Managing Teaching and Learning Innovation in Higher Education: Reassessing Learner Capabilities in the Era of AI

Liu, Rebecca and Rodriguez Garcia, Beatriz and Mouzas, Stefanos (2026) Managing Teaching and Learning Innovation in Higher Education: Reassessing Learner Capabilities in the Era of AI. In: EIASM-IPDMC 2026 :. UNSPECIFIED, p. 1.

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Abstract

(Research Design paper) TITLE Managing Teaching and Learning Innovation in Higher Education: Reassessing Learner Capabilities in the Era of AI EXTENDED ABSTRACT This study investigates how Artificial Intelligence (AI) is reshaping learners’ capabilities in higher education, with particular attention to implications for teaching and learning innovation. Grounded in capability theory and contemporary research on AI in education, the study examines the dual role of AI as both an enabler and potential constraint on learners’ autonomy, engagement, and epistemic agency (Melo-López et al., 2025; Mulaudzi and Hamilton, 2025). As generative and assistive AI tools become increasingly embedded in higher education, understanding how they expand or limit learners’ real opportunities to engage meaningfully in their learning is essential for responsible pedagogical and innovation management. Drawing on the work of Sen (1982, 2009) and Nussbaum (2005, 2011), the study begins from the premise that educational inclusion depends on ensuring that all learners—regardless of prior preparation, background, or access needs—have substantive opportunities to develop and exercise their capabilities. AI presents significant potential to enhance these opportunities. Research shows that AI-enabled personalisation can provide adaptive learning pathways, timely feedback, and differentiated support that strengthen learner autonomy, motivation, and engagement (Layachi and Pitchford, 2025). Moreover, AI-driven accessibility tools, translation interfaces, and content-generation assistants can support learners with diverse cognitive, linguistic, and physical needs, contributing to more inclusive and learner-centred experiences. At the same time, reliance on AI may introduce constraints on capability development. Evidence from cognitive science and educational research (Risko & Gilbert, 2016; Bjork, 1994; Williamson & Kizilcec, 2023) suggests that AI can encourage cognitive offloading, reduce desirable difficulties, bypass self-regulated learning processes, and diminish epistemic agency. In practice, this can affect learner autonomy, depth of engagement, and critical thinking. Additional challenges include inequitable access to AI tools, algorithmic bias, privacy and data concerns, and ambiguity around the boundary between legitimate support and academic misconduct. This research presents early-stage conceptual work and pilot activities designed to explore these tensions in higher education contexts. Pilot activities include exploratory surveys, learner reflections, and small-scale classroom implementations of AI-supported learning tools. Preliminary findings indicate diverse outcomes: some learners reported enhanced clarity, confidence, and personalised learning experiences, while others described overreliance on AI, reduced effort, and uncertainty about ethical boundaries. These findings underscore the need for pedagogical frameworks that balance AI-enabled personalisation with the preservation of cognitive challenge and learner agency. Building on these insights, the study proposes a conceptual framework for managing teaching and learning innovation in higher education. The framework emphasises three interrelated domains: (1) recognising and balancing AI’s expansive and constraining effects on learner capabilities, (2) designing inclusive and personalised learning strategies that maintain critical thinking and self-regulation, and (3) cultivating AI literacy, reflective judgment, and responsible use practices. By foregrounding capability development in the context of AI-driven change, this framework provides a pathway for educators and institutions to innovate teaching and learning while safeguarding learner autonomy and engagement. Overall, this study offers timely insights into how AI is reshaping learner capabilities in higher education. It provides both conceptual and practical guidance for managing teaching and learning innovation responsibly, inclusively, and ethically in an era of rapid technological disruption. REFERENCES Bjork, R. A. (1994). Memory and metamemory considerations in the training of human beings. In J. Metcalfe & A. Shimamura (Eds.), Metacognition: Knowing about knowing (pp. 185–205). MIT Press. Layachi, A., & Pitchford, N. J. (2025). Formative evaluation of an interactive personalised learning technology to inform equitable access and inclusive education for children with special educational needs and disabilities. Technology, Knowledge and Learning, 30(3), 1395-1419. Melo-López, V. A., Basantes-Andrade, A., Gudiño-Mejía, C. B., & Hernández-Martínez, E. (2025). The Impact of Artificial Intelligence on Inclusive Education: A Systematic Review. Education Sciences, 15(5), 539. Mulaudzi, T., & Hamilton, M. (2025). AI accessibility tools and inclusion in higher education: Emerging evidence. [Publisher/Journal details to be added]. Nussbaum, M. C. (2005). Human capabilities, female human beings, and political liberalism. Philosophy & Public Affairs, 33(4), 339–355. https://doi.org/10.1111/j.1088-4963.2005.00035.x Nussbaum, M. C. (2011). Creating capabilities: The human development approach. Harvard University Press. Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002 Sen, A. (1982). Choice, welfare, and measurement. Harvard University Press. Sen, A. (2009). The idea of justice. Harvard University Press. Williamson, B., & Kizilcec, R. (2023). AI in education: Agency, ethics, and learner empowerment. [Publisher/Journal details to be added].