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<p>The incorporation of artificial intelligence (AI) into education is becoming more important over time, although faculty viewpoints on this integration are not well recognized. To analyze educators' attitudes towards AI tools in Bangladesh, this research built a modified model that included components from the technology acceptance model (TAM), unified theory of acceptance and use of technology (UTAUT), and DeLone models. The study's purpose was to assess faculty attitudes towards AI adoption by analysing system quality information quality, facilitating condition, performance expectancy, effort expectancy, and behavioural intention. An online survey was administered to 543 faculty members from private universities in Bangladesh using a cluster and multi-stage sampling technique. The results were analysed using structural equation modelling and complemented by in-depth interviews with 20 educators. The statistically significant model ( <i>p</i> &lt; 0.05) explained 37.4% of the variation in educator's attitudes towards AI. The findings indicated that performance expectancy, effort expectancy, and information quality all had a favourable influence on attitudes, whereas behavioural intention had a negligible negative effect. The research, which uses the DeLone model in a novel way, concludes that information quality had a negligible positive influence on the attitude of educators. In contrast, system quality was not a significant determinant. This study highlights the relevance of knowing faculty attitudes towards AI and the necessity to concentrate on performance and effort expectations to drive successful AI integration methods in education.</p>

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Unveiling the role of educators attitudes & intention toward artificial intelligence in teaching: A multi-dimensional analysis

  • 期刊论文

  • 发表日期：

    2025年1月13日

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  • Fairuz Anjum Binte Habib

摘要

The incorporation of artificial intelligence (AI) into education is becoming more important over time, although faculty viewpoints on this integration are not well recognized. To analyze educators' attitudes towards AI tools in Bangladesh, this research built a modified model that included components from the technology acceptance model (TAM), unified theory of acceptance and use of technology (UTAUT), and DeLone models. The study's purpose was to assess faculty attitudes towards AI adoption by analysing system quality information quality, facilitating condition, performance expectancy, effort expectancy, and behavioural intention. An online survey was administered to 543 faculty members from private universities in Bangladesh using a cluster and multi-stage sampling technique. The results were analysed using structural equation modelling and complemented by in-depth interviews with 20 educators. The statistically significant model ( p < 0.05) explained 37.4% of the variation in educator's attitudes towards AI. The findings indicated that performance expectancy, effort expectancy, and information quality all had a favourable influence on attitudes, whereas behavioural intention had a negligible negative effect. The research, which uses the DeLone model in a novel way, concludes that information quality had a negligible positive influence on the attitude of educators. In contrast, system quality was not a significant determinant. This study highlights the relevance of knowing faculty attitudes towards AI and the necessity to concentrate on performance and effort expectations to drive successful AI integration methods in education.