A systematic review of gender differences in students’ use of AI tools for learning in higher education

Abstract

Integrating artificial intelligence (AI) tools in higher education transforms learning environments, yet limited attention has been given to how students of different genders engage with these technologies. This study presents a systematic literature review examining gender differences in the use, adoption, and engagement with AI tools for learning in higher education. Using a structured search across three major academic databases, Web of Science, Scopus, and ERIC, 30 studies published between 2020 and 2025 were identified and systematically analysed. A qualitative thematic synthesis was used to extract key patterns related to usage behaviour, perceptions, learning needs, and barriers. Findings revealed that male students generally reported higher usage frequency, confidence, and behavioural intention to use AI tools across academic contexts. In contrast, female students approached AI tools more cautiously, emphasizing the importance of ethical use, guided support, and meaningful feedback. While male students perceived AI as a practical utility and career asset, female students voiced stronger concerns about privacy, dependency, and the erosion of critical thinking. Learning needs also diverged: males preferred speed and technical mastery, while females prioritised transparency, reliability, and ethical alignment. Cultural and social factors further shaped engagement, with female students being more responsive to peer expectations, emotional implications, and institutional messaging, especially in non-STEM disciplines. Overall, gendered patterns in AI adoption reflect differences in confidence, learning goals, and ethical concerns. This review emphasizes the importance of designing technology with equity and inclusivity in mind, and recommends creating gender-responsive AI-supported learning environments in higher education.

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