A human-centred artificial intelligence and learning analytics framework for equitable personalised higher education enrolment

Abstract

Artificial intelligence (AI) and learning analytics are increasingly deployed within higher education to support admissions decision-making, student success initiatives, and personalised learning. However, most AI-enabled admissions systems remain narrowly focused on efficiency, prediction, and institutional optimisation, with limited attention to learner agency, educational equity, transparency, and human-centred support. This paper proposes a conceptual framework that repositions admissions as the beginning of a personalised learning journey rather than merely a selection or gatekeeping process. The study adopts a conceptual and integrative literature review approach, synthesising scholarship from human-centred artificial intelligence, learning analytics, personalised learning, educational equity, explainable AI, generative AI, responsible AI governance, learner agency theory, and value-sensitive design. To strengthen transparency during revision, the integrative synthesis was supplemented by a targeted ERIC search and bibliographic verification using publisher and authoritative policy repositories. Drawing upon these interdisciplinary perspectives, the paper develops the Human-Centred Personalised Admissions Framework (HCPAF), a governance-oriented model for ethical and transparent AI adoption in higher education admissions and enrolment management. The framework comprises five interconnected dimensions: learner profiling and contextual understanding; explainable AI decision support; equity and inclusion auditing; human oversight and shared decision-making; and personalised transition and success support. Together, these dimensions provide a governance architecture for evaluating how admissions might be connected to personalised educational pathways while making explicit the trade-offs among equity, privacy, explainability, accuracy, human oversight, and learner agency. The framework identifies governance mechanisms required to address algorithmic bias, transparency, privacy, accountability, and learner autonomy. A five-stage Human-Centred AI Ecosystem extending governance across the full student lifecycle — from admissions through to graduate development — provides a further organising contribution. The study advances existing literature by integrating admissions analytics, learning analytics, personalised learning, learner agency, and responsible AI governance within a unified framework. Unlike conventional admissions models that prioritise institutional efficiency, HCPAF positions admissions as the foundation of a personalised learning ecosystem and conceptualises trust as a relational, calibrated, and empirically contingent construct connecting transparency, fairness, learner agency, and AI adoption. The framework contributes to emerging debates concerning ethical and human-centred AI in education and identifies normative alignments with Sustainable Development Goals 4, 10, and 16, while emphasising that any contribution to those goals requires empirical validation. Introduces the Human-Centred Personalised Admissions Framework (HCPAF) for equitable and personalised higher education enrolment. Repositions admissions as the beginning of a personalised learning journey rather than a terminal selection mechanism. Integrates human-centred AI, learning analytics, educational equity, explainable AI, learner agency, and responsible governance. Introduces trust as a central mediating construct linking transparency, fairness, and AI adoption in education. Provides a governance roadmap for ethical, transparent, and accountable AI adoption across the student lifecycle. Identifies normative alignments with Sustainable Development Goals 4 (Quality Education), 10 (Reduced Inequalities), and 16 (Peace, Justice and Strong Institutions), subject to empirical validation. Establishes a multi-dimensional research agenda for human-centred AI in higher education.