Generative AI Highlights the Contrast between Students' Dualistic Epistemic Practices and Teacher Education Learning Objectives

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Bibliographic Details
Title: Generative AI Highlights the Contrast between Students' Dualistic Epistemic Practices and Teacher Education Learning Objectives
Language: English
Authors: Tellervo Härkki, Tarmo Thorström, Miika Leino, Henriikka Vartiainen, Matti Tedre
Source: Australian Journal of Teacher Education. 2025 50(4):59-83.
Availability: Edith Cowan University. Bradford Street, Mount Lawley, West Australia 6050, Australia. Web site: http://ro.ecu.edu.au/ajte/
Peer Reviewed: Y
Page Count: 26
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Artificial Intelligence, Epistemology, Teacher Education, Learning Objectives, Student Teachers, Technology Uses in Education, Student Research, Student Attitudes, Undergraduate Students, Research Skills, Foreign Countries, Preservice Teachers
Geographic Terms: Finland
ISSN: 0313-5373
1835-517X
Abstract: This study examines student teachers' capabilities when adopting a generative AI system as a new cognitive tool. In our pedagogical intervention, students used ChatGPT 3.5 to support a small research task. Consistent with decades of research on higher education students' epistemic positions, most students approached the knowledge-building task (and, respectively, ChatGPT) with dualistic epistemic practices. Notably, ChatGPT's polished interface invites naïve dualistic interpretations. However, teacher education learning objectives and effective knowledge-building with generative AI tools require more sophisticated epistemic stances: understanding knowledge as contingent and context-bound and knowledge-building as an activity that requires validation. This suggests that the central challenge for teacher education is not generative AI per se but supporting students' epistemic development so that they can use such tools responsibly.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1497656
Database: ERIC
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