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Personalized Learning with AI Tutors: Assessing and Advancing Epistemic Trustworthiness.

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Title: Personalized Learning with AI Tutors: Assessing and Advancing Epistemic Trustworthiness.
Authors: Tanchuk, Nicolas J. (AUTHOR), Taylor, Rebecca M. (AUTHOR)
Source: Educational Theory. Apr2025, Vol. 75 Issue 2, p327-353. 27p.
Subjects: Epistemics, Trust, Class differences, Instructional systems, Collaborative learning, Academic achievement, Individualized instruction
Abstract: AI tutors are promised to expand access to personalized learning, improving student achievement and addressing disparities in resources available to students across socioeconomic contexts. The rapid development and introduction of AI tutors raises fundamental questions of epistemic trust in education. What criteria should guide students' critical assessments of the epistemic trustworthiness of these new technologies? And furthermore, how should these technologies and the environments in which they are situated be designed to improve their epistemic trustworthiness? In this article, Nicolas Tanchuk and Rebecca Taylor argue for a shared responsibility model of epistemic trust that includes a duty to collaboratively improve the epistemic environment. Building off prior frameworks, the model they advance identifies five higher‐order criteria to assess the epistemic credibility of individuals, tools, and institutions and to guide the co‐creation of the epistemic environment: (1) epistemic motivation, (2) epistemic inclusivity, (3) epistemic accountability, (4) epistemic accuracy, and (5) reciprocal epistemic transparency. [ABSTRACT FROM AUTHOR]
Copyright of Educational Theory is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: AI tutors are promised to expand access to personalized learning, improving student achievement and addressing disparities in resources available to students across socioeconomic contexts. The rapid development and introduction of AI tutors raises fundamental questions of epistemic trust in education. What criteria should guide students' critical assessments of the epistemic trustworthiness of these new technologies? And furthermore, how should these technologies and the environments in which they are situated be designed to improve their epistemic trustworthiness? In this article, Nicolas Tanchuk and Rebecca Taylor argue for a shared responsibility model of epistemic trust that includes a duty to collaboratively improve the epistemic environment. Building off prior frameworks, the model they advance identifies five higher‐order criteria to assess the epistemic credibility of individuals, tools, and institutions and to guide the co‐creation of the epistemic environment: (1) epistemic motivation, (2) epistemic inclusivity, (3) epistemic accountability, (4) epistemic accuracy, and (5) reciprocal epistemic transparency. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Educational Theory is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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