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<p>The effective realisation of benefits offered by virtual simulation platforms in AI education is contingent upon teachers’ behavioural intentions to adopt such technologies. This study integrates the Technology Acceptance Model (TAM), Social Cognitive Theory (SCT), and Unified Theory of Acceptance and Use of Technology (UTAUT) to examine determinants underlying AI teachers’ behavioural intentions. Analysis of 266 valid responses using Structural Equation Modelling (SEM) demonstrated significant direct effects of perceived usefulness and perceived ease of use on adoption intentions, while platform support (PS), artificial intelligence literacy (AIL), and self-efficacy (SE) exerted indirect influences. Fuzzy-set Qualitative Comparative Analysis (fsQCA) identified five distinct causal configurations sufficient for high behavioural intention. Crucially, AIL and PU formed necessary core conditions across all solutions. These findings offer substantive theoretical contributions and practical guidance for enhancing AI teachers’ platform adoption in K-12 educational contexts.</p>

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Determinants of AI teachers’ behavioural intention to use virtual simulation platforms: an integrated SEM and fsQCA study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月27日

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  • Ke Qingchao,
  • Bao Tingting,
  • Zhang Siqi

摘要

The effective realisation of benefits offered by virtual simulation platforms in AI education is contingent upon teachers’ behavioural intentions to adopt such technologies. This study integrates the Technology Acceptance Model (TAM), Social Cognitive Theory (SCT), and Unified Theory of Acceptance and Use of Technology (UTAUT) to examine determinants underlying AI teachers’ behavioural intentions. Analysis of 266 valid responses using Structural Equation Modelling (SEM) demonstrated significant direct effects of perceived usefulness and perceived ease of use on adoption intentions, while platform support (PS), artificial intelligence literacy (AIL), and self-efficacy (SE) exerted indirect influences. Fuzzy-set Qualitative Comparative Analysis (fsQCA) identified five distinct causal configurations sufficient for high behavioural intention. Crucially, AIL and PU formed necessary core conditions across all solutions. These findings offer substantive theoretical contributions and practical guidance for enhancing AI teachers’ platform adoption in K-12 educational contexts.