Abstract
Abstract
Recent advances in AI-enabled educational tools have generated rich streams of learner trace data, offering new opportunities to support learning and assessment in project management education. Drawing on a structured review informed by PRISMA guidelines, this developmental paper examines how data-driven AI systems capture, analyse, and mobilise learner traces—such as interaction logs, revision histories, and human–AI dialogues—and how these practices shape educational design and learning outcomes. Rather than treating AI analytics as a purely technical solution, the review adopts a human-centred, design-mediated perspective, highlighting how pedagogical intentions, assessment practices, and institutional contexts influence the educational value of learner data. Synthesising empirical and conceptual studies, the paper identifies recurring patterns in data use, highlights emerging tensions around transparency, fairness, and learner agency, and outlines implications for learning-oriented assessment design in project management education.