Governing Classroom AI: Transparency, Integrity; and Learning in TEFL/EAP
DOI:
https://doi.org/10.55480/saluscultura.v5i2.477Keywords:
Academic Integrity, TEFL, EAP, AI Classroom Contract, Learning AnalyticsAbstract
The rapid uptake of generative AI in TEFL and EAP has outpaced course-level governance, particularly with respect to transparent AI use, academic integrity, and data protection. This study aims to develop and evaluate a human-centred AI Classroom Contract as an auditable micro-policy that bridges the gap between guidance and classroom practice. Over eight months, we conducted a design-based study combined with a quasi-experimental comparison across classes with three collection points: baseline, mid-course, and end. Data included per-assignment AI transparency forms, learning-management-system logs on revision iterations, time on task, and punctuality, draft–revision artefacts with writing and speaking rubric scores, integrity audits, and surveys on perceived fairness and transparency. Analyses used multilevel modelling and mediation tests. Findings indicate higher completeness of transparency statements, fewer mis-citation and fabrication incidents, more iterative revision, improved punctuality, and moderate gains in task performance. Perceived fairness and transparency mediated effects on compliance and outcomes. Implementation was feasible under limited connectivity without meaningful privacy breaches. We conclude that the AI Classroom Contract is an effective micro-policy instrument that connects human-centred principles to TEFL and EAP practice, yielding a replicable package of the contract, templates, and a fact-check rubric to support quality and accountability in language learning.References
British Council. (2024). Artificial intelligence and English language teaching: Preparing for the future. British Council.
Bretag, T. (2019). Contract cheating in higher education: A comprehensive review. Higher Education, 77, 593–610.
Brookhart, S. M. (2017). How to give effective feedback to your students (2nd ed.). ASCD.
Carless, D., & Boud, D. (2018). The development of student feedback literacy: Enabling uptake of feedback. Assessment & Evaluation in Higher Education, 43(8), 1315–1325.
Eaton, S. E. (2021). Plagiarism in higher education: Tackling tough topics in academic integrity. Libraries Unlimited.
Fitriati, S. W. (2025). AI-enhanced self-regulated learning among Indonesian EFL learners. ERIC.
Fleckenstein, J., Liebenow, L. W., & Meyer, J. (2023). Automated feedback and writing: A multilevel meta-analysis. Frontiers in Artificial Intelligence.
Hyland, K., & Hyland, F. (2019). Feedback in second language writing (2nd ed.). Routledge.
Igartua, J. J., & Hayes, A. F. (2021). Mediation, moderation, and conditional process analysis in communication research. Communication Methods and Measures, 15(3), 1–22.
Jisc. (2024). Embracing generative AI in assessments: A guided approach. Jisc.
Kasneci, S., Sessler, K., Straubinger, S., & Kasneci, E. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.
Kovanović, V., Joksimović, S., Gašević, D., & Siemens, G. (2015). Penetrating the black box of time-on-task estimation. In Proceedings of the 5th International Conference on Learning Analytics & Knowledge (LAK’15) (pp. 184–193). ACM.
Liu, S., Zhang, Y., & Chen, L. (2025). Do mobile games improve language learning? A meta-analysis. Computers & Education.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
Mah, C. (2024). Teacher versus LLM feedback: Qualitative insights with randomized evidence context. Annenberg Institute EdWorkingPapers.
Meyer, J., Kumar, N., & Li, Q. (2024). Using large language models to bring evidence-based feedback into the classroom. Patterns.
Nicol, D. (2020). The power of internal feedback: Exploiting the social, cognitive and evaluative processes of self-assessment. Assessment & Evaluation in Higher Education, 45(5), 1–12.
OECD. (2024). The potential impact of AI on equity and inclusion in education. OECD Publishing.
Pack, A. (2024). Using AI in TESOL: Ethical and pedagogical considerations. TESOL Quarterly.
Pan, Z., Xing, W., & Chen, G. (2024). Learning analytics interventions in higher education: A systematic review. Journal of Learning Analytics, 11(2), 1–28.
Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8, 422.
Peña-Acuña, B., López-Meneses, E., & Vázquez-Cano, E. (2024). Learning English with AI: Opportunities and challenges. Frontiers in Education.
QAA. (2022). Contracting to cheat in higher education: How to address contract cheating. The Quality Assurance Agency for Higher Education (UK).
Qiu, X., Ma, Y., & Chen, H. (2024). The effects of virtual reality on EFL learning: A meta-analysis. Education and Information Technologies.
TESOL International Association. (2024). Position statement on artificial intelligence in TESOL. TESOL.
UNESCO. (2023). Guidance for generative AI in education and research. UNESCO.
Werdiningsih, I. (2024). Strategic use of ChatGPT by postgraduate EFL students: Perceptions and practices. Cogent Education.
Wei, P., Wang, X., & Dong, H. (2023). Artificial intelligence-based writing instruction for EFL students: A randomized controlled trial. Frontiers in Psychology.
Winstone, N. E., & Carless, D. (2020). Designing effective feedback processes in higher education: A learning-focused approach. Routledge.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(39), 1–27.
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Estiningtyas Sholikhah, Lutfiana Lutfiana
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Manuscript Template
