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<p>The current study probes the mechanism behind learners' trust development in an AI-driven education system by integrating psychological ABI (Ability, Benevolence, and Integrity) and design-based FEAS (Fairness, Explainability, Auditability, and Safety) perspectives. The research work utilizes two structured vignettes and survey data from 242 MBA students from the Tie-II business Schools. A mixed-methods approach is adopted, combining quantitative modelling and qualitative reflection to improve the study's generalisability. The measurement reliability was strong (Cronbach’s α = 0.88), and the integrated ABI-FEAS model accounted for 51% of the variance in behavioural trust. The thematic insights from the qualitative phase underscore the need to facilitate transparent feedback, protect participants' privacy, and explain their explainability to sustain trust. Our study advances the literature by showing the role of psychological and ethical design factors in bolstering user confidence in AI-mediated learning. Practical recommendations are proposed for educators, administrators, and AI designers to focus on mechanisms that enhance transparency, accountability, and learner engagement in digital education environments.</p>

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Psychological and ethical drivers of trust in AI-enhanced learning among MBA students in tier II business schools

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月9日

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  • Murale Venugopalan,
  • Preetha R,
  • Gyanendra Singh Sisodia,
  • Aqila Rafiuddin

摘要

The current study probes the mechanism behind learners' trust development in an AI-driven education system by integrating psychological ABI (Ability, Benevolence, and Integrity) and design-based FEAS (Fairness, Explainability, Auditability, and Safety) perspectives. The research work utilizes two structured vignettes and survey data from 242 MBA students from the Tie-II business Schools. A mixed-methods approach is adopted, combining quantitative modelling and qualitative reflection to improve the study's generalisability. The measurement reliability was strong (Cronbach’s α = 0.88), and the integrated ABI-FEAS model accounted for 51% of the variance in behavioural trust. The thematic insights from the qualitative phase underscore the need to facilitate transparent feedback, protect participants' privacy, and explain their explainability to sustain trust. Our study advances the literature by showing the role of psychological and ethical design factors in bolstering user confidence in AI-mediated learning. Practical recommendations are proposed for educators, administrators, and AI designers to focus on mechanisms that enhance transparency, accountability, and learner engagement in digital education environments.