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This paper addresses the limited adoption of AI chatbots in higher education by extending the Technology Acceptance Model (TAM) with Human-Centered AI (HCAI) principles, specifically explainability, transparency, trust, and perceived control. The authors developed the HCAI-TAM framework and validated it through an empirical study with 300 respondents using regression analysis. The results demonstrate that incorporating HCAI principles significantly improves the model's explanatory power, accounting for 65% of the variance in behavioral intention and 55% in usage behavior related to AI chatbot adoption.
Concerns about trust and transparency are holding back AI chatbot adoption in higher education, but a new framework integrating human-centered AI principles can significantly boost user acceptance.
Artificial intelligence (AI) is transforming education by enhancing assessments, personalizing learning, and improving administrative efficiency. However, the adoption of AI-powered chatbots in higher education remains limited, primarily due to concerns about trust, transparency, explainability, perceived control, and alignment with human values. While the Technology Acceptance Model (TAM) is commonly used to explain technology adoption, it does not fully address the challenges posed by AI systems, which require human-centered safeguards. To address this gap, this study extends TAM by incorporating Human-Centered AI (HCAI) principles—explainability, transparency, trust, and perceived control—resulting in the HCAI-TAM framework. An empirical study with 300 respondents was conducted using a structured English questionnaire, and regression analysis was applied to assess the relationships among variables. The model explained 65% (R² = 0.65) of the variance in behavioral intention and 55% (R² = 0.55) in usage behavior. The findings highlight that integrating HCAI principles into TAM enhances user adoption of AI chatbots in higher education, contributing both theoretically and practically.