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Enhancing teacher AI literacy and integration through different types of cases in teacher professional development
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Integrating artificial intelligence (AI) into teaching practices is increasingly vital for preparing students for a technology-centric future. This study examined the influence of a case-based AI professional development (PD) program on AI integration strategies and AI literacy among seven middle school science teachers. Employing three distinct case problems, from well-structured to ill-structured, the AI PD program aimed to stimulate teachers’ reflection on AI literacy development and encourage the construction of problem-solving and AI integration strategies within various pedagogical contexts. Analysis of video-recorded case discussions revealed that teachers primarily drew on personal experiences for collaborative problem-solving across the three cases. However, the complexity of the case problems influenced their approach to knowledge co-construction, and dealing with ill-structured problems promoted the application of new knowledge. Through analyzing the survey data, we found a marked increase in teachers’ AI literacy, particularly in the domain of knowing and understanding AI, suggesting a pivotal role for direct instruction that supports AI literacy growth. However, their application of this AI knowledge was limited during the case discussions, while other domains of teacher AI literacy were more frequently employed. The findings highlight the importance of combining direct instruction with case-based discussions in AI-related PD programs to bolster teacher AI literacy effectively. The research has implications for using a case-based learning approach during short-term PD initiatives and advocates the ongoing need for comprehensive AI literacy development to facilitate teachers’ AI integration in subject-specific teaching.
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“Other studies have also suggested that gen-AI can help boost self-efficacy for PSTs by providing them with tools that allow for a more tailored and responsive learning experience (Ayanwale et al., 2024; A.-C. E. Ding et al., 2024; Samarescu et al., 2024; Sanusi et al., 2024).”
From Mentorship in the Age of Generative AI: ChatGPT to Support Self-Regulated Learning of Pre-Service Teachers Before and During Placements (2025) · cites this paper as A.-C. E. Ding et al., 2024
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“The integration of gen-AI tools in WIL placements tangentially supports the development of SRL by enabling PSTs to independently navigate teaching challenges while also drawing on technological enhancements, in part confirming the findings of J. Ding et al. (2022) and A.-C. E. Ding et al. (2024).”
From Mentorship in the Age of Generative AI: ChatGPT to Support Self-Regulated Learning of Pre-Service Teachers Before and During Placements (2025) · cites this paper as A.-C. E. Ding et al. (2024)
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Du, J. (., & Kohnke, L. (2026) · Teaching Education · doi:10.1080/10476210.2025.2590694 · Cited by 4
“This includes both technical competence (e.g. prompt engineering, tool selection, troubleshooting) and pedagogical alignment (e.g. capabilities in lesson planning, instructional design, learner support; Ding et al., 2024; Kohnke & Zou, 2025b; Moorhouse et al., 2024).”
Yau, K. W., Dong, T., Chai, C. S., Chiu, T. K. F., Meng, H. M. L., King, I., Wong, S. W. H., & Yam, Y. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100520 · Cited by 6
Huang, X., Lai, C., Song, H., & Sun, M. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-13901-9 · Cited by 4
“While most existing studies focus on introducing GenAI to teach - ers by demonstrating its use in teaching (Ding et al., 2024 ; Luckin et al., 2022 ), there is a lack of effective programs dedicated to integrating GenAI into teachers’ profes - sional learning.”
Ji, L., Mbowe, A. E., Aziku, M., & Tahri, D. (2026) · Education Sciences · doi:10.3390/educsci16040538 · Cited by 5
“Ding et al. (2024) pointed out that case-based professional development enhances teachers’ AI literacy, which subsequently improves their teaching strategies, noting that despite limited self-reports during discussions, teachers improved in developing strategies to integrate AI into their teaching.”
InfluentialVisedsun, N., Nupueng, S., Imjai, N., Chávez-Díaz, J. M., & Aujirapongpan, S. (2026) · The International Journal of Management Education · doi:10.1016/j.ijme.2026.101437 · Cited by 5
Navigating Ethical Dilemmas in the Implementation of AI-Driven Educational Technologies
Ismail, M., Madathil, N. T., Alalawi, M., Alalawi, S., & Alrabaee, S. (2025) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon62633.2025.11016612 · Cited by 4
Kılıç, E., Almasri, F., & Çelik, H. E. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100486 · Cited by 5
Yang, J. T., & Li, X. (2026) · BMC Psychology · doi:10.1186/s40359-025-03949-6 · Cited by 5
“Teachers’ AI literacy is thus viewed as a multifaceted skill set that integrates knowledge, skills, attitudes, and ethics, enabling educators to effectively navigate AI-driven teaching environments [10, 43].”
Engineering the Unexpected: Faculty Strategies for Navigating Academic Disruptions
Raju, G. M., Balart, T., Ligler, G. T., Shryock, K. J., & Brumbelow, K. (2025) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie63693.2025.11328131 · Cited by 3
“This aligns with research suggesting that faculty training and clear AI policies can mitigate the negative impacts of technology in academia [37].”
Questioning the “Magic” of Generative Artificial Intelligence in a Science Methods Course
Pleasants, J., & Morris, M. M. (2026) · Journal of Science Education and Technology · doi:10.1007/s10956-026-10300-0 · Cited by 3
“…the broader field of research on AI in education, there is a common assumption that greater adoption of AI technologies by teachers is unambiguously a good thing, and that barriers to integration must therefore be reduced (e.g., Ding et al., 2024; Kaplan-Rakowski et al., 2023; Ma & Lei, 2024).”
Metadata
- Authors
- Ding, A.-C. E., Shi, L., Yang, H., & Choi, I.
- Affiliations
- University of Georgia (US)
- Emory University (US)
- Corresponding author
- Ding, Ai-Chu Elisha
- Venue
- Computers and Education Open
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 6
- Pages
- 100178
- ISSN
- 2666-5573 (ISSN-L)
- OpenAlex
- W4394680566
- Cited by
- 228
- References
- 60
- Citations by year
- 2026: 103 · 2025: 105 · 2024: 20
- FWCI
- 85.73
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- Computers and Education Open (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Education › Digital literacy in education
- Social Sciences › Education › Teacher Education and Leadership Studies
- Keywords
- teacher AI literacy, AI in teaching, middle school science teachers, knowledge co-construction, collaborative problem solving, direct instruction, teacher reflection, AI integration strategies
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