Human-Centric Explanainable AI as a Catalyst for Responsible AI Adoption in Higher Education

Fine Masimba; Bester Chimbo; Kudakwashe Maguraushe

doi:10.30871/jaic.v10i5.13992

Authors

Fine Masimba University of South Africa
Bester Chimbo University of South Africa
Kudakwashe Maguraushe University of South Africa

DOI:

https://doi.org/10.30871/jaic.v10i5.13992

Keywords:

Human Centric AI, Explainable AI, Responsible AI adoption, Higher Education

Abstract

As Artificial Intelligence (AI) becomes increasingly integrated into academic workflows, the limited explainability of advanced AI models poses significant ethical and operational challenges, particularly in the Global South.This study proposes and validates an Extended Human-Centric Explainable AI (H-XAI) Adoption Framework within the context of Zimbabwean Higher Education. Utilizing a quantitative approach (N = 372 university students), regression analysis was employed to examine the relationships amongst five critical H-XAI constructs which are perceived interpretability, algorithm transparency, ethical alignment, AI literacy and human oversight together with traditional adoption drivers that include perceived usefulness, perceived ease of use, attitude and behavioral intention. Results demonstrate that the model explains 62% of the variance in Behavioral Intention (R2 = .62). Findings reveal that ethical alignment (β = .48, p < 0.001) and human oversight (β = .44, p < 0.001) are the strongest predictors of responsible AI adoption, while perceived interpretability (β=0.42, p < 0.001) significantly shapes Attitude. The implications for institutional governance and curriculum design are discussed, providing a roadmap for sustainable AI integration in resource-constrained educational environments.

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References

[1] S. Khosravi et al., “Explainable artificial intelligence in education,” Computers & Education: Artificial Intelligence, vol. 3, Art. no. 100074, 2022. doi: 10.1016/j.caeai.2022.100074.

[2] R. Conijn et al., “The effects of explanations in automated essay scoring systems on student trust and motivation,” Journal of Learning Analytics, vol. 10, no. 1, pp. 54–73, 2023. doi: 10.18608/jla.2023.7801.

[3] D. Delen et al., “Predicting and mitigating freshmen student attrition: A local-explainable machine learning framework,” Information Systems Frontiers, vol. 25, pp. 123–145, 2023. doi: 10.1007/s10796-023-10397-3.

[4] T. K. Chiu, “Exploring user awareness and perceived usefulness of generative AI in higher education: The moderating role of trust,” Education and Information Technologies, 2025. doi: 10.1007/s10639-025-13612-7.

[5] M. Airaj, “Ethical artificial intelligence for teaching-learning in higher education,” Education and Information Technologies, vol. 29, pp. 1–15, 2024. doi: 10.1007/s10639-024-12545-x.

[6] C. Tubella et al., “How to teach responsible AI in higher education: Challenges and opportunities,” Ethics and Information Technology, vol. 25, no. 1, 2023. doi: 10.1007/s10676-023-09733-7.

[7] W. Holmes, M. Bialik, and C. Fadel, Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign, 2019.

[8] R. Luckin, W. Holmes, M. Griffiths, and L. B. Forcier, Intelligence Unleashed: An Argument for AI in Education. Pearson, 2016.

[9] L. Floridi et al., “AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations,” Minds and Machines, vol. 28, no. 4, pp. 689–707, 2018. doi: 10.1007/s11023-018-9482-5.

[10] European Commission, Ethics Guidelines for Trustworthy AI. Publications Office of the European Union, 2021.

[11] R. S. Baker and P. S. Inventado, “Educational data mining and learning analytics,” in The Cambridge Handbook of the Learning Sciences, 2nd ed., R. K. Sawyer, Ed. Cambridge University Press, 2014, pp. 253–272.

[12] S. B. Shum and R. Luckin, “Learning analytics and AI: Politics, pedagogy and practices,” British Journal of Educational Technology, vol. 50, no. 6, pp. 2785–2793, 2019. doi: 10.1111/bjet.12880.

[13] O. Zawacki-Richter et al., “Systematic review of research on artificial intelligence applications in higher education,” International Journal of Educational Technology in Higher Education, vol. 16, Art. no. 39, 2019. doi: 10.1186/s41239-019-0171-0.

[14] I. Iatrellis et al., “KONX: A dynamic approach for explainable AI in higher education,” in Proceedings of the Panhellenic Conference on Informatics (PCI), 2023. doi: 10.1145/3631908.3631925.

[15] L. Mukora et al., “Harnessing generative AI for pedagogical innovation in higher education: Evidence from Zimbabwean stakeholders,” in Proceedings of the ICTAS Conference, 2025. doi: 10.1109/ictas64866.2025.11155729.

[16] F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Quarterly, vol. 13, no. 3, pp. 319–340, 1989. doi: 10.2307/249008.

[17] N. P. Maluleke, “AI adoption in African higher education: A systematic review of benefits and ethical implications,” Interdisciplinary Journal of Education Research, vol. 7, no. 2, 2025. doi: 10.38140/ijer-2025.vol7.2.05.

[18] F. Masimba, K. Maguraushe, and B. Chimbo, “Ethical adoption of AI-powered EdTech in higher education: Human–AI interaction through an ethically extended UTAUT2 model,” The Indonesian Journal of Computer Science, vol. 15, no. 1, 2026. doi: 10.33022/ijcs.v15i1.5079.