Personalisation versus Equity in AI-Driven Formative Feedback: Evidence from Heterogeneous Learner Populations
DOI:
https://doi.org/10.62872/5n9cmy65Keywords:
responsible AI, formative feedback, personalisation, algorithmic fairness, inclusivity, higher educationAbstract
This study examines whether AI-driven personalised formative feedback improves learning while maintaining equity across heterogeneous learner populations. Grounded in self-regulated learning theory, feedback intervention theory, universal design for learning, and algorithmic fairness, the study examines the relationships among AI feedback, perceived feedback quality, student engagement, and learning achievement. A quantitative explanatory design was employed with diverse higher education students using an AI-enabled learning platform. Data were collected through pre- and post-tests, system logs, and validated survey instruments. Structural equation modelling and multigroup analysis were used to test direct, mediated, moderated, and subgroup effects. The findings are expected to show that AI-driven feedback enhances perceived feedback quality, engagement, and achievement. However, its benefits may vary across learners with different prior achievement, digital literacy, socioeconomic background, and language proficiency. The study contributes to educational technology research by moving beyond effectiveness claims and examining whether personalisation produces inclusive or unequal outcomes. The findings offer practical implications for designing transparent, fair, and pedagogically responsible AI feedback systems that support both individualised learning and educational equity.
Downloads
References
Al-Domi, H. A., AL-Dalaeen, A., ALRosan, S. H., Batarseh, N., & Nawaiseh, H. (2021). Healthy Nutritional Behavior During COVID-19 Lockdown: A Cross-Sectional Study. Clinical Nutrition Espen, 42, 132–137. https://doi.org/10.1016/j.clnesp.2021.02.003
Alharbi, W. (2023). AI in the Foreign Language Classroom: A Pedagogical Overview of Automated Writing Assistance Tools. Education Research International, 2023, 1–15. https://doi.org/10.1155/2023/4253331
Anh, B. N. T., & Phạm, M. (2022). Combination of SCCT and TPB in Explaining the Social Entrepreneurial Intention. Ho Chi Minh City Open University Journal of Science - Economics and Business Administration, 12(2), 127–138. https://doi.org/10.46223/hcmcoujs.econ.en.12.2.2140.2022
Bahg, G., Sloutsky, V. M., & Turner, B. M. (2025). Algorithmic Personalization of Information Can Cause Inaccurate Generalization and Overconfidence. Journal of Experimental Psychology General, 154(9), 2503–2522. https://doi.org/10.1037/xge0001763
Bai, X., & Stede, M. (2023). A Survey of Current Machine Learning Approaches to Student Free-Text Evaluation for Intelligent Tutoring. International Journal of Artificial Intelligence in Education, 33(4), 994–1032. https://doi.org/10.1007/s40593-022-00323-0
Berdahl, C. T., Baker, L., Mann, S., Osoba, O., & Girosi, F. (2023). Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review. Jmir Ai, 2, e42936. https://doi.org/10.2196/42936
Chen, H., Wang, C., Wu, J., Wang, M., Wang, S., Wang, X., Wang, J., Yu, H., Hu, Y., & Shang, S. (2022). Measurement Properties of Performance-Based Measures to Assess Physical Function in Knee Osteoarthritis: A Systematic Review. Clinical Rehabilitation, 36(11), 1489–1511. https://doi.org/10.1177/02692155221107731
Chen, S.-Y., & Chen, W. (2025). Driven Intelligent Feedback System for Enhancing Self‐Assessment Accuracy in Higher Education Writing. Expert Systems, 43(1). https://doi.org/10.1111/exsy.70184
Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Aldraiweesh, A., Alturki, U., Almutairy, S., Shutaleva, A., & Soomro, R. B. (2024). Extended TAM-Based Acceptance of AI-Powered ChatGPT for Supporting Metacognitive Self-Regulated Learning in Education: A Mixed-Methods Study. Heliyon, 10(8), e29317. https://doi.org/10.1016/j.heliyon.2024.e29317
Davis, S. E., Dorn, C., Park, D., & Matheny, M. E. (2025). Emerging Algorithmic Bias: Fairness Drift as the Next Dimension of Model Maintenance and Sustainability. Journal of the American Medical Informatics Association, 32(5), 845–854. https://doi.org/10.1093/jamia/ocaf039
Gallegos-Rejas, V., Kelly, J. T., Lucas, K., Snoswell, C. L., Haydon, H. M., Pager, S., Smith, A. C., & Thomas, E. (2023). A Cross-Sectional Study Exploring Equity of Access to Telehealth in Culturally and Linguistically Diverse Communities in a Major Health Service. Australian Health Review, 47(6), 721–728. https://doi.org/10.1071/ah23125
Ganta, T., Kia, A., Parchure, P., Wang, M., Besculides, M., Mazumdar, M., & Smith, C. B. (2024). Fairness in Predicting Cancer Mortality Across Racial Subgroups. Jama Network Open, 7(7), e2421290. https://doi.org/10.1001/jamanetworkopen.2024.21290
González-Carrillo, C. D., Restrepo‐Calle, F., Echeverry, J. J. R., & González, F. A. (2021). Automatic Grading Tool for Jupyter Notebooks in Artificial Intelligence Courses. Sustainability, 13(21), 12050. https://doi.org/10.3390/su132112050
Griffin, A. (2024). The Pivot to Online Teaching: An Opportunity to Create Effective Problem‐based Learning Environments for Dietetic Education. Journal of Human Nutrition and Dietetics, 38(1). https://doi.org/10.1111/jhn.13378
Grupen, N. A., Selman, B., & Lee, D. D. (2022). Cooperative Multi-Agent Fairness and Equivariant Policies. Proceedings of the Aaai Conference on Artificial Intelligence, 36(9), 9350–9359. https://doi.org/10.1609/aaai.v36i9.21166
Hall, G. J., & Swanlund, L. (2023). Differential Nonlinear Relations of Language Proficiencies to Reading and Math Achievement in Spanish or English. School Psychology, 38(5), 294–307. https://doi.org/10.1037/spq0000545
Isroani, F., Jaafar, N., & Muflihaini, M. (2022). Effectiveness of E-Learning in Improving Student Learning Outcomes at Madrasah Aliyah. International Journal of Science Education and Cultural Studies, 1(1), 42–51. https://doi.org/10.58291/ijsecs.v1i1.26
Jarudin, & Dedi. (2026). Personalisation and Predictive Insights: Applied AI Strategies in Modern Learning Technologies. Digital Education Review, 49(49), 95–115. https://doi.org/10.1344/der.2026.49.95-114
Kim, H. H., & Ryu, J. (2021). Social Distancing Attitudes, National Context, and Health Outcomes During the COVID-19 Pandemic: Findings From a Global Survey. Preventive Medicine, 148, 106544. https://doi.org/10.1016/j.ypmed.2021.106544
Lam, J., Coret, M., Khalil, C., Butler, K., Giroux, R., & Martimianakis, M. A. (2024). The Need for Critical and Intersectional Approaches to Equity Efforts in Postgraduate Medical Education: A Critical Narrative Review. Academic Medicine, 58(12), 1442–1461. https://doi.org/10.1111/medu.15425
Lazarus, J. V, Wyka, K., White, T. M., Picchio, C. A., Rabin, K., Ratzan, S. C., Leigh, J. P., Hu, J., & El-Mohandes, A. (2022). Revisiting COVID-19 Vaccine Hesitancy Around the World Using Data From 23 Countries in 2021. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-31441-x
Li, L., Chen, H., Chen, W., & Yang, J. (2025). Microeukaryotic Habitat Specialists Exhibit Stronger Determinism and Biodiversity-Nutrient Cycling Relationship Than Generalists in a Subtropical River. Applied and Environmental Microbiology, 91(10). https://doi.org/10.1128/aem.01364-25
Liem, V. T. (2025). The Impact of Leadership Style, Reward System, Environmental Strategy, and Environmental Management Accounting on Environmental Performance of Vietnamese Manufacturers. Plos One, 20(5), e0323662. https://doi.org/10.1371/journal.pone.0323662
Lin, S., Tsai, P.-C., Su, F.-Y., Chen, C.-Y., Li, F., Zhao, J., Ho, Y. Y., Lee, M. T., Healey, E., Lin, P.-J., Kao, T., Vremenko, D., Roetzer-Pejrimovsky, T., Sholl, L. M., Dillon, D., Lin, N. U., Meredith, D. M., Ligon, K. L., Lo, Y., … Yu, K. (2025). Contrastive Learning Enhances Fairness in Pathology Artificial Intelligence Systems. Cell Reports Medicine, 6(12), 102527. https://doi.org/10.1016/j.xcrm.2025.102527
Lu, J., Sattler, A., Wang, S., Khaki, A. R., Callahan, A., Fleming, S. L., Fong, R., Ehlert, B., Li, R., Shieh, L., Ramchandran, K., Gensheimer, M. F., Chobot, S. E., Pfohl, S., Li, S., Shum, K., Parikh, N., Desai, P., Seevaratnam, B., … Shah, N. H. (2022). Considerations in the Reliability and Fairness Audits of Predictive Models for Advance Care Planning. Frontiers in Digital Health, 4. https://doi.org/10.3389/fdgth.2022.943768
Maheshi, B., Dai, W., Martínez‐Maldonado, R., & Tsai, Y. (2024). Dialogic Feedback at Scale: Recommendations for Learning Analytics Design. Journal of Computer Assisted Learning, 40(6), 2790–2808. https://doi.org/10.1111/jcal.13034
Mangaroska, K., Martínez‐Maldonado, R., Vesin, B., & Gašević, D. (2021). Challenges and Opportunities of Multimodal Data in Human Learning: The Computer Science Students' Perspective. Journal of Computer Assisted Learning, 37(4), 1030–1047. https://doi.org/10.1111/jcal.12542
Marshik, T., McCracken, C. C., Kopp, B., & O'Marrah, M. (2024). Student and Instructor Perceptions and Uses of Artificial Intelligence in Higher Education. Teaching of Psychology, 52(3), 339–346. https://doi.org/10.1177/00986283241299745
Nayyar, D., Pendrith, C., Kishimoto, V., Chu, C., Fujioka, J., Rios, P., Bhatia, R. S., Lyons, O. D., Harvey, P., O'Brien, T., Martin, D., Agarwal, P., & Mukerji, G. (2022). Quality of Virtual Care for Ambulatory Care Sensitive Conditions: Patient and Provider Experiences. International Journal of Medical Informatics, 165, 104812. https://doi.org/10.1016/j.ijmedinf.2022.104812
Ni, A., & Cheung, A. (2022). Understanding Secondary Students' Continuance Intention to Adopt an AI-powered Intelligent Tutoring System for English Learning. Education and Information Technologies, 28(3), 3191–3216. https://doi.org/10.1007/s10639-022-11305-z
Pogorskiy, E., & Beckmann, J. F. (2023). From Procrastination to Engagement? An Experimental Exploration of the Effects of an Adaptive Virtual Assistant on Self-Regulation in Online Learning. Computers and Education Artificial Intelligence, 4, 100111. https://doi.org/10.1016/j.caeai.2022.100111
Rana, Md. M. Siddiqee, M. S., Sakib, Md. N., & Ahamed, Md. R. (2024). Assessing AI Adoption in Developing Country Academia: A Trust and Privacy-Augmented UTAUT Framework. Heliyon, 10(18), e37569. https://doi.org/10.1016/j.heliyon.2024.e37569
Schwensow, N., Heni, A. C., Schmid, J., Montero, B. K., Brändel, S. D., Halczok, T. K., Mayer, G., Fackelmann, G., Wilhelm, K., Schmid, D., & Sommer, S. (2022). Disentangling Direct From Indirect Effects of Habitat Disturbance on Multiple Components of Biodiversity. Journal of Animal Ecology, 91(11), 2220–2234. https://doi.org/10.1111/1365-2656.13802
Sun, F. (2026). Effect of Artificial Intelligence‐Based Tutoring, Cognitive Engagement, and Teacher Feedback on University Students' Academic Performance. European Journal of Education, 61(2). https://doi.org/10.1111/ejed.70619
Teferi, B., Omar, M., Jeyakumar, T., Charow, R., Gillan, C., Jardine, J., Mattson, J., Dhalla, A., Koçak, S. A., Salhia, M., Davies, B. R., Clare, M., Younus, S., & Wiljer, D. (2023). Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Prioritizing IDEA to Champion a Collaborative Educational Approach in a Stressed System. Education Sciences, 14(1), 39. https://doi.org/10.3390/educsci14010039
Toyokawa, Y., Horikoshi, I., Majumdar, R., & Ogata, H. (2023). Challenges and Opportunities of AI in Inclusive Education: A Case Study of Data-Enhanced Active Reading in Japan. Smart Learning Environments, 10(1). https://doi.org/10.1186/s40561-023-00286-2
Ulum, Ö. G. (2024). Unveiling the Layers: Analyzing ChatGPT Implementations in Turkish State Universities. Base for Electronic Educational Sciences, 5(1), 114–134. https://doi.org/10.29329/bedu.2024.651.7
Ummels, D., Bols, E., Frantzen, R. J. A., Frantzen, T., Robeerts, L., & Beekman, E. (2025). Activity Trackers in Physical Therapy for People With Chronic Obstructive Pulmonary Disease in the Netherlands: Cross-Sectional Study on Current Use and Implementation Determinants. Jmir Formative Research, 9, e59533–e59533. https://doi.org/10.2196/59533
Viberg, O., Baars, M., Mello, R. F., Weerheim, N., Spikol, D., Bogdan, C., Gašević, D., & Paas, F. (2024). Exploring the Nature of Peer Feedback: An Epistemic Network Analysis Approach. Journal of Computer Assisted Learning, 40(6), 2809–2821. https://doi.org/10.1111/jcal.13035
Vostal, B. R., Oehrtman, J. P., & Gilfillan, B. H. (2023). School Counselors Engaging All Students: Universal Design for Learning in Classroom Lesson Planning. Professional School Counseling, 27(1). https://doi.org/10.1177/2156759x231203199
Wang, Q. (2025). EFL Learners' Motivation and Acceptance of Using Large Language Models in English Academic Writing: An Extension of the UTAUT Model. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1514545
Watts, F. M., Dood, A. J., Shultz, G. V, & Rodriguez, J.-M. G. (2023). Comparing Student and Generative Artificial Intelligence Chatbot Responses to Organic Chemistry Writing-to-Learn Assignments. Journal of Chemical Education, 100(10), 3806–3817. https://doi.org/10.1021/acs.jchemed.3c00664
Weber, F., Wambsganß, T., & Söllner, M. (2024). Enhancing Legal Writing Skills: The Impact of Formative Feedback in a Hybrid Intelligence Learning Environment. British Journal of Educational Technology, 56(2), 650–677. https://doi.org/10.1111/bjet.13529
Weidlich, J., Fink, A., Jivet, I., Yau, J. Y., Giorgashvili, T., Drachsler, H., & Frey, A. (2024). Emotional and Motivational Effects of Automated and Personalized Formative Feedback: The Role of Reference Frames. Journal of Computer Assisted Learning, 40(6), 2735–2752. https://doi.org/10.1111/jcal.13024
Yao, S., Dai, F., Sun, P., Zhang, W., Qian, B., & Lü, H. (2024). Enhancing the Fairness of AI Prediction Models by Quasi-Pareto Improvement Among Heterogeneous Thyroid Nodule Population. Nature Communications, 15(1). https://doi.org/10.1038/s41467-024-44906-y
Yue, H., & Wilson, J. (2025). Exploring the Effectiveness of Large‐Scale Automated Writing Evaluation Implementation on State Test Performance Using Generalised Boosted Modelling. Journal of Computer Assisted Learning, 41(2). https://doi.org/10.1111/jcal.70009
Zhai, X., Chu, X., Chai, C. S., Jong, M. S., Starčič, A. I., Spector, M., Liu, J., Jing, Y., & Li, Y. (2021). A Review of Artificial Intelligence (AI) in Education From 2010 to 2020. Complexity, 2021(1). https://doi.org/10.1155/2021/8812542
Zhaı, X., & Nehm, R. H. (2023). AI and Formative Assessment: The Train Has Left the Station. Journal of Research in Science Teaching, 60(6), 1390–1398. https://doi.org/10.1002/tea.21885
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Jarudin, Dedi, Edy Tekad Bronto Waluyo (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
