Evaluating Generative AI as a Supportive Analytic Partner in Qualitative Coding of Metacognitive Student Reflections
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Published
May 24, 2026
Rebecca Marrone
Abhinava Barthakur
David Randall
Nazanin Mottaghi
Vitomir Kovanovic
Maarten de Laat
https://orcid.org/0000-0002-2437-6892
Abstract
Qualitative data provides rich insight into student and educator thinking but remains difficult to analyse systematically at scale. The rise of generative artificial intelligence (GenAI) introduces new opportunities for pattern recognition and interpretive support, while also raising questions about how such systems can be responsibly embedded in educational research workflows. This study investigates the application of a fine-tuned GenAI model to classify metacognitive elements in student self-reflections and examines the methodological and epistemological implications of this process.In partnership with a secondary school, a university, and a state education department, the study analysed more than 14,000 student reflection artefacts collected between 2018 and 2023. A total of 4,631 samples were manually coded for four sub-elements of metacognition—Goal Setting, Strategy Choice, Reflection on Learning, and Effort Regulation—which were then used to train and evaluate a fine-tuned GPT‑4o-mini model that achieved 80.98% classification accuracy. However, our analysis also raises critical questions. While the model could detect the existence of metacognitive constructs, it lacked the pedagogical and contextual grounding to assess their quality or relevance and required significant human effort. These findings highlight the need to reconceptualise GenAI not as a replacement for human judgement, but as a supportive analytic partner. We argue that co-design processes between educators, researchers, and developers are essential to ensure AI systems are trustworthy, theoretically grounded, and practically useful. The approach outlined in this study provides a roadmap for extending the use of GenAI to other complex educational constructs, ensuring that AI is framed not merely as a tool but as a supportive analytic partner whose design reflects the needs and values of all system actors.
How to Cite
Evaluating Generative AI as a Supportive Analytic Partner in Qualitative Coding of Metacognitive Student Reflections. (2026). Journal of Educational Data Mining, 18(1), 318-340. https://doi.org/10.5281/
Details
Keywords
Metacognition, Human-in-the-loop, Artificial Intelligence (AI), Qualitative Research, K-12 education
References
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Calisto, F. M., Fernandes, J., Morais, M., Santiago, C., Abrantes, J. M., Nunes, N., and Nascimento, J. C. 2023. Assertiveness-based Agent Communication for a Personalized Medicine on Medical Imaging Diagnosis. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1–20. https://doi.org/10.1145/3544548.3580682
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Cronbach, L. J. 1951. Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.
Flavell, J. H. 1979. Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34, 10, 906–911.
Garg, R., Han, J., Cheng, Y., Fang, Z., and Swiecki, Z. 2024. Automated Discourse Analysis via Generative Artificial Intelligence. In Proceedings of the 14th Learning Analytics and Knowledge Conference, 814–820. https://doi.org/10.1145/3636555.3636879
Gascoine, L., Higgins, S., and Wall, K. 2017. The assessment of metacognition in children aged 4–16 years: A systematic review. Review of Education, 5, 1, 3–57. https://doi.org/10.1002/rev3.3077
Gibson, A., Kitto, K., and Bruza, P. 2016. Towards the Discovery of Learner Metacognition From Reflective Writing. Journal of Learning Analytics, 3, 2, 22–36. https://doi.org/10.18608/jla.2016.32.3
Gisev, N., Bell, J. S., & Chen, T. F. (2013). Interrater agreement and interrater reliability: key concepts, approaches, and applications. Research in Social and Administrative Pharmacy, 9(3), 330-338.
Harwood, T. G., and Garry, T. 2003. An overview of content analysis. The Marketing Review, 3, 4, 479–498.
Holstein, K., and Aleven, V. 2022. Designing for human–AI complementarity in K-12 education. AI Magazine, 43, 2, 239–248.
İpek, Z. H., Gözüm, A. İ. C., Papadakis, S., and Kallogiannakis, M. 2023. Educational Applications of the ChatGPT AI System: A Systematic Review Research. Educational Process International Journal, 12, 3. https://doi.org/10.22521/edupij.2023.123.2
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Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y.-S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., and Gašević, D. 2022. Explainable Artificial Intelligence in education. Computers and Education: Artificial Intelligence, 3, 100074. https://doi.org/10.1016/j.caeai.2022.100074
Kovanović, V., Joksimović, S., Mirriahi, N., Blaine, E., Gašević, D., Siemens, G., and Dawson, S. 2018. Understand students’ self-reflections through learning analytics. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge, 389–398. https://doi.org/10.1145/3170358.3170374
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Li, B., Bhattarai, A., and Ding, Z. 2025. In search of humanness: Professional identities of qualitative research educators in the age of generative AI. Learning, Media and Technology, 1–13. https://doi.org/10.1080/17439884.2025.2521547
Lichtman, M. 2023. Qualitative research in education: A user’s guide. Routledge.
Miles, M., Huberman, A.M., and Saldana, j. 2014. Qualitative Data Analysis: A Methods Sourcebook (4th. ed.). Sage.
Nguyen-Trung, K. (2025). ChatGPT in thematic analysis: Can AI become a research assistant in qualitative research?. Quality & Quantity, 59(6), 4945-4978.
Ohtani, K., and Hisasaka, T. 2018. Beyond intelligence: A meta-analytic review of the relationship among metacognition, intelligence, and academic performance. Metacognition and Learning, 13, 2, 179–212. https://doi.org/10.1007/s11409-018-9183-8
Ozturk, N. 2017. Assessing Metacognition: Theory and Practices. International Journal of Assessment Tools in Education, 134–134. https://doi.org/10.21449/ijate.298299
Parker, J., Richard, V., and Becker, K. 2023. Guidelines for the Integration of Large Language Models in Developing and Refining Interview Protocols. The Qualitative Report. https://doi.org/10.46743/2160-3715/2023.6801
Pintrich, P. R., Wolters, C. A., and Baxter, G. P. 2000. Assessing metacognition and self-regulated learning. https://digitalcommons.unl.edu/burosmetacognition/3/
Prescott, M. R., Yeager, S., Ham, L., Rivera Saldana, C. D., Serrano, V., Narez, J., Paltin, D., Delgado, J., Moore, D. J., and Montoya, J. 2024. Comparing the Efficacy and Efficiency of Human and Generative AI: Qualitative Thematic Analyses. JMIR AI, 3, e54482. https://doi.org/10.2196/54482
Ramadhanti, D., Ghazali, A. S., Hasanah, M., Harsiati, T., and Yanda, D. P. 2020. The Use of Reflective Journal as a Tool for Monitoring of Metacognition Growth in Writing. International Journal of Emerging Technologies in Learning (iJET), 15, 11, 162. https://doi.org/10.3991/ijet.v15i11.11939
Ramanathan, S., Lim, L. A., Mottaghi, N. R., & Buckingham Shum, S. (2025, March). When the prompt becomes the codebook: Grounded prompt engineering (groproe) and its application to belonging analytics. In Proceedings of the 15th International Learning Analytics and Knowledge Conference (pp. 713-725).
Saldaña, J. 2015. The Coding Manual for Qualitative Research (3rd. ed.). Sage, Newcastle upon Tyne.
Schraw, G. 2000. Assessing Metacognition: Implications Of The Buros Symposium. In Issues in the measurement of metacognition, G. Schraw, and J. C. Impara, Eds. Lincoln, Nebraska: Buros Institute of Mental Measurements, 297–321.
Schroeder, H., Aubin Le Quéré, M., Randazzo, C., Mimno, D., and Schoenebeck, S. 2025. Large Language Models in Qualitative Research: Uses, Tensions, and Intentions. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 1–17. https://doi.org/10.1145/3706598.3713120
Schunk, D. H., and Greene, J. A. 2017. Historical, contemporary, and future perspectives on self-regulated learning and performance. In Handbook of self-regulation of learning and performance. Routledge, 1–15.
Siemens, G., Marmolejo-Ramos, F., Gabriel, F., Medeiros, K., Marrone, R., Joksimovic, S., and de Laat, M. 2022. Human and artificial cognition. Computers and Education: Artificial Intelligence, 3, 100107.
Siiman, L. A., Rannastu-Avalos, M., Pöysä-Tarhonen, J., Häkkinen, P., and Pedaste, M. 2023. Opportunities and Challenges for AI-Assisted Qualitative Data Analysis: An Example from Collaborative Problem-Solving Discourse Data. In Innovative Technologies and Learning. Y.-M. Huang and T. Rocha. Eds., 14099. Springer Nature Switzerland, 87–96. https://doi.org/10.1007/978-3-031-40113-8_9
Silver, N. 2023. Reflective Pedagogies and the Metacognitive Turn in College Teaching. In Using Reflection and Metacognition to Improve Student Learning (1st ed.), D. LaVaque-Manty, D. Meizlish, N. Silver, M. Kaplan, and J. Rhem, Eds. Routledge, 1–17. https://doi.org/10.4324/9781003448570-1
Strauss, A., and Corbin, J. 1998. Basics of qualitative research techniques. Sage Publications, Inc.
Ullmann, T. D. 2019. Automated Analysis of Reflection in Writing: Validating Machine Learning Approaches. International Journal of Artificial Intelligence in Education, 29, 2, 217–257. https://doi.org/10.1007/s40593-019-00174-2
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