Résumés
Abstract
This study examined an online professional development program integrating artificial intelligence (AI) literacy into mathematics instruction through unplugged, explainable machine-learning activities. Ten K–12 educators created explainable feature matrices to classify geometric shapes, making machine-learning algorithms visible and accessible without requiring complex software or technological tools. The intervention used ontological principles to bridge familiar mathematical concepts with algorithmic processes. Findings demonstrated positive changes across all constructs, with participants’ AI self-efficacy increasing from below-moderate to above-moderate levels. Sentiment analysis revealed dramatic shifts from negative to positive perceptions of AI in education, with 30% of participants initially using negative descriptors versus 0% post intervention. Thematic analysis revealed three key outcomes: (a) AI concepts became explainable and learnable, (b) participants gained enhanced understanding of classification processes, and (c) participants valued the practical applicability of unplugged approaches. The study demonstrates that effective AI literacy education can be delivered through conceptual understanding rather than technological implementation, providing an accessible pathway for K–12 AI integration regardless of resource constraints.
Keywords:
- AI literacy,
- explainable machine learning,
- geometry,
- online delivery,
- teacher professional development
Parties annexes
Bibliography
- Abdennour, O., Kemouss, H., & Khaldi, M. (2025). Integrating AI education in non-technical disciplines: Bridging the gap between theory and practice. In M. Khaldi (Ed.), Ethics and AI integration into modern classrooms (pp. 117–146). IGI Global Scientific Publishing. https://www.doi.org/10.4018/979-8-3373-2262-9.ch005
- Amin, M. R. M., Ismail, I., & Sivakumaran, V. M. (2025). Revolutionizing education with artificial intelligence (AI)? Challenges, and implications for open and distance learning (ODL). Social Sciences & Humanities Open, 11, Article 101308. https://doi.org/10.1016/j.ssaho.2025.101308
- Aydin, M., & Yurdugül, H. (2024). Developing a curriculum framework of artificial intelligence teaching for gifted students. Kastamonu Eğitim Dergisi, 32(1), 14–37. https://doi.org/10.24106/kefdergi.1426429
- Belle, V., & Papantonis, I. (2021). Principles and practice of explainable machine learning. Frontiers in Big Data, 4, Article 688969. https://doi.org/10.3389/fdata.2021.688969
- Blake-West, J. C., & Bers, M. U. (2023). ScratchJr design in practice: Low floor, high ceiling. International Journal of Child-Computer Interaction, 37, Article 100601. https://doi.org/10.1016/j.ijcci.2023.100601
- Borko, H., Jacobs, J., & Koellner, K. (2009). Contemporary approaches to teacher professional development. In H. Borko, J. Jacobs, & K. Koellner (Eds.), International encyclopedia of education (3rd ed., pp. 548–556). Elsevier. https://doi.org/10.1016/B978-0-08-044894-7.00654-0
- Bragg, L. A., Walsh, C., & Heyeres, M. (2021). Successful design and delivery of online professional development for teachers: A systematic review of the literature. Computers & Education, 166, Article 104158. https://doi.org/10.1016/j.compedu.2021.104158
- Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
- Casal-Otero, L., Catala, A., Fernández-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023). AI literacy in K–12: A systematic literature review. International Journal of STEM Education, 10(1), Article 29. https://doi.org/10.1186/s40594-023-00418-7
- Clarke, V., & Braun, V. (2017). Thematic analysis. The Journal of Positive Psychology, 12(3), 297–298. https://doi.org/10.1080/17439760.2016.1262613
- Code.org. (2026). AI for oceans. https://studio.code.org/courses/oceans
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://www.jstor.org/stable/249008
- Ding, A.-C. E., Shi, L., Yang, H., & Choi, I. (2024). Enhancing teacher AI literacy and integration through different types of cases in teacher professional development. Computers and Education Open, 6, Article 100178. https://doi.org/10.1016/j.caeo.2024.100178
- Dogan, M. E., Goru Dogan, T., & Bozkurt, A. (2023). The use of artificial intelligence (AI) in online learning and distance education processes: A systematic review of empirical studies. Applied Sciences, 13(5), Article 3056. https://doi.org/10.3390/app13053056
- Doll, W. J., Hendrickson, A., & Deng, X. (1998). Using Davis’s perceived usefulness and ease‐of‐use instruments for decision making: A confirmatory and multigroup invariance analysis. Decision Sciences, 29(4), 839–869. https://doi.org/10.1111/j.1540-5915.1998.tb00879.x
- Feldman, R., & Ungar, L. (2012, June 4–7). Sentiment mining from user generated content. [Tutorial]. The Sixth International AAAI Conference on Weblogs and Social Media, Dublin, Ireland. http://www.cis.upenn.edu/~ungar/AAAI/AAAI13.pdf
- Grimm, S. (2009). Knowledge representation and ontologies. In M. M. Gaber (Ed.), Scientific data mining and knowledge discovery: Principles and foundations (pp. 111–137). Springer. https://doi.org/10.1007/978-3-642-02788-8_6
- Grover, S. (2024). Teaching AI to K–12 learners: Lessons, issues, and guidance. In B. Stephenson & J. A. Stone (Chairs), Proceedings of the 55th ACM Technical Symposium on Computer Science Education (Vol. 1, pp. 422–428). ACM. https://doi.org/10.1145/3626252.3630937
- Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43. https://doi.org/10.3102/0013189X12463051
- Hwang, G.-J., & Tu, Y.-F. (2021). Roles and research trends of artificial intelligence in mathematics education: A bibliometric mapping analysis and systematic review. Mathematics, 9(6), Article 584. https://doi.org/10.3390/math9060584
- Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415
- Kiili, C., Kauppinen, M., Coiro, J., & Utriainen, J. (2016). Measuring and supporting pre-service teachers’ self-efficacy towards computers, teaching, and technology integration. Journal of Technology and Teacher Education, 24(4), 443–469. https://www.learntechlib.org/primary/p/152285/
- Kohnke, L., Zou, D., Ou, A. W., & Gu, M. M. (2025). Preparing future educators for AI-enhanced classrooms: Insights into AI literacy and integration. Computers and Education: Artificial Intelligence, 8, Article 100398. https://doi.org/10.1016/j.caeai.2025.100398
- Lee, D., & Yeo, S. (2022). Developing an AI-based chatbot for practicing responsive teaching in mathematics. Computers & Education, 191, Article 104646. https://doi.org/10.1016/j.compedu.2022.104646
- Lee, I., Ali, S., Zhang, H., DiPaola, D., & Breazeal, C. (2021, March). Developing middle school students’ AI literacy. In M. Sherriff & L. D. Merkle (Chairs), Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (pp. 191–197). ACM. https://doi.org/10.1145/3408877.3432513
- Lee, J. A., Sagong, M. J., Jung, J., Kim, E. S., & Kim, H. S. (2023). Explainable machine learning for understanding and predicting geometry and defect types in Fe-Ni alloys fabricated by laser metal deposition additive manufacturing. Journal of Materials Research and Technology, 22, 413–423. https://doi.org/10.1016/j.jmrt.2022.11.137
- Liu, C.-F., Chen, Z.-C., Kuo, S.-C., & Lin, T.-C. (2022). Does AI explainability affect physicians’ intention to use AI? International Journal of Medical Informatics, 168, Article 104884. https://doi.org/10.1016/j.ijmedinf.2022.104884
- Martins, R. M., & Gresse Von Wangenheim, C. (2023). Findings on teaching machine learning in high school: A ten-year systematic literature review. Informatics in Education, 22(3), 421–440. https://www.ceeol.com/search/article-detail?id=1192082
- Michigan State University. (n.d.). Hierarchical classification. College of Agriculture and Natural Resources. https://www.canr.msu.edu/resources/hierarchical-classification
- Mills, K. A., Cope, J., Scholes, L., & Rowe, L. (2024). Coding and computational thinking across the curriculum: A review of educational outcomes. Review of Educational Research, 95(3), 581–618. https://doi.org/10.3102/00346543241241327
- Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, Article 100041. https://doi.org/10.1016/j.caeai.2021.100041
- Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71(1), 137–161. https://doi.org/10.1007/s11423-023-10203-6
- Richard, P. R., Pilar Vélez, M., & Van Vaerenbergh, S. (Eds.). (2022). Mathematics education in the age of artificial intelligence. How artificial intelligence can serve mathematical human learning. Springer Nature.
- Samek, W., & Müller, K.-R. (2019). Towards explainable artificial intelligence. In W. Samek, G. Montavon, A. Vedaldi, L. K. Hansen, & K.-R. Müller (Eds.), Explainable AI: Interpreting, explaining and visualizing deep learning (pp. 5–22). Springer. https://doi.org/10.1007/978-3-030-28954-6_1
- Schulz, S. & Stenzhorn, H. (2007). Ten theses on clinical ontologies. In L. Bos & B. Blobel (Eds.), Medical and Care Compunetics 4 (pp. 268–275). IOS Press.
- Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, Article 102551. https://doi.org/10.1016/j.ijhcs.2020.102551
- Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive Robotics, 3, 54–70. https://doi.org/10.1016/j.cogr.2023.04.001
- Steinbauer, G., Kandlhofer, M., Chklovski, T., Heintz, F., & Koenig, S. (2021). A differentiated discussion about AI education K–12. KI-Künstliche Intelligenz, 35(2), 131–137. https://doi.org/10.1007/s13218-021-00724-8
- Tan, Q., & Tang, X. (2025). Unveiling AI literacy in K-12 education: A systematic literature review of empirical research. Interactive Learning Environments, 33(9), 5347–5363. https://doi.org/10.1080/10494820.2025.2482586
- Tiddi, I., & Schlobach, S. (2022). Knowledge graphs as tools for explainable machine learning: A survey. Artificial Intelligence, 302, Article 103627. https://doi.org/10.1016/j.artint.2021.103627
- Timm, J. T. E., & Gannod, G. C. (2005, July). A model-driven approach for specifying semantic Web services. In C. K. Chang & L.-J. Zhang (Chairs), Proceedings 2005 IEEE International Conference on Web Services (pp. 313–320). IEEE. https://doi.org/10.1109/ICWS.2005.9
- UNESCO. (2022). K–12 AI curricula: A mapping of government-endorsed AI curricula. https://doi.org/10.54675/ELYF6010
- Vittorini, P., Menini, S., & Tonelli, S. (2021). An AI-based system for formative and summative assessment in data science courses. International Journal of Artificial Intelligence in Education, 31(2), 159–185. https://doi.org/10.1007/s40593-020-00230-2
- Walter, Y. (2024). Embracing the future of artificial intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21(1), Article 15. https://doi.org/10.1186/s41239-024-00448-3
- Wang, T., & Cheng, E. C. K. (2021). An investigation of barriers to Hong Kong K–12 schools incorporating artificial intelligence in education. Computers and Education: Artificial Intelligence, 2, Article 100031. https://doi.org/10.1016/j.caeai.2021.100031
- Wang, N., & Lester, J. (2023). K–12 education in the age of AI: A call to action for K–12 AI literacy. International Journal of Artificial Intelligence in Education, 33(2), 228–232. https://doi.org/10.1007/s40593-023-00358-x
- Younis, B. (2024). Effectiveness of a professional development program based on the instructional design framework for AI literacy in developing AI literacy skills among pre-service teachers. Journal of Digital Learning in Teacher Education, 40(3), 142–158. https://doi.org/10.1080/21532974.2024.2365663
- Zhou, J., & Ye, J.-M. (2023). Sentiment analysis in education research: A review of journal publications. Interactive Learning Environments, 31(3), 1252–1264. https://doi.org/10.1080/10494820.2020.1826985