Successful AI-driven personalized learning depends as much on institutional conditions as it does on technology. Drawing on a sociotechnical systems perspective, this viewpoint explores the curriculum, governance and human capabilities needed for responsible and effective AI implementation in higher education.
The article draws on findings from two complementary empirical studies on students’ motivation and staff perspectives, alongside broader research on personalized learning and intelligent tutoring, to examine AI-enabled personalized learning through a sociotechnical systems perspective.
AI can build learner profiles and adapt content, pacing and feedback at scale, but its effectiveness depends on data quality and stops at the classroom door: socioeconomic status, family circumstances, and other off-platform pressures stay invisible to AI even though they are established drivers of attrition. Confidence, motivation and belonging stay central throughout, shaped by curriculum and educators as much as by any platform.
As a conceptual synthesis rather than an empirical study, the conclusions require validation across different institutional contexts.
Universities should assess curriculum fit, strengthen governance, equitable access and AI literacy, and keep educators central when determining where and how AI-enabled personalization should be scaled.
This viewpoint reframes AI-enabled personalized learning as a discipline-sensitive institutional readiness problem, highlighting curriculum fit, AI literacy, algorithmic bias, equitable access, and the continuing importance of human care and judgment.
