Abstract
Feedback is a critical component of formative assessment yet remains underutilized in higher education STEM courses due to large class sizes and instructor workload. Emerging artificial intelligence (AI) technologies are reshaping STEM education by enabling scalable, personalized feedback and promoting active learning. This study explores the instructional potential of AI-supported feedback in addressing these challenges, with a particular focus on self-explanations. It reports on two iterations of a classroom response system in an undergraduate computer science (CS) course across two semesters. The first iteration provided static feedback based on instructor-prepared sample responses, while the second incorporated AI-generated adaptive feedback powered by natural language processing. Using a comparative case study design, the authors investigated differences in student self-efficacy, engagement, and perceptions of system usability. Both implementations sustained student engagement and participation in CS courses, demonstrating the system’s effectiveness in fostering active learning. Findings highlight the potential of AI-driven feedback to enhance scalability and interactivity, deepen conceptual understanding, and reduce instructor workload. These results underscore the promise of AI-driven feedback systems for enhancing interactivity and learning in STEM courses, particularly in CS contexts with growing enrollments. Future work should explore question frequency, question formats, instructor’s perspectives and pedagogy with feedback tools, and improve AI accuracy to reduce over-reliance.
- Acar, E., Yigit, F., & Deiri, Y. (2025). A focused review of artificial intelligence in education: Evolution and challenges. Journal of Interdisciplinary Research in Artificial Intelligence and Society, 1(1), Article 3. https://doi.org/10.20897/jirais/17640
- Adamakis, M., & Rachiotis, T. (2025). Artificial intelligence in higher education: A state-of-the-art overview of pedagogical integrity, artificial intelligence literacy, and policy integration. Encyclopedia, 5(4), Article 180. https://doi.org/10.3390/encyclopedia5040180
- Adekola, S. (2025). Integrating AI into STEM education: Challenges and opportunities. In P. A. Okebukola (Ed.), Handbook of artificial intelligence and quality higher education: AI and curriculum development for the future (Vol. 2, pp. 139–144). Sterling Publishers.
- Baartman, L. K., & Quinlan, K. M. (2024). Assessment and feedback in higher education reimagined: Using programmatic assessment to transform higher education. Perspectives: Policy and Practice in Higher Education, 28(2), 57–67. https://doi.org/10.1080/13603108.2023.2283118
- Beauchamp, G., & Kennewell, S. (2010). Interactivity in the classroom and its impact on learning. Computers & Education, 54(3), 759–766. https://doi.org/10.1016/j.compedu.2009.09.033
- Bisra, K., Liu, Q., Nesbit, J. C., Salimi, F., & Winne, P. H. (2018). Inducing self-explanation: A meta-analysis. Educational Psychology Review, 30(3), 703–725. https://doi.org/10.1007/s10648-018-9434-x
- Blasco-Arcas, L., Buil, I., Hernández-Ortega, B., & Sese, F. J. (2013). Using clickers in class: The role of interactivity, active collaborative learning and engagement in learning performance. Computers & Education, 62, 102–110. https://doi.org/10.1016/j.compedu.2012.10.019
- Braun, V., & Clarke, V. (2012). Thematic analysis. In H. Cooper, P. M. Camic, D. L. Long, A. T. Panter, D. Rindskopf, & K. J. Sher (Eds.), APA handbook of research methods in psychology (Vol. 2, pp. 57–71). American Psychological Association. https://doi.org/10.1037/13620-004
- Brooke, J. (1996). SUS: A 'quick and dirty' usability scale. In P. W. Jordan, B. Thomas, I. L. McClelland, & B. Weerdmeester (Eds.), Usability evaluation in industry (pp. 189–194). Taylor & Francis.
- 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), Article 58. https://doi.org/10.3390/ educsci15010058
- Campbell, J., & Mayer, R. E. (2009). Questioning as an instructional method: Does it affect learning from lectures? Applied Cognitive Psychology, 23(6), 747–759. https://doi.org/10.1002/acp.1513
- Carpenter, D., Min, W., Lee, S., Ozogul, G., Zheng, X., & Lester, J. (2024, June). Assessing student explanations with large language models using fine-tuning and few-shot learning. In Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024) (pp. 403–413). https://aclanthology.org/ 2024.bea-1.33.pdf
- Chan, C. K. Y., & Hu, W. (2023). Students' voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), Article 43. https://doi.org/10.1186/s41239-023-00411-8
- Chen, P., Yang, D., Zhao, J., Yang, S., & Lavonen, J. (2025). The effects of self-explanation on secondary school students' computational thinking and programming behaviour. Journal of Computer Assisted Learning, 41(5), Article e70116. https://doi.org/10.1111/jcal.70116
- Chen, Q. (2025). Students' perceptions of AI-powered feedback in English writing: Benefits and challenges in higher education. International Journal of Changes in Education. https://doi.org/10.47852/bonviewIJCE52025580
- Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145–182. https://doi.org/ 10.1207/s15516709cog1302_1
- Chiu, J. L., & Chi, M. T. H. (2014). Supporting self-explanation in the classroom. In V. A. Benassi, C. E. Overson, & C. M. Hakala (Eds.), Applying science of learning in education: Infusing psychological science into the curriculum (pp. 91–103). American Psychological Association.
- Diaz, P., Hrastinski, S., & Norström, P. (2024). How using a response system in blended synchronous seminars encourages online and onsite student participation. Education and Information Technologies, 29, 19889–19911. https://doi.org/10.1007/s10639-024-12665-4
- Elme, L., Jørgensen, M. L., Dandanell, G., Mottelson, A., & Makransky, G. (2022). Immersive virtual reality in STEM: Is IVR an effective learning medium and does adding self-explanation after a lesson improve learning outcomes? Educational Technology Research and Development, 70, 1601–1626. https://doi.org/10.1007/s11423-022-10139-3
- Er, E., Akçapınar, G., Bayazıt, A., Noroozi, O., & Banihashem, S. K. (2025). Assessing student perceptions and use of instructor versus AI-generated feedback. British Journal of Educational Technology, 56(3), 1074–1091. https://doi.org/10.1111/bjet.13558
- Fastowski, A., Prenkaj, B., & Kasneci, G. (2025). From confidence to collapse in LLM factual robustness. Conference on Empirical Methods in Natural Language Processing. https://www.semanticscholar.org/reader/ 5e51fe92bb1ebd2e89b06afb9f776c65b8c18771
- Firat, M. (2023). What ChatGPT means for universities: Perceptions of scholars and students. Journal of Applied Learning and Teaching, 6(1), 57–63. https://doi.org/10.37074/jalt.2023.6.1.22
- Fonseca, B. A., & Chi, M. T. H. (2011). Instruction based on self-explanation. In R. E. Mayer & P. A. Alexander (Eds.), Handbook of research on learning and instruction (pp. 310–335). Routledge.
- Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415. https://doi.org/10.1073/pnas.1319030111
- Gonsar, N., Patrick, L., & Cotner, S. (2021). Graduate- and undergraduate-student perceptions of and preferences for teaching practices in STEM classrooms. Disciplinary and Interdisciplinary Science Education Research, 3, Article 6. https://doi.org/10.1186/s43031-021-00035-w
- González-Cacho, T., & Abbas, A. (2022). Impact of interactivity and active collaborative learning on students' critical thinking in higher education. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 17(3), 254–261. https://doi.org/10.1109/RITA.2022.3191286
- Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
- Henderson, M., Ajjawi, R., Boud, D., & Molloy, E. (2019a). Identifying feedback that has impact. In M. Henderson, R. Ajjawi, D. Boud, & E. Molloy (Eds.), The impact of feedback in higher education (pp. 15–34). Palgrave Macmillan. https://doi.org/10.1007/978-3-030-25112-3_2
- Henderson, M., Ryan, T., & Phillips, M. (2019b). The challenges of feedback in higher education. Assessment & Evaluation in Higher Education, 44(8), 1237–1252. https://doi.org/10.1080/02602938.2019.1599815
- Hooda, M., Rana, C., Dahiya, O., Rizwan, A., & Hossain, M. S. (2022). Artificial intelligence for assessment and feedback to enhance student success in higher education. Mathematical Problems in Engineering, 2022, Article 5215722. https://doi.org/10.1155/2022/5215722
- Hunsu, N. J., Adesope, O., & Bayly, D. J. (2016). A meta-analysis of the effects of audience response systems (clicker-based technologies) on cognition and affect. Computers & Education, 94, 102–119. https://doi.org/10.1016/j.compedu.2015.11.013
- Jammeh, A. L. J., Karegeya, C., & Ladage, S. (2025). Clicker-integrated instruction and conventional instruction: The comparative evaluations of students' performances in chemistry. Education and Information Technologies, 30, 5331–5351. https://doi.org/10.1007/s10639-024-12992-6
- Jelks, S. M., & Crain, A. M. (2020). Sticking with STEM: Understanding STEM career persistence among STEM bachelor's degree holders. The Journal of Higher Education, 91(5), 805–831. https://doi.org/10.1080/00221546. 2019.1700477
- Joseph, O. B., & Uzondu, N. C. (2024). Integrating AI and machine learning in STEM education: Challenges and opportunities. Computer Science & IT Research Journal, 5(8), 1732–1750.
- Kaliisa, R., Misiejuk, K., López-Pernas, S., & Saqr, M. (2026). How does artificial intelligence compare to human feedback? A meta-analysis of performance, feedback perception, and learning dispositions. Educational Psychology, 46(1), 80–111. https://doi.org/10.1080/01443410.2025.2553639
- Khairuddin, Z., Shahabani, N. S., Ahmad, S. N., Ahmad, A. R., & Zamri, N. A. (2024). Students' perceptions on the artificial intelligence (AI) tools as academic support. Malaysian Journal of Social Sciences and Humanities, 9(11), Article e003087. https://doi.org/10.47405/mjssh.v9i11.3087
- López-Pernas, S., Misiejuk, K., Oliveira, E., & Saqr, M. (2025). The dynamics of the self-regulation process in student-AI interactions. In Proceedings of the 25th Koli Calling International Conference on Computing Education Research (pp. 1–12). Association for Computing Machinery. https://doi.org/10.1145/3769994.3770043
- Marwan, S., Gao, G., Fisk, S., Price, T. W., & Barnes, T. (2020). Adaptive immediate feedback can improve novice programming engagement and intention to persist in computer science. In Proceedings of the 2020 ACM Conference on International Computing Education Research (pp. 194–203). Association for Computing Machinery. https://doi.org/10.1145/3372782.3406264
- Mayer, R. E., Stull, A., DeLeeuw, K., Almeroth, K., Bimber, B., Chun, D., Bulger, M., Campbell, J., Knight, A., & Zhang, H. (2009). Clickers in college classrooms: Fostering learning with questioning methods in large lecture classes. Contemporary Educational Psychology, 34(1), 51–57. https://doi.org/10.1016/j.cedpsych. 2008.04.002
- Mirhosseini, S., Henley, A. Z., & Parnin, C. (2023, March). What is your biggest pain point? An investigation of CS instructor obstacles, workarounds, and desires. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education (Vol. 1, pp. 291–297). Association for Computing Machinery. https://dl.acm.org/ doi/10.1145/3545945.3569816
- Muangprathub, J., Boonjing, V., & Chamnongthai, K. (2020). Learning recommendation with formal concept analysis for intelligent tutoring system. Heliyon, 6(10), Article e05227. https://doi.org/10.1016/j.heliyon. 2020.e05227
- Nakamoto, R., Flanagan, B., Dai, Y., Yamauchi, T., Takami, K., & Ogata, H. (2023). Enhancing self-explanation learning through a real-time feedback system: An empirical evaluation study. Sustainability, 15(21), Article 15577.https://doi.org/10.3390/su152115577
- Nakamoto, R., Flanagan, B., Dai, Y., Yamauchi, T., Takami, K., & Ogata, H. (2024). Integrating self-explanation and operational data for impasse detection in mathematical learning. Research and Practice in Technology Enhanced Learning, 20, Article 19. https://doi.org/10.58459/rptel.2025.20019
- O'Brien, H. L., Cairns, P., & Hall, M. (2018). A practical approach to measuring user engagement with the refined user engagement scale (UES) and new UES short form. International Journal of Human-Computer Studies, 112, 28–39. https://doi.org/10.1016/j.ijhcs.2018.01.004
- Ortiz Moreno, H., Godoy-Rangel, C., & Ahumada-Lazo, R. (2025). From lectures to active learning: A case study on problem-based learning in the material balances course. International Journal of Mechanical Engineering Education, 1–22. https://doi.org/10.1177/03064190251356618
- Ozogul, G., Zheng, X., Min, W., Lee, S., Carpenter, D., & Lester, J. (2025). Towards AI-enhanced classroom response system eliciting self-explanations in computer science courses. The Journal of Applied Instructional Design, 14(2), 335–341. https://doi.org/10.59668/2222.20820
- Park, L. E., O'Brien, C., Italiano, A., Ward, D. E., & Panlilio, Z. (2023). "That's a great question!" Instructors' positive responses to students' questions improve STEM-related outcomes. Self and Identity, 22(6), 849–895. https://doi.org/10.1080/15298868.2023.2207836
- Park, L. E., Ward, D. E., Moore-Russo, D., Rickard, B., Vessels, V., & Hundley, J. (2024). Positive feedback as a lever to boost students' STEM outcomes. Personality and Social Psychology Bulletin, 52(1), 176–197. https://doi.org/10.1177/01461672241265954
- Pitt, E., & Quinlan, K. M. (2022). Impacts of higher education assessment and feedback policy and practice on students: A review of the literature 2016–2021. Advance HE. https://www.advance-he.ac.uk/knowledge-hub/impacts-higher-education-assessment-and-feedback-policy-and-practice-students-review
- Prasad, P., & Sane, A. (2024). A self-regulated learning framework using generative AI and its application in CS educational intervention design. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education (Vol. 1, pp. 1070–1076). Association for Computing Machinery. https://doi.org/10.1145/3626252.3630828
- Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26, 582–599. https://doi.org/10.1007/s40593-016-0110-3
- Roy, M., & Chi, M. T. H. (2005). The self-explanation principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 271–286). Cambridge University Press.
- Sandoval-Medina, C., Arévalo-Mercado, C. A., Muñoz-Andrade, E. L., & Muñoz-Arteaga, J. (2024). Self-explanation effect of cognitive load theory in teaching basic programming. Journal of Information Systems Education, 35(3), 303–312. https://doi.org/10.62273/GMIV1698
- Seco, D., Grösser, S., & Pedrosa, A. M. (2025). Use of generative artificial intelligence tools in higher education environments.Multidisciplinary Journal for Education, Social and Technological Sciences, 12(1), 156–175.https://doi.org/10.4995/muse.2025.23623
- Shapiro, A. M., Sims-Knight, J., O'Rielly, G. V., Capaldo, P., Pedlow, T., Gordon, L., & Monteiro, K. (2017). Clickers can promote fact retention but impede conceptual understanding: The effect of the interaction between clicker use and pedagogy on learning.Computers & Education, 111, 44–59. https://doi.org/10.1016/ j.compedu.2017.03.017
- Shishakly, R. (2025). Understanding AI in higher education: Gendered and intersectional students' experience with ChatGPT use.European Journal of STEM Education, 10(1), Article 36. https://doi.org/10.20897/ ejsteme/17646
- Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153–189.https://doi.org/10.3102/0034654307313795
- Slimi, Z., Benayoune, A., & Alemu, A. E. (2025). Students' perceptions of artificial intelligence integration in higher education. European Journal of Educational Research, 14(2), 471–484. https://doi.org/10.12973/eu-jer.14.2.471
- Tamang, L. J., Alshaikh, Z., Ait Khayi, N., & Rus, V. (2020). The effects of open self-explanation prompting during source code comprehension. In Proceedings of the Thirty-Third International Florida Artificial Intelligence Research Society Conference (pp. 451–456). AAAI Press.
- Tang, X., Wong, S., Huynh, M., He, Z., Yang, Y., & Chen, Y. (2024). SPHERE: Scaling personalized feedback in programming classrooms with structured review of LLM outputs. arXiv. https://doi.org/10.48550/ arXiv.2410.16513
- Theobald, E. J., Hill, M. J., Tran, E., Agrawal, S., Arroyo, E. N., Behling, S., Chambwe, N., Cintrón, D. L., Cooper, J. D., Dunster, G., Grummer, J. A., Hennessey, K., Hsiao, J., Iranon, N., Jones, L., Jordt, H., Keller, M., Lacey, M. E., Littlefield, C. E., ... Freeman, S. (2020). Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math. Proceedings of the National Academy of Sciences, 117(12), 6476–6483. https://doi.org/10.1073/pnas.1916903117
- Uğraş, H., Uğraş, M., Papadakis, S., & Kalogiannakis, M. (2024). ChatGPT-supported education in primary schools: The potential of ChatGPT for sustainable practices. Sustainability, 16(22), Article 9855. https://doi.org/10.3390/su16229855
- UNESCO. (2019). Beijing consensus on artificial intelligence and education. https://unesdoc.unesco.org/ark:/ 48223/pf0000368303
- Unfried, A., Faber, M., Stanhope, D. S., & Wiebe, E. (2015). The development and validation of a measure of student attitudes toward science, technology, engineering, and math (S-STEM). Journal of Psychoeducational Assessment, 33(7), 622–639.https://doi.org/10.1177/0734282915571160
- Vihavainen, A., Miller, C. S., & Settle, A. (2015). Benefits of self-explanation in introductory programming. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (pp. 284–289). Association for Computing Machinery.https://doi.org/10.1145/2676723.2677260
- Villegas-Ch, W., Buenano-Fernandez, D., Navarro, A. M., & Mera-Navarrete, A. (2025). Adaptive intelligent tutoring systems for STEM education: Analysis of the learning impact and effectiveness of personalized feedback. Smart Learning Environments, 12, Article 41. https://doi.org/10.1186/s40561-025-00389-y
- Yin, R. K. (2009). Case study research and applications: Design and methods (4th ed.). SAGE Publications.
- Zacharis, G., & Papadakis, S. (2025). Can AI grade like a human? Validity, reliability, and fairness in university coursework assessment. Educational Process: International Journal, 19, Article e2025591. https://doi.org/10. 22521/edupij.2025.19.591
- Zhao, X. (2023). The impact of live polling quizzes on student engagement and performance in computer science lectures. arXiv.https://doi.org/10.48550/arXiv.2309.12335
APA 7th edition
In-text citation: (Ozogul et al., 2026)
Reference: Ozogul, G., Zheng, X., Min, W., Lee, S., Esiason, J., Carpenter, D., & Lester, J. (2026). Student experiences with static versus adaptive AI feedback on self-explanations in computer science courses.
European Journal of STEM Education, 11(1), Article 40.
https://doi.org/10.20897/ejsteme/18919
AMA 10th edition
In-text citation: (1), (2), (3), etc.
Reference: Ozogul G, Zheng X, Min W, et al. Student experiences with static versus adaptive AI feedback on self-explanations in computer science courses.
European Journal of STEM Education. 2026;11(1), 40.
https://doi.org/10.20897/ejsteme/18919
Chicago
In-text citation: (Ozogul et al., 2026)
Reference: Ozogul, Gamze, Xiaoying Zheng, Wookhee Min, Seung Lee, Jordan Esiason, Dan Carpenter, and James Lester. "Student experiences with static versus adaptive AI feedback on self-explanations in computer science courses".
European Journal of STEM Education 2026 11 no. 1 (2026): 40.
https://doi.org/10.20897/ejsteme/18919
Harvard
In-text citation: (Ozogul et al., 2026)
Reference: Ozogul, G., Zheng, X., Min, W., Lee, S., Esiason, J., Carpenter, D., and Lester, J. (2026). Student experiences with static versus adaptive AI feedback on self-explanations in computer science courses.
European Journal of STEM Education, 11(1), 40.
https://doi.org/10.20897/ejsteme/18919
MLA
In-text citation: (Ozogul et al., 2026)
Reference: Ozogul, Gamze et al. "Student experiences with static versus adaptive AI feedback on self-explanations in computer science courses".
European Journal of STEM Education, vol. 11, no. 1, 2026, 40.
https://doi.org/10.20897/ejsteme/18919
Vancouver
In-text citation: (1), (2), (3), etc.
Reference: Ozogul G, Zheng X, Min W, Lee S, Esiason J, Carpenter D, et al. Student experiences with static versus adaptive AI feedback on self-explanations in computer science courses. European Journal of STEM Education. 2026;11(1):40.
https://doi.org/10.20897/ejsteme/18919