598/accentia.v1i1.1015
Bangert-Drowns, R. L., Hurley, M. M., and Wilkinson, B. (2004). The effects of school-based writing-to-learn interventions on academic achievement: A meta-analysis. Review of Educational Research, 74(1), 29–58. https://doi.org/10.3102/00346543074001029
Barr, N., Pennycook, G., Stolz, J. A., and Fugelsang, J. A. (2015). The brain in your pocket: Evidence that smartphones are used to supplant thinking. Computers in Human Behavior, 48, 473–480. https://doi.org/10.1016/j.chb.2015.02.029
Bertsch, S., Pesta, B. J., and McDaniel, M. A. (2007). The generation effect: A meta-analytic review. Memory & Cognition, 35, 201–210. https://doi.org/10.3758/BF03193441
Bjork, E. L., and Bjork, R. A. (2011). Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. In M. A. Gernsbacher, & J. R. Pomerantz (Eds.), Psychology and the real world: Essays illustrating fundamental contributions to society, Worth Publishers, 2nd ed., 55–64. https://bjorklab.psych.ucla.edu/wp-content/uploads/sites/13/2016/04/EBjork_RBjork_2011.pdf
Butterfield, B., and Metcalfe, J. (2001). Errors committed with high confidence are hypercorrected. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27(6), 1491–1494. https://doi.org/10.1037/0278-7393.27.6.1491
Corder, S. P. J. (1967) The Significance of Learner’s Errors. International Review of Applied Linguistics, No. 4, 161-170. https://doi.org/10.1515/iral.1967.5.1-4.161
Dwivedi, Y. K., et al. (2023). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Edelouei, S. (2025). Factors influencing language teachers’ judgements and decision-making about the use of generative AI tools: A systematic review. Teaching and Teacher Education, 171, 1–14. https://doi.org/10.1016/j.tate.2025.105333
Emig, J. (1977). Writing as a mode of learning. College Composition and Communication, 28(2), 122–128. https://openlab.citytech.cuny.edu/fywpd/files/2019/01/emig-writing-as-a-mode-of-learning.pdf
Ganai, P. A., and Naikoo, I. A. (2024). An analysis of grammatical errors in academic essay writing among the students of Jazan University. International Journal of Arts and Humanities, 2(1), 10–25. https://doi.org/10.61424/ijah.v2.i2.68
Government Offices of Sweden. (2024, February 8). Government investing in more reading time and less screen time. https://www.government.se/articles/2024/02/government-investing-in-more-reading-time-and-less-screen-time/
Guo, J., et al. (2024). Harnessing artificial intelligence in generative content for enhancing motivation in learning. Learning and Individual Differences, 116, Article 102547. https://doi.org/10.1016/j.lindif.2024.102547
Gupta, D. S., and Chatterjee, J. (2023). Transforming English language teaching: A study of the National Education Policy 2020. Journal of English Language Teaching, 65(4), 11–17. https://journals.eltai.in/index.php/jelt/article/view/JELT650403/982
Hamouda, A. (2011). A study of students’ and teachers’ preferences and attitudes towards correction of classroom written errors in the Saudi EFL context. English Language Teaching, 4(3), 128–141. https://doi.org/10.5539/elt.v4n3p128
Jose, B., Cherian, J., Verghis, A. M., Varghise, S. M., Mumthas, S., and Joseph, S. (2025). The cognitive paradox of AI in education: Between enhancement and erosion. Frontiers in Psychology, 16, Article 1550621. https://doi.org/10.3389/fpsyg.2025.1550621
Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379–424. https://doi.org/10.1080/07370000802212669
Kapur, M. (2016). Examining productive failure, productive success, unproductive failure, and unproductive success in learning. Educational Psychologist, 51(2), 289–299. https://doi.org/10.1080/00461520.2016.1155457
Kasneci, E., et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Keith, N., and Frese, M. (2008). Effectiveness of error management training: A meta-analysis. Journal of Applied Psychology, 93(1), 59–69. https://doi.org/10.1037/0021-9010.93.1.59
Kiefer, M., and Velay, J.-L. (2016). Writing in the digital age. Trends in Neuroscience and Education, 5(3), 77–81. https://doi.org/10.1016/j.tine.2016.07.008
Klein, P. D., and Boscolo, P. (2016). Trends in research on writing as a learning activity. Journal of Writing Research, 7(3), 311–350. https://doi.org/10.17239/jowr-2016.07.03.01
Krashen, S. D. (1982). Principles and practice in second language acquisition. Pergamon Press. http://www.sdkrashen.com/content/books/principles_and_practice.pdf
Mangen, A., et al. (2015). Handwriting versus keyboard writing: Effect on word recall. Journal of Writing Research, 7(2), 227–247. https://doi.org/10.17239/jowr-2015.07.02.1
Mayer, R. E. (Ed.). (2014). The Cambridge handbook of multimedia learning (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9781139547369
Mayer, R. E., et al. (2014). Multimedia learning in a second language: A cognitive load perspective. Applied Cognitive Psychology, 28(5), 653–660. https://doi.org/10.1002/acp.3050
Mehdi, M. F. (2018). Analysis of errors made by first-year secondary school students in writing English sentences: A case study of Libyan EFL students. International Journal of Linguistics, 10(6), 206–226. https://doi.org/10.5296/ijl.v10i6.14134
Metcalfe, J. (2017). Learning from errors. Annual Review of Psychology, 68, 465–489. https://doi.org/10.1146/annurev-psych-010416-044022
Ministry of Education. (2020). National Education Policy 2020. Government of India. https://www.education.gov.in/sites/upload_files/mhrd/files/NEP_Final_English_0.pdf
Mosier, K. L., and Skitka, L. J. (1996). Human decision makers and automated decision aids: Made for each other? In R. Parasuraman and M. Mouloua (Eds.), Automation and human performance: Theory and applications, Lawrence Erlbaum Associates, 201–220. https://www.researchgate.net/publication/230601064_Human_Decision_Makers_and_Automated_Decision_Aids_Made_for_Each_Other
Raimes, A. (1983). Techniques in Teaching Writing. In the series: Teaching Techniques in English as a Second Language, Oxford University Press: New York, 1-164. ISBN:9780194341318
Risko, E. F., and Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002
Runco, M. A. (2023). AI can only produce artificial creativity. Journal of Creativity, 33(3), Article 100063. https://doi.org/10.1016/j.yjoc.2023.100063
Runco, M. A. (2025). The misleading definition of creativity suggested by AI must be kept out of the classroom. Education Sciences, 15(9), Article 1141. https://doi.org/10.3390/educsci15091141
Sarkar, S. (2026). Mapping Interlanguage through Error Analysis: A PEN Framework for Pedagogical Intervention in Rural ESL Contexts. Euro-Global Journal of Linguistics and Language Education, 251-266. https://doi.org/10.69760/egjlle.2602029
Slamecka, N. J., and Graf, P. (1978). The generation effect: Delineation of a phenomenon. Journal of Experimental Psychology: Human Learning and Memory, 4(6), 592–604. https://doi.org/10.1037/0278-7393.4.6.592
Sriram, R. (2020, April 13). The neuroscience behind productive struggle. Edutopia. https://www.edutopia.org/article/neuroscience-behind-productive-struggle/
Sumakul, D. T., Hamied, F. A., and Sukyadi, D. (2022). Artificial intelligence in EFL classrooms: Friend or foe? LEARN Journal: Language Education and Acquisition Research Network, 15(1), 232–256. https://so04.tci-thaijo.org/index.php/LEARN/article/view/256723
Suzuki, W., Nassaji, H., and Sato, K. (2019). The effects of feedback explicitness and type of target structure on accuracy in revision and new pieces of writing. System, 81, 135–145. https://doi.org/10.1016/j.system.2018.12.017
Swain, M. (1995). Three functions of output in second language learning. In G. Cook, & B. Seidlhofer (Eds.), Principle and practice in applied linguistics: Studies in honour of H. G. Widdowson, Oxford: Oxford.
Sweller, J., Ayres, P., and Kalyuga, S. (2011). Cognitive load theory in perspective. In Cognitive load theory, pp. 237–242. Springer. https://doi.org/10.1007/978-1-4419-8126-4_18
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., and Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 1–24. https://doi.org/10.1186/s40561-023-00237-x
Ulla, M. B., Perales, W. F., and Busbus, S. O. (2023). “To generate or stop generating response”: Exploring EFL teachers’ perspectives on ChatGPT in English language teaching in Thailand. Learning: Research and Practice, 1–15. https://doi.org/10.1080/23735082.2023.2257252
Umar, U. (2024). Advancements in English language teaching: Harnessing the power of artificial intelligence. FLIP: Foreign Language Instruction Probe, 3(1), 29–42. https://doi.org/10.54213/flip.v3i1.402
Wei, L. (2023). Artificial intelligence in language instruction: Impact on English learning achievement, L2 motivation, and self-regulated learning. Frontiers in Psychology, 14, Article 1261955. https://doi.org/10.3389/fpsyg.2023.1261955
Zhai, X. (2023). ChatGPT and AI: The game changer for education. 67, 16-17 (1-4). https://doi.org/10.13140/RG.2.2.31107.37923