Machine learning for post-diploma educational and career guidance: a scoping review in AI-driven decision support systems - Manganello, Flavio; Rasca, Elisa; Villa, Alberto; Maddalena, Andrea; Boccuzzi, Giannangelo (Frontiers in Education, 2025)
The increasing complexity of career decision-making, shaped by rapid technological advancements and evolving job markets, highlights the need for more responsive and data-informed post-diploma guidance. Machine learning (ML), a core component of artificial intelligence, is gaining attention for its potential to support personalized educational and career decisions by analyzing academic records, individual preferences, and labor market data. Despite growing interest, research in this field remains fragmented and methodologically diverse. This scoping review maps the application of ML in post-diploma guidance by examining the types of models used, data sources, reported outcomes, and ethical considerations related to fairness, privacy, and transparency. A systematic search of Scopus and Web of Science was conducted, with the final search completed on December 31, 2023. Twenty-one studies met the inclusion criteria, primarily employing supervised or mixed-method ML techniques to develop recommendation systems or predictive models. While several contributions report positive technical performance, evidence on educational effectiveness and user impact is limited. Ethical concerns such as bias, opacity, and limited explainability are acknowledged but not consistently addressed. The findings point to the need for more rigorous empirical research, greater methodological transparency, and the integration of educational perspectives to ensure that ML-based systems for career guidance are used responsibly and with clear added value.