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Co-designing AI Education Curriculum with Cross-Disciplinary High School Teachers
Abstract
High school teachers from many disciplines have growing interests in teaching about artificial intelligence (AI). This cross-disciplinary interest reflects the prevalence of AI tools across society, such as Generative AI tools built upon Large Language Models (LLM). However, high school classes are unique and complex environments, led by teachers with limited time and resources with priorities that vary by class and the students they serve. Therefore, developing curricula about AI for classes that span many disciplines (e.g. history, art, math) must involve centering the expertise of cross-disciplinary teachers. In this study, we conducted five collaborative curricular co-design sessions with eight teachers who taught high school humanities and STEM classes. We sought to understand how teachers considered AI when it was taught in art, math, and social studies contexts, as well as opportunities and challenges they identified with incorporating AI tools into their instruction. We found that teachers considered technical skills and ethical debates around AI, opportunities for "dual exploration" between AI and disciplinary learning, and limitations of AI tools as supporting engagement and reflection but also potentially distracting. We interpreted our findings relative to co-designing adaptable AI curricula to support teaching about and with AI across high school disciplines.
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“Institutions have started to develop AI literacy curricula instructing students on the rationale behind AI algorithms and appropriate AI use in learning environments (Almatrafi et al., 2024;Xie et al., 2024;Zhang, 2024).”
From Cheating in the second year of generative AI chatbots: a follow-up study on high school student cheating behaviors (2026) · cites this paper as Xie et al., 2024
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Cites
Using thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414
Qualitative Quality: Eight “Big-Tent” Criteria for Excellent Qualitative Research
Tracy, S. J. (2010) · Qualitative Inquiry · doi:10.1177/1077800410383121 · Cited by 7,536 · cited 1×
“Instead, we should consider the credibility, richness, and re-coverability of this study (Locke 2019; Holwell 2004; Tracy 2010), treating the results of our coding effort as organizations of claims about data rather than as quantitative data in and of itself (Hammer and Berland 2014).”
BackgroundHenrich, J., Heine, S. J., & Norenzayan, A. (2010) · Nature · doi:10.1038/466029a · Cited by 3,441 · cited 3×
“A threat to validity is that this research was conducted in a Western, educated, industrialized, rich and democratic (W.E.I.R.D.) societal context.”
BackgroundWhat is AI Literacy? Competencies and Design Considerations
Long, D., & Magerko, B. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376727 · Cited by 3,241
Rudin, C., & Radin, J. (2019) · Harvard Data Science Review · doi:10.1162/99608f92.5a8a3a3d · Cited by 432
A systematic review of teaching and learning machine learning in K-12 education
Sanusi, I. T., Oyelere, S. S., Vartiainen, H., Suhonen, J., & Tukiainen, M. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11416-7 · Cited by 212
MusicLM: Generating Music From Text
Agostinelli, A., Denk, T. I., Borsos, Z., Engel, J., Verzetti, M., Caillon, A., Huang, Q., Jansen, A., Roberts, A., Tagliasacchi, M., et al. (2023) · arXiv · doi:10.48550/arxiv.2301.11325 · Cited by 183
Co-designing AI literacy exhibits for informal learning spaces
Long, D., Blunt, T., & Magerko, B. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3476034 · Cited by 128
Research works printed but not linked: 28
- Ali, S.; Kumar, V.; and Breazeal, C. 2023. AI Audit: A Card Game to Refect on Everyday AI Systems. AAAI, 37(13).
- Alvarez, L.; Gransbury, I.; Cateté, V.; Barnes, T.; Ledéczi, Á.; and Grover, S. 2022. A Socially Relevant Focused AI Curriculum Designed for Female High School Students. AAAI, 36(11): 12698-12705.
- Blanchard, E. G. 2012. On the WEIRD Nature of IT- S/AIED Conferences. In Intelligent Tutoring Systems, 280- 285. Springer Berlin Heidelberg.
- Bowen, G. A. 2006. Grounded Theory and Sensitizing Con- cepts. International Journal of Qualitative Methods, 5(3).
- Chao, J.; Ellis, R.; Jiang, S.; Rosé, C.; Finzer, W.; Tatar, C.; Fiacco, J.; and Wiedemann, K. 2023. Exploring Artifcial Intelligence in English Language Arts with StoryQ. AAAI, 37(13): 15999-16003.
- Costanza-Chock, S. 2020. Design Justice: Community-Led Practices to Build the Worlds We Need. MIT Press.
- Druga, S.; Otero, N.; and Ko, A. J. 2022. The Landscape of Teaching Resources for AI Education. In 27th ACM Confer- ence on on Innovation and Technology in Computer Science Education Vol. 1, 96-102. New York, NY, USA: ACM.
- Grimm, P. 2010. Social Desirability Bias. In Sheth, J.; and Malhotra, N., eds., Wiley International Encyclopedia of Marketing, volume 50, 537. Chichester, UK: John Wiley & Sons, Ltd. ISBN 9781405161787, 9781444316568.
- Guerzhoy, M.; Neumann, M.; Johnson*, E.; Johnson, D.; Chai, H.; Garijo, D.; Lyu, Z.; and MacLellan, C. J. 2022. EAAI-22 Blue Sky Ideas in Artifcial Intelligence Education from the AAAI/ACM SIGAI New and Future AI Educator Program. AI Matters, 8(2): 16-21.
- Hammer, D.; and Berland, L. K. 2014. Confusing Claims for Data: A Critique of Common Practices for Presenting Qualitative Research on Learning. Journal of the Learning Sciences, 23(1): 37-46.
- Holwell, S. 2004. Themes, Iteration, and Recoverability in Action Research. In Kaplan, B.; Truex, D. P.; Wastell, D.; Wood-Harper, A. T.; and DeGross, J. I., eds., Information Systems Research: Relevant Theory and Informed Practice, 353-362. Boston, MA: Springer US. ISBN 9781402080951. Impact Research. 2023. Teachers and Students Embrace ChatGPT for Education. Technical report, Walton Family Foundation.
- Jiang, S.; Lee, V. R.; and Rosenberg, J. M. 2022. Data sci- ence education across the disciplines: Underexamined op- portunities for K-12 innovation. British Journal of Educa- tional Technology, 53(2): 1073-1079.
- Krakowski, A.; Greenwald, E.; Hurt, T.; Nonnecke, B.; and Cannady, M. 2022. Authentic Integration of Ethics and AI through Sociotechnical, Problem-Based Learning. AAAI, 36(11): 12774-12782.
- Kumar, V.; and Worsley, M. 2023. Scratch for Sports: Ath- letic Drills as a Platform for Experiencing, Understanding, and Developing AI-Driven Apps. AAAI, 37(13).
- Lee, I.; and Perret, B. 2022. Preparing High School Teachers to Integrate AI Methods into STEM Classrooms. AAAI.
- Lin, P.; and Van Brummelen, J. 2021. Engaging Teachers to Co-Design Integrated AI Curriculum for K-12 Classrooms. In CHI Conference on Human Factors in Computing Sys- tems, CHI '21. New York, NY, USA: ACM.
- Linxen, S.; Sturm, C.; Brühlmann, F.; Cassau, V.; Opwis, K.; and Reinecke, K. 2021. How WEIRD is CHI? In CHI Con- ference on Human Factors in Computing Systems, number Article 143 in CHI '21, 1-14. New York, NY, USA: ACM.
- Locke, L. A. 2019. Typical Areas of Confusion for Students New to Qualitative Research. In Strunk, K. K.; and Locke, L. A., eds., Research Methods for Social Justice and Equity in Education, 117-123. Springer.
- Norouzi, N.; Chaturvedi, S.; and Rutledge, M. 2020. Lessons Learned from Teaching Machine Learning and Nat- ural Language Processing to High School Students. AAAI, 34(09): 13397-13403.
- Penuel, W. R.; Allen, A.-R.; Coburn, C. E.; and Farrell, C. 2015. Conceptualizing Research-Practice Partnerships as Joint Work at Boundaries. Journal of Education for Students Placed at Risk (JESPAR), 20(1-2): 182-197.
- Penuel, W. R.; Allen, A.-R.; Henson, K.; Campanella, M.; Patton, R.; Rademaker, K.; Reed, W.; Watkins, D.; Wingert, K.; Reiser, B.; and Zivic, A. 2022. Learning Practical Design Knowledge through Co-Designing Storyline Science Cur- riculum Units. Cognition and Instruction, 40(1): 148-170.
- Sentance, S.; Barendsen, E.; Howard, N. R.; and Schulte, C. 2023. Computer Science Education: Perspectives on Teach- ing and Learning in School. Bloomsbury Publishing.
- Severance, S.; Penuel, W. R.; Sumner, T.; and Leary, H. 2016. Organizing for Teacher Agency in Curricular Co- Design. Journal of the Learning Sciences, 25(4): 531-564.
- Song, J.; Yu, J.; Yan, L.; Zhang, L.; Liu, B.; Zhang, Y.; and Lu, Y. 2023. Develop AI Teaching and Learning Resources for Compulsory Education in China. AAAI, 37(13).
- Song, J.; Zhang, L.; Yu, J.; Peng, Y.; Ma, A.; and Lu, Y. 2022. Paving the Way for Novices: How to Teach AI for K-12 Education in China. AAAI, 36(11).
- Tedre, M.; Toivonen, T.; Kahila, J.; Vartiainen, H.; Valtonen, T.; Jormanainen, I.; and Pears, A. 2021. Teaching Machine Learning in K-12 Classroom: Pedagogical and Technologi- cal Trajectories for Artifcial Intelligence Education. IEEE Access, 9: 110558-110572.
- Touretzky, D. S.; and Gardner-McCune, C. 2023. Guiding Students to Investigate What Google Speech Recognition Knows about Language. AAAI, 37(13): 16040-16047.
- Walsh, B.; Dalton, B.; Forsyth, S.; and Yeh, T. 2023. Liter- acy and STEM Teachers Adapt AI Ethics Curriculum. AAAI, 37(13): 16048-16055.
Cited by
Sorted by citations
Chen, R., Lee, V. R., Kuo, A. C., Pope, D. C., & Miles, S. (2026) · Educational technology research and development · doi:10.1007/s11423-026-10587-1 · Cited by 8
Self-citation
Metadata
- Authors
- Xie, B., Sarin, P., Wolf, J., Garcia, R. C. C., Delaney, V., Sieh, I., Fuloria, A., Varuvel Dennison, D., Bywater, C., & Lee, V. R.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2024-03-24
- Volume
- 38
- Issue
- 21
- Pages
- 23146-23154
- ISSN
- 2374-3468
- 2159-5399
- Cited by
- 38
- References
- 8
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