Integrating Artificial Intelligence Across Higher Education: A Three-Part Framework for Programme Design, Learner-Centred Delivery, and Assessment Analytics

Abstract

Artificial intelligence (AI) is increasingly being integrated into higher education, yet effective institutional adoption requires more than the deployment of individual tools. This paper presents a discipline-neutral, threepart framework for integrating AI across higher education, connecting programme design, learner-centred delivery, and assessment analytics. The framework positions AI as a decision-support mechanism while retaining academic judgement, disciplinary standards, and faculty oversight. In programme design, AI supports curriculum auditing, alignment of learning outcomes, identification of content gaps, and assessment mapping. In programme delivery, it enables dynamic content, course-bounded AI tutoring, adaptive learning activities, and active engagement. In assessment, AI supports task design, formative feedback, learning analytics, and the identification of learning gaps while maintaining human responsibility for consequential decisions. The framework incorporates principles of outcome-first design, human oversight, evidence-bounded AI use, inclusion, transparency, and data security. It further proposes measurement and governance mechanisms based on learning outcomes, student experience, equity, feedback, and responsible implementation. The paper concludes that meaningful AI integration should be evaluated through demonstrable educational value, stronger alignment, improved feedback, equitable participation, and responsible academic practice rather than tool adoption alone