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<p>The integration of artificial intelligence (AI) in higher education necessitates understanding how AI literacy fosters student creativity. This study examines the mediating roles of AI proactive integration and innovation self-efficacy, and the moderating effects of gender and disciplinary background. Using multi-group structural equation modeling with data from 5,615 Chinese undergraduates, we found that AI literacy enhances creativity both directly and through a sequential pathway: AI literacy → AI proactive integration → innovation self-efficacy → creativity. Multi-group analysis revealed significant gender differences—male students exhibited stronger indirect effects along the “AI proactive integration → innovation self-efficacy → creativity” chain—while disciplinary background (humanities/social sciences vs. natural sciences) showed no significant moderating effect. These findings validate a “technology-behavior-psychology-innovation” framework and offer practical insights for developing inclusive AI education strategies.</p>

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The impact of artificial intelligence literacy on college students’ creativity: a sequential mediation and multi-group analysis

  • 期刊论文

  • 发表日期：

    2026年6月16日

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  • Lichuan Yu,
  • Xiang Ding,
  • Yadong Ding,
  • Jing Li

摘要

The integration of artificial intelligence (AI) in higher education necessitates understanding how AI literacy fosters student creativity. This study examines the mediating roles of AI proactive integration and innovation self-efficacy, and the moderating effects of gender and disciplinary background. Using multi-group structural equation modeling with data from 5,615 Chinese undergraduates, we found that AI literacy enhances creativity both directly and through a sequential pathway: AI literacy → AI proactive integration → innovation self-efficacy → creativity. Multi-group analysis revealed significant gender differences—male students exhibited stronger indirect effects along the “AI proactive integration → innovation self-efficacy → creativity” chain—while disciplinary background (humanities/social sciences vs. natural sciences) showed no significant moderating effect. These findings validate a “technology-behavior-psychology-innovation” framework and offer practical insights for developing inclusive AI education strategies.