Generative artificial intelligence (GenAI) is transforming higher education, reshaping assessment, curriculum, pedagogical relationships, and the broader dynamics of teaching and learning. While research on AI in education (AIEd) is expanding, it remains fragmented and often dominated by tool-centric use cases and techno-determinist narratives. In this Editorial, we argue for a new phase of meaningful AI research in higher education, research that is pedagogically grounded, ethically aware, and theoretically informed. We critique the limitations of traditional reflective practice, which, like large language models, can mirror existing assumptions without producing genuinely new insights. Instead, we call for a reframing of the Scholarship of Teaching and Learning (SoTL) that acknowledges complexity, examines power, and embraces the entangled nature of human and non-human collaboration. Meaningful research must engage with the long-term consequences of AI for teaching, learning, and academic work and not just its immediate functionality. Drawing on interdisciplinary perspectives across pedagogy, trust, ethics, and institutional change, we outline an agenda for AI research that centres on criticality, sustainability, and inclusion. We contend that meaningful research must go beyond short-term efficiency gains to examine long-term impact, support educator and student agency, and address the structural values shaping AI’s integration in higher education. This agenda is urgent: the foundations laid now will determine how we as educators lead the transformation of academic work, student experience, and the future of knowledge in an AI enhanced era.
Fitzgerald et al. (Mon,) studied this question.
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