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ERIC Number: EJ1465886
Record Type: Journal
Publication Date: 2025-Apr
Pages: 27
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0013-2004
EISSN: EISSN-1741-5446
Available Date: 2025-03-19
Personalized Learning with AI Tutors: Assessing and Advancing Epistemic Trustworthiness
Nicolas J. Tanchuk1; Rebecca M. Taylor1
Educational Theory, v75 n2 p327-353 2025
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.
Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www-wiley-com.bibliotheek.ehb.be/en-us
Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Author Affiliations: 1Department of Education Policy, Organization and Leadership, University of Illinois at Urbana-Champaign