Fonolla, Sara PortellFassi, Yasmina ElGaspar, Augusta D.Correia, LuísPinto, Joana Carneiro2026-05-052026-05-052026-02-22642ef6bf-3933-47d7-9687-e857b1b77e73http://hdl.handle.net/10400.14/57656Artificial intelligence (AI) is increasingly integrated into career guidance and organisational decision systems, yet empirical evidence on applications designed to support women’s career development remains limited. Following PRISMA-ScR and a preregistered protocol, we searched 7 databases (plus backward and forward citation searching) and synthesised 13 empirical studies published between 2018 and 2025. Using inductive thematic analysis, we identified three functional domains: (a) bias mitigation and representation (e.g., auditing gendered language and platform-level disparities), (b) skills development and empowerment (e.g., AI-supported learning and writing interventions), and (c) career pathways and retention (e.g., matching and attrition-risk modelling). The evidence base was concentrated in system-facing applications that detect or shape inequities within recruitment, evaluation, and exposure systems; fewer studies evaluated individual-facing developmental support, and sustained career outcomes were rarely measured. Formal theory use was limited, with only a small minority of studies explicitly drawing on established frameworks; reporting on ethics, transparency, and governance was inconsistent. We suggest that research prioritises longitudinal and theory-informed evaluations, including intersectionality-informed analyses, and assess downstream impacts on women’s career trajectories alongside robust governance and accountability practices.engArtificial intelligenceCareer developmentGender equityWomen in STEMSDG 5SDG 8Bias mitigationGovernancepreprint10.31234/osf.io/ef7q5_v1