AI-Assisted Feedback in Online Learning: Students’ Experiences, Preferences, and Perceived Benefits
DOI:
https://doi.org/10.58524/aidie.v1i2.85Keywords:
AI-assisted feedback, online learning, student perceptions, feedback preferencesAbstract
This study examined how students experience and interpret AI-assisted feedback in online learning, addressing the growing need to understand its cognitive, emotional, and developmental implications. Using a convergent mixed-methods design, data were collected from 212 undergraduate students through a structured questionnaire including Likert-scale items and open-ended responses. Quantitative analyses provided descriptive and inferential results on students’ experiences, preferences, and perceived benefits, while qualitative thematic analysis identified patterns related to clarity, explanatory value, confidence building, and concerns about accuracy. Integrated findings showed strong convergence across strands, indicating that students generally valued AI feedback for its immediacy and usefulness, yet remained cautious about its limitations. The study concludes that AI-assisted feedback can support learning processes when designed to provide explanatory depth and align with instructional expectations. These insights contribute to research on AI-enhanced education by clarifying how learners engage with automated feedback and by highlighting design considerations for future implementation.
Downloads
References
Amigud, A., & Pell, D. J. (2025). Responsible and Ethical Use of AI in Education: Are We Forcing a Square Peg into a Round Hole? World, 6(2), 81. https://doi.org/10.3390/world6020081
Brailas, A. (2025). Artificial Intelligence in Qualitative Research: Beyond Outsourcing Data Analysis to the Machine. Psychology International, 7(3), 78. https://doi.org/10.3390/psycholint7030078
Burner, T., Lindvig, Y., & Wærness, J. I. (2025). “We Should Not Be Like a Dinosaur”—Using AI Technologies to Provide Formative Feedback to Students. Education Sciences, 15(1), 58. https://doi.org/10.3390/educsci15010058
Deepshikha, D. (2025). A comprehensive review of AI-powered grading and tailored feedback in universities. In Discover Artificial Intelligence (Vol. 5, Issue 1). Springer Nature. https://doi.org/10.1007/s44163-025-00517-0
Dunne, G. (2025). Rethinking ‘Thinking Skills’ in 21st-Century Education: Combining Conceptual Clarity with a Novel 4E Cognitive Framework. Studies in Philosophy and Education, 44(5), 493–511. https://doi.org/10.1007/s11217-025-09997-0
Ebbes, R., Zee, M., Jansen, B. R. J., Koomen, H. M. Y., & Schuitema, J. A. (2026). Promoting self-regulated learning during the covid-mandated remote learning period: Insights from interviews with primary school teachers. Teaching and Teacher Education, 169, 105287. https://doi.org/10.1016/j.tate.2025.105287
Gianni, A. M., Nikolakis, N., & Antoniadis, N. (2025). An LLM based learning framework for adaptive feedback mechanisms in gamified XR. Computers & Education: X Reality, 7, 100116. https://doi.org/10.1016/j.cexr.2025.100116
Grenier, S., Gagné, M., & O’Neill, T. (2024). Self‐determination theory and its implications for team motivation. Applied Psychology, 73(4), 1833–1865. https://doi.org/10.1111/apps.12526
He, G. (2025). Predicting learner autonomy through AI-supported self-regulated learning: A social cognitive theory approach. Learning and Motivation, 92, 102195. https://doi.org/10.1016/j.lmot.2025.102195
Jiang, L., Lv, M., Cheng, M., Chen, X., & Peng, C. (2024). Factors affecting deep learning of EFL students in higher vocational colleges under small private online courses‐based settings: A grounded theory approach. Journal of Computer Assisted Learning, 40(6), 3098–3110. https://doi.org/10.1111/jcal.13060
Khalil, M., Wong, J., Wasson, B., & Paas, F. (2024). Adaptive support for self‐regulated learning in digital learning environments. British Journal of Educational Technology, 55(4), 1281–1289. https://doi.org/10.1111/bjet.13479
Li, L., Zhang, X., Zou, B., & Yang, Q. (2025). AI partner or peer partner? Exploring AI-mediated interaction in EFL pronunciation from a socio-cultural perspective. Learning, Culture and Social Interaction, 55, 100958. https://doi.org/10.1016/j.lcsi.2025.100958
Lin, H., & Chen, Q. (2024). Artificial intelligence (AI) -integrated educational applications and college students’ creativity and academic emotions: students and teachers’ perceptions and attitudes. BMC Psychology, 12(1). https://doi.org/10.1186/s40359-024-01979-0
Mac Fadden, I., García-Alonso, E.-M., & López Meneses, E. (2024). Science Mapping of AI as an Educational Tool Exploring Digital Inequalities: A Sociological Perspective. Multimodal Technologies and Interaction, 8(12), 106. https://doi.org/10.3390/mti8120106
Naser, M. Z. (2025). A decision architecture for epistemic prioritization: Machine learning at the intersection of technology and society. Technology in Society, 83, 103039. https://doi.org/10.1016/j.techsoc.2025.103039
Negura, L. (2025). Simulated Sense‐Making or Social Knowledge? Artificial Intelligence and the Boundaries of Representation. Journal for the Theory of Social Behaviour, 55(3). https://doi.org/10.1111/jtsb.70012
Popov, V., Gabelica, C., Tomaka, S., & Danciu, T. (2025). Making the invisible visible: how multisource feedback and guided facilitation affect team reflection. Cognition, Technology and Work. https://doi.org/10.1007/s10111-025-00835-4
Rodríguez-Ardura, I., Meseguer-Artola, A., Lladós-Masllorens, J., & de Luna, I. R. (2025). Evidence of the role of presence in enhancing engagement in virtual learning environments via psychological ownership and flow: a dual PLS-neural network approach. International Journal of Educational Technology in Higher Education, 22(1). https://doi.org/10.1186/s41239-025-00531-3
Salloum, S. A., Alomari, K. M., Alfaisal, A. M., Aljanada, R. A., & Basiouni, A. (2025). Emotion recognition for enhanced learning: using AI to detect students’ emotions and adjust teaching methods. Smart Learning Environments, 12(1). https://doi.org/10.1186/s40561-025-00374-5
Sari, E., & Han, T. (2024). The impact of automated writing evaluation on English as a foreign language learners’ writing self‐efficacy, self‐regulation, anxiety, and performance. Journal of Computer Assisted Learning, 40(5), 2065–2080. https://doi.org/10.1111/jcal.13004
Sjödin, D., Parida, V., Palmié, M., & Wincent, J. (2021). How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops. Journal of Business Research, 134, 574–587. https://doi.org/10.1016/j.jbusres.2021.05.009
Song, C., Shin, S.-Y., & Shin, K.-S. (2024). Implementing the Dynamic Feedback-Driven Learning Optimization Framework: A Machine Learning Approach to Personalize Educational Pathways. Applied Sciences, 14(2), 916. https://doi.org/10.3390/app14020916
Xiao, F., Zou, E. W., Lin, J., Li, Z., & Yang, D. (2025). Parent‐led vs. AI ‐guided dialogic reading: Evidence from a randomized controlled trial in children’s e‐book context. British Journal of Educational Technology, 56(5), 1784–1813. https://doi.org/10.1111/bjet.13615
Yang, C., Wei, M., & Liu, Q. (2025). Intersections between cognitive‐emotion regulation, critical thinking and academic resilience with academic motivation and autonomy in EFL learners: Contributions of AI ‐mediated learning environments. British Educational Research Journal. https://doi.org/10.1002/berj.4140
Yang, H., & Rui, Y. (2025). Transforming EFL students’ engagement: How AI-enhanced environments bridge emotional health challenges like depression and anxiety. Acta Psychologica, 257, 105104. https://doi.org/10.1016/j.actpsy.2025.105104
Yu, M., Liu, Z., Long, T., Li, D., Deng, L., Kong, X., & Sun, J. (2025). Exploring cognitive presence patterns in GenAI-integrated six-hat thinking technique scaffolded discussion: an epistemic network analysis. International Journal of Educational Technology in Higher Education, 22(1). https://doi.org/10.1186/s41239-025-00545-x
Zhang, T., & Strbac, G. (2025). Novel Artificial Intelligence Applications in Energy: A Systematic Review. Energies, 18(14), 3747. https://doi.org/10.3390/en18143747
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Adyt Anugrah, Yani Suryani

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.