What is ethical: AIHED driving humans or Human-Driven AIHED? A conceptual framework enabling the ‘ethos’ of AI-driven higher education

Artificial Intelligence (AI) is transforming higher education (HE) through personalised learning, predictive analytics, and intelligent automation. However, concerns over algorithmic bias, privacy, and inequity necessitate governance models that prioritise Human Intelligence (HI). This study introduces the Human-Driven AI in Higher Education (HD-AIHED) framework, which embeds stakeholder-driven HI, particularly in decision-making and feedback across all five AI lifecycle phases: adoption (multi-stakeholder governance and anticipatory ethical foresight), design (co-creation with educators, students, and administrators), deployment (context-sensitive implementation with transparent stakeholder input), evaluation (participatory audits and continuous, inclusive feedback loops), and exploration (scaling informed by regional insight and open communication of outcomes). Using qualitative meta-synthesis, the study synthesises global policy frameworks (e.g. UNESCO, GDPR), institutional case studies, and adoption theories. The HD-AIHED framework maps solutions to global real-time setbacks – such as digital inequities, fragmented governance, and algorithmic opacity by integrating HI into AI lifecycle. Insights emphasise that AI’s educational value depends on sustained HI, rooted in inclusive decision-making and dynamic, real-time feedback from all stakeholders. The framework incorporates SWOC analysis and recommends establishing AI Ethical Review Boards to institutionalise accountability, transparency, and trust. HD-AIHED re-conceptualises AI not as autonomous but as a collaborative tool, ensuring ethical, equitable, and scalable transformation in HE globally.