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<p>This study assesses AI literacy among college students and examines its interplay with motivation and self-efficacy, proposing a multidimensional framework (AI literacy conceptual model, AICM) that integrates AI literacy (understanding, applying, evaluating, and creating AI) and ethical considerations alongside affective factors. A 29-item self-report questionnaire, validated with data from 1,034 college students, measured AI literacy components, motivational commitment, and self-efficacy. Structural equation modeling (SEM) revealed that motivational commitment positively associated with self-efficacy and AI literacy, with self-efficacy serving as a significant mediator. Multi-group analyses highlighted this mediation effect was stronger for male students, underscoring gendered dynamics in efficacy-driven learning. Key barriers included limited AI experience, technical challenges, and insufficient curricular support. The AICM equips educators to benchmark AI literacy, design gender-responsive interventions, and address systemic access gaps, fostering AI literacy development. Generalizability may be limited by self-report data. Future work should validate the AICM cross-culturally, examine longitudinal attitude impacts, and investigate socio-cultural drivers of efficacy disparities through mixed-method approaches.</p>

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Assessing AI literacy in college students: the mediating role of self-efficacy in motivational commitment pathways

  • 期刊论文

  • 发表日期：

    2025年8月23日

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  • Jing Kong,
  • Jialiang Liu,
  • Gaowei Chen,
  • Wengang Shang

摘要

This study assesses AI literacy among college students and examines its interplay with motivation and self-efficacy, proposing a multidimensional framework (AI literacy conceptual model, AICM) that integrates AI literacy (understanding, applying, evaluating, and creating AI) and ethical considerations alongside affective factors. A 29-item self-report questionnaire, validated with data from 1,034 college students, measured AI literacy components, motivational commitment, and self-efficacy. Structural equation modeling (SEM) revealed that motivational commitment positively associated with self-efficacy and AI literacy, with self-efficacy serving as a significant mediator. Multi-group analyses highlighted this mediation effect was stronger for male students, underscoring gendered dynamics in efficacy-driven learning. Key barriers included limited AI experience, technical challenges, and insufficient curricular support. The AICM equips educators to benchmark AI literacy, design gender-responsive interventions, and address systemic access gaps, fostering AI literacy development. Generalizability may be limited by self-report data. Future work should validate the AICM cross-culturally, examine longitudinal attitude impacts, and investigate socio-cultural drivers of efficacy disparities through mixed-method approaches.