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The Past, Present, and Future Use of Artificial Intelligence in Teacher Education
Open accessAbstract
The use of artificial intelligence (AI) is not a new concept. Still, the press, the worry, and the hype around the potential benefits and limitations of the explosion of these tools in this field is a current topic in teacher education. In this article, the authors summarize the past use of AI, present easily adaptable tools in teacher education, and discuss what is on the horizon in industry and special education teacher education. The authors highlight tools that should be considered in programs today, followed by ways to expand the field of AI in teacher education to support the learning outcomes of struggling students.
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The inclusion of disability in AI data is relatively unknown.
“This is especially relevant in consideration of the source of AI data and that the inclusion of disability is relatively unknown (Mosher et al., 2024).”
From Procedure to Evaluate Artificial Intelligence (AI) Output for Educational and Clinical Tasks (2026) · cites this paper as Mosher et al., 2024
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Metadata
- Authors
- Mosher, M., Dieker, L., & Hines, R.
- Affiliations
- University of Kansas (US)
- University of Central Florida (US)
- Venue
- Journal of Special Education Preparation
- Publisher
- Ball State University Libraries
- Published
- 2024-09-02
- Volume
- 4
- Issue
- 2
- Pages
- 6-17
- ISSN
- 2768-1432 (ISSN-L)
- OpenAlex
- W4402186862
- Cited by
- 18
- References
- 68
- Citations by year
- 2026: 10 · 2025: 8
- FWCI
- 6.79
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Journal of Special Education Preparation (publishedVersion, cc-by-nc-nd)
- Journal of Special Education Preparation (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- artificial intelligence, teacher professional development, special education teacher training, AI tools, struggling students, learning outcomes
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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