GRAMMAR ACHIEVEMENT IN AI-SUPPORTED EFL LEARNING: ACADEMIC ENGAGEMENT, AI LITERACY, AND TASK-ASSESSMENT ALIGNMENT
DOI:
https://doi.org/10.35631/IJEPC.1163016Keywords:
Academic Engagement, AI Literacy, AI-Supported Learning, EFL Learning, Grammar Achievement, Task-Assessment AlignmentAbstract
As artificial intelligence (AI) becomes increasingly integrated into higher education, grammar achievement in English as a Foreign Language (EFL) context remains uneven, suggesting that technology alone is insufficient to ensure effective learning. Existing research has often examined AI-supported learning, academic engagement, AI literacy, and grammar achievement separately, leaving unclear how these factors interact under conditions of task-assessment alignment. This review adopts a targeted conceptual narrative approach to examine how academic engagement and AI literacy shape grammar achievement in AI-supported environments. Drawing on a narrative synthesis of research in language education, educational psychology, and learning analytics, this review identifies key mechanisms underlying grammar learning outcomes. The synthesis indicates that academic engagement functions as a proximal learning mechanism through which grammar-related learning processes are sustained, while AI literacy serves as an enabling condition that shapes the quality of learner engagement. In addition, task-assessment alignment is identified as a validity constraint influencing whether AI-supported learning behaviours meaningfully reflect assessed outcomes. This review proposes a novel mechanism-condition-constraint framework and provides a theoretically grounded basis for designing engagement-driven tasks, scaffolding learner AI literacy, and aligning AI-supported learning activities with grammar assessment in higher education EFL contexts.
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References
Bergdahl, N., Bond, M., Sjöberg, J., Dougherty, M., & Oxley, E. (2024). Unpacking student engagement in higher education learning analytics: A systematic review. International Journal of Educational Technology in Higher Education, 21, Article 63. https://doi.org/10.1186/s41239-024-00493-y
Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
Ellis, R. (2006). Current issues in the teaching of grammar: An SLA perspective. TESOL Quarterly, 40(1), 83–107. https://doi.org/10.2307/40264512
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059
Fredricks, J. A., Filsecker, M., & Lawson, M. A. (2016). Student engagement, context, and adjustment: Addressing definitional, measurement, and methodological issues. Learning and Instruction, 43, 1–4. https://doi.org/10.1016/j.learninstruc.2016.02.002
Lei, H., Cui, Y., & Zhou, W. (2018). Relationships between student engagement and academic achievement: A meta-analysis. Social Behavior and Personality: An International Journal, 46(3), 517–528. https://doi.org/10.2224/sbp.7054
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). Association for Computing Machinery. https://doi.org/10.1145/3313831.3376727
Lu, C. (2025). AI-generated corpus learning and EFL learners’ learning of grammatical structures, lexical bundles, and willingness to write. PLOS ONE, 20(7), Article e0321544. https://doi.org/10.1371/journal.pone.0321544
Mazzullo, E., Bulut, O., Wongvorachan, T., & Tan, B. (2023). Learning analytics in the era of large language models. Analytics, 2(4), 877–898. https://doi.org/10.3390/analytics2040046
Mohebbi, A. (2025). Enabling learner independence and self-regulation in language education using AI tools: A systematic review. Cogent Education, 12(1), Article 2433814. https://doi.org/10.1080/2331186X.2024.2433814
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, Article 100041. https://doi.org/10.1016/j.caeai.2021.100041
Ng, D. T. K., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu, S. K. W. (2024). Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach. British Journal of Educational Technology, 55(3), 1082–1104. https://doi.org/10.1111/bjet.13411
Popay, J., Roberts, H., Sowden, A., Petticrew, M., Arai, L., Rodgers, M., Britten, N., Roen, K., & Duffy, S. (2006). Guidance on the conduct of narrative synthesis in systematic reviews: A product from the ESRC Methods Programme. Lancaster University. https://doi.org/10.13140/2.1.1018.4643
Purpura, J. E. (2014). Assessing grammar. In A. J. Kunnan (Ed.), The companion to language assessment (pp. 100–124). John Wiley & Sons. https://doi.org/10.1002/9781118411360.wbcla147
Reeve, J. (2012). A self-determination theory perspective on student engagement. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 149–172). Springer. https://doi.org/10.1007/978-1-4614-2018-7_7
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
Thomas, J., & Harden, A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Medical Research Methodology, 8, Article 45. https://doi.org/10.1186/1471-2288-8-45
UNESCO. (2021). AI and education: Guidance for policy-makers. UNESCO. https://doi.org/10.54675/PCSP7350
Wang, Y., Singh, S. S. B., Zheng, J., & Zhang, Q. (in press). Academic engagement and artificial intelligence platform behaviors in grammar achievement. International Journal of Evaluation and Research in Education.
Yang, C., & Singh, S. S. B. (2024). User experience in information system platforms: A study on learning styles and academic challenges. Journal of Internet Services and Information Security, 14(4), 209–223. https://doi.org/10.58346/JISIS.2024.I4.012
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2
