Bridging student autonomy and AI integration: an exploratory study toward a student-AI-centered learning framework

Purpose This paper explores expert perspectives on the integration of artificial intelligence (AI) in higher education and proposes a preliminary Student-AI-Centered conceptual framework. While AI is increasingly deployed in teaching and learning contexts, the field currently lacks conceptual frameworks that intentionally balance student agency with AI-enabled support. This study addresses that gap by examining how educators and institutions can promote responsible, ethical and student-driven AI adoption. Design/methodology/approach A qualitative research design was employed, drawing on semi-structured interviews with educational technology experts selected through purposive sampling. Each participant had a minimum of five years of experience in AI-related practice within higher education. Data were analysed using Braun and Clarke’s (2006) six-phase thematic analysis framework, with trustworthiness strengthened through member checking, triangulation, peer debriefing and an audit trail. Findings Thematic analysis of expert interviews produced two overarching themes: (1) Potentials of AI in Higher Education, encompassing three sub-themes, personalised learning, inclusive education and enhanced learning participation and (2) Challenges of AI Integration, encompassing two sub-themes, unverified information and ethical compliance. Drawing on these themes, the study proposes a seven-component Student-AI-Centered conceptual framework that integrates AI into curricula while emphasising ethical awareness, diverse assessment modalities and ongoing educator support. Research limitations/implications The exploratory nature of the study and the small, purposive sample limit the generalisability of the findings. Future research should validate the proposed conceptual framework through larger, cross-institutional studies and longitudinal designs that assess its applicability across diverse educational settings and cultural contexts. Practical implications The Student-AI-Centered conceptual framework offers educators and institutions practical guidance for integrating AI tools responsibly. Pedagogical integrity refers to maintaining the primacy of genuine learning outcomes by ensuring AI supplements rather than supplant critical reasoning, independent inquiry and authentic assessment. Promoting critical thinking means equipping students to interrogate, verify and evaluate AI-generated content rather than accepting it uncritically. Together, these principles, alongside fostering ethical awareness, aim to cultivate responsible, reflective AI users in higher education. Originality/value This paper contributes a novel conceptual framework that positions AI as a complementary agent within student-centered learning, rather than as a replacement for educators or a source of uncritical dependency. The Student-AI-Centered approach bridges the gap between teacher-centered, student and AI-centered paradigms.