Data-Driven AI-Enabled Assessment in Project Management Education: Learner Traces, Data Use, and Educational Implications

Artificial intelligence (AI) is increasingly used in project management education (PME), especially in digital learning environments where learner activity can be captured as process data. However, limited attention has been given to how AI-enabled systems use these data to produce evidence for assessment in PME. This systematic mapping review addresses this gap on how AI-generated or AI-mediated learner data are collected, analysed, and used for assessment-related purposes, and by identifying the educational implications reported in the literature. Following PRISMA 2020 guidelines, searches were conducted in Web of Science, Scopus, ERIC, and IEEE Xplore for studies published between 2016 and 2025. Fourteen studies met the inclusion criteria. The review identified six recurring types of learner process data. Across the included studies, these data were analysed using natural language processing, machine learning, rule-based techniques, and learning analytics, and were used to generate feedback, indicators, recommendations, and scoring support. Reported benefits mainly relate to stronger formative feedback, better visibility of learning processes, etc. The review also identifies important gaps in validity, fairness, transparency, and longer-term educational effects. AI in PME is used not simply to automate assessment, but to broaden how learner activity is captured, interpreted, and used in pedagogical and assessment practice.