Navigating generative AI in higher education

A framework for educator decision-making

Abstract

The rapid emergence of generative AI (GenAI) in higher education offers transformative opportunities but also significant pedagogical and ethical challenges. Unlike coordinated institutional responses to COVID-19, GenAI adoption has been slower and more fragmented, often relying on individual educators navigating an ambiguous institutional landscape without clear guidance. This preliminary qualitative study introduces a five-stage decision-making framework, developed from a thematic literature review and interviews with early GenAI adopters across all six faculties at a large research-intensive university. The framework foregrounds reflective practice, ethical considerations, educator identity, and AI literacy as key factors shaping context-sensitive decisions about GenAI integration. Preliminary findings highlight the critical influence of institutional culture and leadership on educator practice. They also underscore the need for explicit institutional ethics frameworks that address data privacy, algorithmic bias, and pedagogical integrity. The model offers a practical, staged approach to support thoughtful decision-making, with broader applicability for informing institutional policy and guiding faculty-level strategy in this evolving context.