Cognitively-Informed Explainable AI for Education: Enhancing Student Engagement and Transparent Assessment Through Human-Centered Modeling

Bulus Bali

doi:10.58230/josse.v2i3.351

Authors

Bulus Bali Adamawa State University https://orcid.org/0000-0001-7249-9347

DOI:

https://doi.org/10.58230/josse.v2i3.351

Keywords:

AI in Education, Adaptive Learning Systems, Cognitive Modeling, Explainable Artificial Intelligence (XAI), Human-Centered AI, Student Engagement, Transparent Assessment

Abstract

This study proposes a novel, cognitively-informed Explainable Artificial Intelligence (XAI) framework aimed at enhancing student engagement and promoting transparent, equitable assessment in digital education. By embedding cognitive science principles into interpretable AI models, the framework aligns with diverse cognitive styles and learning trajectories, offering a human-centered approach to educational data analytics. Utilizing secondary analysis of large-scale datasets, specifically the Open University Learning Analytics Dataset (OULAD), the model combines machine learning with cognitive modeling, incorporating attention mechanisms and interpretable neural networks for real-time feedback and decision transparency. Embedded within an adaptive learning environment, the system achieved 85.6% alignment with historical engagement labels and demonstrated a 12% improvement in early detection of at-risk learners. Beyond predictive accuracy, the model offers actionable insights by revealing decision pathways, and empowering educators to implement fairer grading practices and targeted interventions. This work contributes a scalable, ethical, and domain-independent AI solution that can be adapted across STEM and non-STEM curricula. It lays the groundwork for next-generation intelligent tutoring systems, learning management platforms and education policy frameworks centered on explainability and fairness. Future research will expand the model to integrate multimodal inputs (EEG, eye tracking) and investigate long-term learning retention, reinforcing the role of cognitively informed XAI in advancing inclusive, data-driven education systems.

Downloads

Download data is not yet available.

References

Altukhi, Z. M., & Pradhan, S. (2025). Systematic Literature Review: Explainable AI Definitions and Challenges in Education. arXiv preprint arXiv:2504.02910. https://doi.org/10.48550/arXiv.2504.02910

Alwarthan, S., Aslam, N., & Khan, I. U. (2022). An explainable model for identifying at-risk student at higher education. IEEE Access, 10, 107649-107668. https://doi.org/10.1109/ACCESS.2022.3211070

Bali, B., Garba, E. J., Ahmadu, A. S., Takwate, K. T., & Malgwi, Y. M. (2024). Analysis of emerging trends in artificial intelligence for education in Nigeria. Discover Artificial Intelligence, 4(1), 110. https://doi.org/10.1007/s44163-024-00163-y

Bobrytska, V. I., Krasylnykova, H. V., Beseda, N. А., Krasylnykov, S. R., & Skyrda, T. S. (2024). Artificial intelligence (AI) in Ukrainian higher education: A comprehensive study of stakeholder attitudes, expectations and concerns. International Journal of Learning, Teaching and Educational Research, 23(1), 400-426. https://doi.org/10.26803/ijlter.23.1.20

Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(1), 38. https://doi.org/10.1186/s41239-023-00408-3

Choksi, K., Chen, H., Joshi, K., Jade, S., Nirjon, S., & Lin, S. (2024). SensEmo: Enabling affective learning through real-time emotion recognition with smartwatches. Proceedings of the 2024 IEEE International Conference on Mobile Ad-Hoc and Smart Systems. https://doi.org/10.1109/MASS62177.2024.00066

Contrino, M. F., Reyes-Millán, M., Vázquez-Villegas, P., & Membrillo-Hernández, J. (2024). Using an adaptive learning tool to improve student performance and satisfaction in online and face-to-face education for a more personalized approach. Smart Learning Environments, 11(1), 6. https://doi.org/10.1186/s40561-024-00261-2

Dahri, N. A., Dahri, F. H., & Javed, M. J. (2025). Decoding ChatGPT's impact on student satisfaction and performance: A multimodal machine learning and explainable AI approach. Complex Engineering Systems, 5, Article 7. https://doi.org/10.20517/ces.2025.07

Davies, S., Seitamaa-Hakkarainen, P., & Hakkarainen, K. (2024). Knowledge creation through maker practices and the role of teacher and peer support in collaborative invention projects. International Journal of Computer-Supported Collaborative Learning, 19(3), 283–310. https://doi.org/10.1007/s11412-024-09427-2

Du Plooy, E., Casteleijn, D., & Franzsen, D. (2024). Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement. Heliyon, 10(21), e39630. https://doi.org/10.1016/j.heliyon.2024.e39630

Eden, C. A., Adeleye, O. O., & Adeniyi, I. S. (2024). A review of AI-driven pedagogical strategies for equitable access to science education. Magna Scientia Advanced Research and Reviews, 10(2), 044-054. https://doi.org/10.30574/msarr.2024.10.2.0043

Fabia, J. N., Napoles, V., Goh, J. E., & Borbon Jr, M. (2025). How AI Tools are Accepted and Utilized in Academia: A Mixed Methods Study. Journal of Social and Scientific Education, 2(1), 24-41. https://doi.org/10.58230/josse.v2i1.295

Fernández, A. (2024). Opacity, Machine Learning and Explainable AI. In Ethics of Artificial Intelligence (pp. 39-58). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-48135-2_3

González-Nucamendi, J. A., Noguez, J., Neri, L., & García-Castelán, R. M. G. (2023). Predictive analytics study to determine undergraduate students at risk of dropout. Frontiers in Education, 8. https://doi.org/10.3389/feduc.2023.1110334

Halkiopoulos, C., & Gkintoni, E. (2024). Leveraging AI in e-learning: Personalized learning and adaptive assessment through cognitive neuropsychology-A systematic analysis. Electronics, 13(18), 3762. https://doi.org/10.3390/electronics13183762

Holmes, W., Bialik, M., & Fadel, C. (2021). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.

Hoq, M., Brusilovsky, P., & Akram, B. (2024). Explaining explainability: Early performance prediction with student programming pattern profiling. Journal of Educational Data Mining, 16(2), 115–148. https://doi.org/10.5281/zenodo.14246435

Idrizi, E. (2024, Mei). Exploring the role of explainable artificial intelligence (XAI) in adaptive learning systems. In Proceedings of the Cognitive Models and Artificial Intelligence Conference (pp. 100-105). https://doi.org/10.1145/3660853.3660877

Kanyiri, A. (2025). School Climate and Teacher Attrition Intentions: A Case Study of Junior High Schools in Bole District, Ghana. Journal of Social and Scientific Education, 2(1), 51-72. https://doi.org/10.58230/josse.v2i1.280

Khong, M. L., & Tanner, J. A. (2024). Surface and deep learning: A blended learning approach in preclinical years of medical school. BMC Medical Education, 24, Article 1029. https://doi.org/10.1186/s12909-024-05963-5

Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y. S., Kay, J., ... & Gašević, D. (2022). Explainable artificial intelligence in education. Computers and Education: Artificial Intelligence, 3, 100074. https://doi.org/10.1016/j.caeai.2022.100074

Kuzilek, J., Hlosta, M., & Zdrahal, Z. (2017). Open University Learning Analytics Dataset. Scientific Data, 4(1), 1-8. https://doi.org/10.1038/sdata.2017.171

Lebiere, C., Pirolli, P., Johnson, M., Martin, M., & Morrison, D. (2024). Cognitive Models for Machine Theory of Mind. Topics in Cognitive Science. https://doi.org/10.1111/tops.12773

Li, T., Yan, L., Iqbal, S., Srivastava, N., Singh, S., Raković, M., ... & Li, X. (2025). Analytics of self-regulated learning strategies and scaffolding: Associations with learning performance. Computers and Education: Artificial Intelligence, 100410. https://doi.org/10.1016/j.caeai.2025.100410

Liao, C. H., & Wu, J. Y. (2023). Learning analytics on video-viewing engagement in a flipped statistics course: Relating external video-viewing patterns to internal motivational dynamics and performance. Computers & Education, 197, 104754. https://doi.org/10.1016/j.compedu.2023.104754

Longo, L., Brcic, M., Cabitza, F., Choi, J., Confalonieri, R., Del Ser, J., ... & Stumpf, S. (2024). Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions. Information Fusion, 106, 102301. https://doi.org/10.1016/j.inffus.2024.102301

Lu, M., Farhat, J. H., & Beck Dallaghan, G. L. (2021). Enhanced learning and retention of medical knowledge using the mobile flash card application Anki. Medical Science Educator, 31(6), 1975–1981. https://doi.org/10.1007/s40670-021-01386-9

Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022). Explainable artificial intelligence: a comprehensive review. Artificial Intelligence Review, 1-66. https://doi.org/10.1007/s10462-021-10088-y

Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B. P. T. (2023). Ethical Principles for Artificial Intelligence In Education. Education and Information Technologies, 28(4), 4221-4241. https://doi.org/10.1007/s10639-022-11316-w

Nnadi, L. C., Watanobe, Y., Rahman, M. M., & John-Otumu, A. M. (2024). Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models. Applied Sciences, 14(12), 5141. https://doi.org/10.3390/app14125141

Ouyang, F., & Zhang, L. (2024). AI-driven Learning Analytics Applications and Tools in Computer-Supported Collaborative Learning: A Systematic Review. Educational Research Review, 44, 100616. https://doi.org/10.1016/j.edurev.2024.100616

Pölzl-Stefanec, E., & Feierabend, S. (2025). Conditions: Online Professional Development in Early Childhood Education. Journal of Social and Scientific Education, 2(1), 1-11. https://doi.org/10.58230/josse.v2i1.260

Psathas, G., Chatzidaki, T. K., & Demetriadis, S. N. (2023). Predictive modeling of student dropout in MOOCs and self-regulated learning. Computers, 12(10), 194. https://doi.org/10.3390/computers12100194

Sánchez-Prieto, J. C., Gamazo, A., Cruz-Benito, J., Therón, R., & García-Peñalvo, F. J. (2020). AI-Driven Assessment of Students: Current Uses and Research Trends. In International Conference on Human-Computer Interaction (pp. 292-302). Springer International Publishing. https://doi.org/10.1007/978-3-030-50513-4_22

Sanusi, I. T., Agbo, F. J., Dada, O. A., Yunusa, A. A., Aruleba, K. D., Obaido, G., ... & Oyelere, S. S. (2024). Stakeholders’ Insights on Artificial Intelligence Education: Perspectives of Teachers, Students, and Policymakers. Computers and Education Open, 7, 100212. https://doi.org/10.1016/j.caeo.2024.100212

Saqr, M., & López-Pernas, S. (2024). Why explainable AI may not be enough: Predictions and mispredictions in decision making in education. Smart Learning Environments, 11, Article 52. https://doi.org/10.1186/s40561-024-00343-4

Sharma, A., & Naidu, V. R. (2020). A Study on emerging trends to enhance learning experience in higher education institutions. In INTED2020 Proceedings (pp. 7391-7396). IATED. https://doi.org/10.21125/inted.2020.1974

Tan, H., & Li, X. (2025). Dropout in online education: A longitudinal multilevel analysis of elementary students' extracurricular English course engagement in China. Behavioral Sciences, 15(4), 483. https://doi.org/10.3390/bs15040483

Vincent-Lancrin, S., & Van der Vlies, R. (2020). Trustworthy Artificial Intelligence (AI) in Education: Promises and Challenges. OECD Education Working Papers, (218), 0_1-17. https://doi.org/10.1787/a6c90fa9-en

Wisniewski, B., Zierer, K., & Hattie, J. (2020). The power of feedback revisited: A meta-analysis of educational feedback research. Frontiers in Psychology, 10, 3087. https://doi.org/10.3389/fpsyg.2019.03087