GENERATIVE AI IN HIGHER EDUCATION: REFRAMING TEACHING, LEARNING, ASSESSMENT, AND EDUCATOR READINESS

Abstract

Generative artificial intelligence (GenAI) is reshaping higher education by providing tools for dialogue, drafting, feedback, content generation, coding support, and learning-resource design. Its educational value, however, depends less on the mere availability of a chatbot than on how it is embedded in curriculum, assessment, institutional policy, and human judgement. This article develops a critical integrative review of research and policy literature on educator perspectives, pedagogical opportunities, academic integrity, institutional readiness, and equitable access. The synthesis draws on foundational studies of educators’ experiences, recent systematic reviews of pedagogical practice and assessment, and guidance from international and higher-education organisations. The analysis identifies five connected issues: task-specific usefulness; redesign of assessment around learning processes; educator and student AI literacy; transparency and data protection; and the risk that unequal access or uncritical reliance may widen existing educational differences. The article proposes a Human-Centred Generative AI Integration Framework (H-GAIF) linking AI capabilities to pedagogical design, learning activities, assessment redesign, human oversight, and continuous evaluation. The framework is a conceptual contribution and is not presented as an empirically validated model. The review concludes that institutions should move beyond blanket acceptance or prohibition toward clearly communicated, course-specific rules, supported professional learning, authentic assessment, and evaluation of learning outcomes. Future empirical work should compare disciplinary contexts and measure both short-term performance and durable independent learning.

Keywords

• generative artificial intelligence
• higher education
• educator perspectives
• assessment redesign
• AI literacy
• academic integrity
• human-centred education