AI Literacy in Teacher Education: Empowering Educators Through Critical Co-Discovery
Abstract
Teacher education increasingly requires educators to engage with generative AI technologies, yet critical and reflective engagement opportunities remain scarce. While AI is often framed as a tool for automation, its broader pedagogical and ethical implications receive less attention. To address this gap, we implemented a critical co-discovery approach within an online AI in Education (AIEd) course to enhance educators’ AI literacy. This illustrative case study examines which AI literacy components can be developed through critical co-discovery and how this approach fosters educators’ reflective, critical, and participatory engagement with AI. Findings revealed that through co-discovery activities, educators co-constructed an understanding of AI concepts, ethical considerations, and context-specific applications. The study highlights the need for prolonged engagement with AI literacy by integrating it into teacher education program to ensure educators can critically navigate and assert their agency in AI’s complex role in education.
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Engaging key stakeholders in all phases of co-design fosters a deep understanding of the process, increases ownership, and enhances commitment to utilization of resultant findings.
“Engaging these actors in all phases fosters a deep understanding of the process, increases ownership, and enhances commitment to utilization of resultant findings (Dilek et al., 2025; Patton and Campbell-Patton, 2022).”
From AI literacy for teacher educators: a holistic curriculum for capacity-building in higher education (2026) · cites this paper as Dilek et al., 2025
Recent studies conceptualize the integration of AI into teaching in relation to AI-TPACK, highlighting the need for teachers to develop competencies that enable them to critically and pedagogically integrate AI technologies into instructional practices.
“Recent studies have also begun to conceptualize this integration in relation to AI-TPACK, highlighting the need for teachers to develop competencies that enable them to critically and pedagogically integrate AI technologies into instructional practices (Dilek et al., 2025; Zhang & Zhang, 2024).”
From Integrating artificial intelligence into preservice teacher education: enhancing AI literacy and digital material design competency (2026) · cites this paper as Dilek et al., 2025
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Lyublinskaya, I., & Du, X. (2026) · Springer texts in education · doi:10.1007/978-3-032-23570-1_6
Biagini, G., & Ranieri, M. (2026) · EXCELLENCE AND INNOVATION IN LEARNING AND TEACHING · doi:10.3280/exioa1-2026oa23214
Developing Preservice Teachers’ AI Literacy for Creative, Critical, and Agentic Classrooms
Sun, L. (2026) · School Science and Mathematics · doi:10.1111/ssm.70054
EFL Teachers' Perceptions of AI-Assisted Material Development in Language Classrooms
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From Metrics to Practice: Rethinking and Refining the AI Literacy Scale for Teachers
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- Authors
- Dilek, M., Baran, E., & Aleman, E.
- Affiliations
- Iowa State University (US)
- Universidad Tecnológica de Uruguay (UY)
- Corresponding author
- Baran, Evrim
- Venue
- Journal of Teacher Education
- Publisher
- SAGE Publications
- Published
- 2025-03-18
- Volume
- 76
- Issue
- 3
- Pages
- 294-311
- ISSN
- 0022-4871 (ISSN-L)
- 1552-7816
- OpenAlex
- W4408544040
- Semantic Scholar
- 861afe483ed306fd2cf166893a0d7c1eaa58d155
- Cited by
- 86
- References
- 52
- Citations by year
- 2026: 68 · 2025: 18
- FWCI
- 89.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Teacher Education (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Digital literacy in education
- Physical Sciences › Computer Science › Information Systems › Digital Education and Society
- Keywords
- AI literacy, teacher professional development, generative AI in education, AI ethics, reflective practice, teacher agency, co-construction of knowledge
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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