AI as Rubric Mediator: A Reflective Practice Brief on GPT-Supported Assessment for Learning in Music Education
DOI:
https://doi.org/10.37134/ajatel.vol16.1.6.2026Keywords:
generative AI, assessment for learning, feedback literacy, evaluative judgement, self-assessment, music education, rubric-mediated feedbackAbstract
Generative artificial intelligence (GenAI) has intensified debates about academic integrity, authorship, assessment validity, and the future of teacher judgement in higher education. Yet the discussion should not be reduced to whether AI should be prohibited or accepted. A more educationally productive question is how AI may be designed and bounded to support assessment for learning, feedback literacy, evaluative judgement, and ethical self-assessment. This brief communication presents a reflective practice account from AMU60104 Assessment in Music Education Research, a master’s coursework course involving 18 consenting students, most of whom were practising music teachers in primary, secondary, or private music education settings. A customised GPT, AMU60104 Assessment & Feedback Assistant (M251PM2), was introduced to support students’ self-assessment after they had posted weekly reflections in the learning management system. Students were guided to use the tool to understand rubrics, identify strengths and areas for improvement, and strengthen future reflective writing, while being explicitly instructed not to use AI to generate their reflections. Interpreted through feedback literacy, evaluative judgement, relational validity, and situated AI ethics, the practice suggests that AI-supported assessment is educationally defensible only when it preserves student authorship, strengthens human judgement, and deepens assessment understanding. As a reflective practice account, the paper focuses on students’ perceptions and experiences of using GPT-supported feedback rather than evaluating the content of individual prompts, AI-generated responses, or the technical accuracy of the feedback provided. The paper argues that the value of AI in assessment lies not in replacing teachers or automating judgement, but in mediating students’ engagement with criteria, evidence, feedback, and ethical responsibility.
Downloads
References
Adarkwah, M. A. (2021). The power of assessment feedback in teaching and learning: A narrative review and synthesis of the literature. SN Social Sciences, 1, Article 75. https://doi.org/10.1007/s43545-021-00086-w
Bearman, M., Tai, J., Dawson, P., Boud, D., & Ajjawi, R. (2024). Developing evaluative judgement for a time of generative artificial intelligence. Assessment & Evaluation in Higher Education, 49(6), 893–905. https://doi.org/10.1080/02602938.2024.2335321
Bizami, N. A., Tasir, Z., & Kew, S. N. (2023). Innovative pedagogical principles and technological tools capabilities for immersive blended learning: A systematic literature review. Education and Information Technologies, 28, 1373–1425. https://doi.org/10.1007/s10639-022-11243-w
Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74. https://doi.org/10.1080/0969595980050102
Black, P., & Wiliam, D. (2018). Classroom assessment and pedagogy. Assessment in Education: Principles, Policy & Practice, 25(6), 551–575. https://doi.org/10.1080/0969594X.2018.1441807
Carless, D. (2015). Excellence in university assessment: Learning from award-winning practice. Routledge. https://doi.org/10.4324/9781315740621
Carless, D., & Boud, D. (2018). The development of student feedback literacy: Enabling uptake of feedback. Assessment & Evaluation in Higher Education, 43(8), 1315–1325. https://doi.org/10.1080/02602938.2018.1463354
Din Eak, A., Ooi, L. H., & Tan, S. F. (2025). Relational validity in practice: A decade of research on alternative assessment in English language teaching higher education (2014–2024). Asian Journal of Assessment in Teaching and Learning, 15(2), 96–110. https://doi.org/10.37134/ajatel.vol15.2.7.2025
Gartner, S., & Krašna, M. (2023). Ethics of artificial intelligence in education. Journal of Elementary Education, 16(2), 221–237. https://doi.org/10.18690/rei.16.2.2846
Gaur, A. S., Sharan, H. O., & Kumar, R. (2024). AI in education: Ethical challenges and opportunities. In The ethical frontier of AI and data analysis (pp. 39–54). IGI Global. https://doi.org/10.4018/979-8-3693-2964-1.ch003
Gedera, D. (2023). A holistic approach to authentic assessment. Asian Journal of Assessment in Teaching and Learning, 13(2), 23–34. https://doi.org/10.37134/ajatel.vol13.2.3.2023
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
Lian Zhao, & Ting, S.-H. (2025). Formative assessment through supervisory feedback on undergraduate thesis writing. Asian Journal of Assessment in Teaching and Learning, 15(1), 126–141. https://doi.org/10.37134/ajatel.vol15.1.9.2025
Mazlan, C. A. N., Abdullah, M. H., Othman, M. A., Md Noor, A. R., & Jamnongsarn, S. (2026a). From abstract ethics to situated practice: A bibliometric analysis of AI ethics and professional judgement. Educational Technology Research and Development. https://doi.org/10.1007/s11423-026-10630-1
Mazlan, C. A. N., Abdullah, M. H., Sulong, M. A., Abas, A., Uyub, A. I., Pisali, A., Hidayatullah, R., & Daud, I. S. (2024). Revolutionizing music education: Bite-sized learning and TikTok in the context of Education 5.0. The International Journal of Arts, Culture & Heritage, 12(4), 79–122. https://doi.org/10.62312/asw.ijach.12.4.2024
Mazlan, C. A. N., Hanafi, H. F., Sarifin, M. R., Md Noor, A. R., Sadykova, S. A., Hidayatullah, R., & Jamnongsarn, S. (2026b). Artificial intelligence applications and pedagogical challenges in music education. Discover Education, 5, Article 140. https://doi.org/10.1007/s44217-026-01127-3
Mazlan, C. A. N., Mohd Yusoff, M. Y., Safian, A. R., Koning, S. I., Saearani, M. F. T. B., Md Noor, A. R., Sadykova, S. A., & Sinaga, F. S. S. (2025). The evolution of modern learning: A dual systematic review of emerging trends and challenges. Asian Journal of University Education, 21(3), 793–813. https://doi.org/10.24191/ajue.v21i3.54
Mohamed Sapawi, M. S., & Nik Yusoff, N. M. R. (2025). Artificial intelligence in curriculum development: A global systematic review of trends, challenges, and strategic directions. Journal of Curriculum Studies Research, 7(2), 466–497. https://doi.org/10.46303/jcsr.2025.30
Ngu, I.Y., Lau, S.Y., & Schellini, M. (2025). Student’s attitudes and motivations toward using ChatGPT feedback for personalised learning. Asian Journal of Assessment in Teaching and Learning, 15(2), 43–57. https://doi.org/10.37134/ajatel.vol15.2.3.2025
Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218. https://doi.org/10.1080/03075070600572090
Niloy, A. C., & Huda, T. (2025). Is AI only to blame? Assessing teachers’ perceived challenges in AI detectability. Asian Journal of Assessment in Teaching and Learning, 15(2), 21–42. https://doi.org/10.37134/ajatel.vol15.2.2.2025
Panadero, E., Brown, G. T. L., & Strijbos, J.-W. (2016). The future of student self-assessment: A review of known unknowns and potential directions. Educational Psychology Review, 28(4), 803–830. https://doi.org/10.1007/s10648-015-9350-2
Parkes, K. A., & Burrack, F. (Eds.). (2020). Developing and applying assessments in the music classroom. Routledge. https://doi.org/10.4324/9780429202308
Sadler, D. R. (2010). Fidelity as a precondition for integrity in grading academic achievement. Assessment & Evaluation in Higher Education, 35(6), 727–743. https://doi.org/10.1080/02602930902977756
Sankaran, S., & Low, S. F. (2025). Effectiveness of feedback on continuous assessment: Students’ views. Asian Journal of Assessment in Teaching and Learning, 15(1), 49–62. https://doi.org/10.37134/ajatel.vol15.1.4.2025
Song, M. H., Park, J., Lim, J., & Park, J. (2025). Improving feedback literacy through peer evaluation: A quantitative and qualitative analysis. Innovations in Education and Teaching International. https://doi.org/10.1080/14703297.2025.2593371
Tai, J., Ajjawi, R., Boud, D., Dawson, P., & Panadero, E. (2018). Developing evaluative judgement: Enabling students to make decisions about the quality of work. Higher Education, 76(3), 467–481. https://doi.org/10.1007/s10734-017-0220-3
Walter, Y. (2024). Embracing the future of artificial intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21, Article 15. https://doi.org/10.1186/s41239-024-00448-3
Wiliam, D. (2011). What is assessment for learning? Studies in Educational Evaluation, 37(1), 3–14. https://doi.org/10.1016/j.stueduc.2011.03.001
Wu, S. P., Choi, Y. F., & Patel, L. (2025). Transforming feedback into learning throughout the trajectory of competency based medical education. Indian Pediatrics, 62(3), 221–227. https://doi.org/10.1007/s13312-025-00012-w
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Sajastanah Imam Koning, Chamil Arkhasa Nikko Mazlan
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
