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An Empirical Study on Trust, Ethical Concerns, and Bias in AI-Powered Educational Chatbots


Authors : Sudha Valan; Dr. Vikas Kumar

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/5n874cbk

Scribd : https://tinyurl.com/verz5a2a

DOI : https://doi.org/10.38124/ijisrt/26apr2388

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Abstract : In contemporary ages, Artificial Intelligence has swiftly changed all the paces of education. Tools like ChatGPT and other AI chatbots are being extensively used for providing support to students in learning. The increasing usage and involvement of AI-powered chatbots in educational environments has elevated crucial concerns pertaining to trust, ethical behavior, and expected bias in automated communications. When the earlier research has greatly concentrated on theoretical discussions and isolated assessments, it has been observed that the comprehensive empirical studies amalgamating user perception with experimental validation was lacking. With a view to address this gap, a mixed-method approach involving both systematic literature analysis and primary data collection was applied in this study. A total of 134 research papers were reviewed, of which 29 were selected for in-depth investigation to find key dimensions of trust, ethics, and bias in educational chatbots. Building on these insights, an investigational study was performed with a sample of n = 120 students. They were divided into two groups. One group interacted with a custom-developed AI chatbot, while another group was trained using traditional teaching methods such as providing them with the notes and structured assessments. A standardized survey instrument was dispensed to both groups to evaluate perceived trust, ethical reliability, and bias in learning interactions. The results showed that chatbot-assisted learning enhanced perceived accessibility and engagement, with trust levels increasing by approximately 18.4% compared to the traditional group. Similarly, ethical perception improved by approximately 13%, reflecting better alignment with fairness, transparency, and accountability principles. However, the analysis of bias-related parameters revealed an approximately 8% gap in neutrality, suggesting the necessity for additional refinement in reducing bias and ensuring equitable responses. Statistical analysis using a two-sample t-test confirmed that the observed differences were significant (p < 0.05). This study contributes by offering an incorporated experiential context for assessing AI-powered educational chatbots and suggests hands-on understandings for developing more trustworthy, ethical, and unbiased AI systems in education.

Keywords : AI Chatbot; Ethics & Trust; Bias; Automated Communications.

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In contemporary ages, Artificial Intelligence has swiftly changed all the paces of education. Tools like ChatGPT and other AI chatbots are being extensively used for providing support to students in learning. The increasing usage and involvement of AI-powered chatbots in educational environments has elevated crucial concerns pertaining to trust, ethical behavior, and expected bias in automated communications. When the earlier research has greatly concentrated on theoretical discussions and isolated assessments, it has been observed that the comprehensive empirical studies amalgamating user perception with experimental validation was lacking. With a view to address this gap, a mixed-method approach involving both systematic literature analysis and primary data collection was applied in this study. A total of 134 research papers were reviewed, of which 29 were selected for in-depth investigation to find key dimensions of trust, ethics, and bias in educational chatbots. Building on these insights, an investigational study was performed with a sample of n = 120 students. They were divided into two groups. One group interacted with a custom-developed AI chatbot, while another group was trained using traditional teaching methods such as providing them with the notes and structured assessments. A standardized survey instrument was dispensed to both groups to evaluate perceived trust, ethical reliability, and bias in learning interactions. The results showed that chatbot-assisted learning enhanced perceived accessibility and engagement, with trust levels increasing by approximately 18.4% compared to the traditional group. Similarly, ethical perception improved by approximately 13%, reflecting better alignment with fairness, transparency, and accountability principles. However, the analysis of bias-related parameters revealed an approximately 8% gap in neutrality, suggesting the necessity for additional refinement in reducing bias and ensuring equitable responses. Statistical analysis using a two-sample t-test confirmed that the observed differences were significant (p < 0.05). This study contributes by offering an incorporated experiential context for assessing AI-powered educational chatbots and suggests hands-on understandings for developing more trustworthy, ethical, and unbiased AI systems in education.

Keywords : AI Chatbot; Ethics & Trust; Bias; Automated Communications.

Paper Submission Last Date
31 - May - 2026

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