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<p>Artificial intelligence (AI) is globally transforming industries, demanding AI literacy for future Science, Technology, Engineering and Mathematics (STEM) professionals. Ghana is adapting its educational and technological landscape to meet this need. This study investigates factors influencing Ghanaian STEM students’ intentions to learn AI, providing insights for educational policy. A descriptive cross-sectional survey of 233 AI-familiar STEM students examined subjective norm, facilitating conditions, self-efficacy, perceived usefulness, perceived social good, AI anxiety, AI literacy, and career relevance. Using partial least squares structural equation modelling and fuzzy-set qualitative comparative analysis (fsQCA), the analysis revealed multiple combinations of individual and contextual factors that drive students’ intentions to learn AI. Results indicate that subjective norm, facilitating conditions, self-efficacy, social good, AI literacy, and career relevance positively influence AI learning intentions. Also, AI anxiety negatively influence AI learning intentions. fsQCA revealed six configurations for high intention. Overall, individual and contextual factors shape Ghanaian STEM students’ AI learning intentions. Fostering AI literacy and career relevance boosts motivation. We recommend integrating AI literacy into STEM curriculum and increasing AI resource access as a national policy priority.</p>

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Modelling STEM students’ intention to learn artificial intelligence (AI) in Ghana: a PLS-SEM and fsQCA approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月27日

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  • Might Kojo Abreh,
  • Francis Arthur,
  • Freda Awonakie Akwetey,
  • Sharon Abam Nortey

摘要

Artificial intelligence (AI) is globally transforming industries, demanding AI literacy for future Science, Technology, Engineering and Mathematics (STEM) professionals. Ghana is adapting its educational and technological landscape to meet this need. This study investigates factors influencing Ghanaian STEM students’ intentions to learn AI, providing insights for educational policy. A descriptive cross-sectional survey of 233 AI-familiar STEM students examined subjective norm, facilitating conditions, self-efficacy, perceived usefulness, perceived social good, AI anxiety, AI literacy, and career relevance. Using partial least squares structural equation modelling and fuzzy-set qualitative comparative analysis (fsQCA), the analysis revealed multiple combinations of individual and contextual factors that drive students’ intentions to learn AI. Results indicate that subjective norm, facilitating conditions, self-efficacy, social good, AI literacy, and career relevance positively influence AI learning intentions. Also, AI anxiety negatively influence AI learning intentions. fsQCA revealed six configurations for high intention. Overall, individual and contextual factors shape Ghanaian STEM students’ AI learning intentions. Fostering AI literacy and career relevance boosts motivation. We recommend integrating AI literacy into STEM curriculum and increasing AI resource access as a national policy priority.