AI POLICIES IN HIGHER EDUCATION: PEDAGOGICAL, GOVERNANCE, AND OPERATIONAL DIMENSIONS

Dini Noor Arini

This qualitative study investigates how higher education institutions conceptualize, govern, and operationalize policies for the ethical use of generative artificial intelligence in teaching and learning. Guided by Chan’s AI Ecological Education Policy Framework, the study examines three interconnected policy dimensions: pedagogical, governance, and operational, to understand how institutional expectations for artificial intelligence use are defined, implemented, and experienced by key actors in university contexts.Data were collected through semi-structured interviews with higher education stakeholders (including faculty, preservice teachers, and university policy leaders) and analysis of relevant institutional documents. Using thematic analysis, findings are organized by the three framework dimensions to provide a descriptive account of how artificial intelligence policy is discussed and enacted across academic settings. In the pedagogical dimension, participants emphasized the need for ethical and balanced integration, preparation for skills and workplace readiness, and the redesign of assessment practices while supporting students’ moral development. In the governance dimension, participants highlighted the importance of clear policy language and ethical oversight, strengthened privacy and data protection practices, and mechanisms to address equity and accountability. In the operational dimension, participants identified training and support as institutional foundations, inconsistent monitoring and implementation across units, and persistent communication and collaboration challenges that limit policy effectiveness. Across all dimensions, virtue ethics emerged as a cross-cutting lens, foregrounding values that participants viewed as essential for responsible artificial intelligence use, including honesty, fairness, care, and accountability. This study contributes a framework-aligned, practice-oriented synthesis of institutional needs and tensions surrounding artificial intelligence policy in higher education. The findings inform actionable recommendations for designing clearer, more implementable, and ethically grounded policies that better align classroom practice, governance expectations, and institutional capacity.

AI POLICIES IN HIGHER EDUCATION: PEDAGOGICAL, GOVERNANCE, AND OPERATIONAL DIMENSIONS

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