Teaching Artificial Intelligence to K-12 Through a Role-Playing Game Questioning the Intelligence Concept
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In the majority of papers covering teachers' professional development and curriculum development, teachers' epistemic knowledge is reflected in implicit terms, often briefly referring back to existing curricula or frameworks that are not further explained.
“In the majority of the papers covering teachers ' pro fessional development (e.g., [86,87,89]) and curriculum development [70 -72,90,91], teachers ' epistemic knowledge is reflected in much more implicit terms, often briefly referring back to existing curricula or frameworks that are not further explained.”
From In search of artificial intelligence (AI) literacy in teacher education: A scoping review (2024) · cites this paper as [70]
Cites
Wagner, H. R., Glaser, B. G., & Strauss, A. L. (1968) · Social Forces · doi:10.2307/2575405 · Cited by 46,470 · see citing works
The Discovery of Grounded Theory; Strategies for Qualitative Research
Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968) · Nursing Research · doi:10.1097/00006199-196807000-00014 · Cited by 39,449 · see citing works
Design Experiments in Educational Research
Cobb, P., Confrey, J., diSessa, A., Lehrer, R., & Schauble, L. (2003) · Educational Researcher · doi:10.3102/0013189x032001009 · Cited by 3,548 · see citing works
What 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 · cited 1× · see citing works
“Relevant AI literacy frameworks intended for school audiences were developed recently [18, 21, 27, 35].”
BackgroundDesign-Based Research: An Emerging Paradigm for Educational Inquiry
(2003) · Educational Researcher · doi:10.3102/0013189x032001005 · Cited by 2,813 · see citing works
Design-based research: Putting a stake in the ground
Barab, S., & Squire, K. (2004) · Journal of the Learning Sciences · doi:10.1207/s15327809jls1301_1 · Cited by 2,642 · see citing works
Anderson, T. D., & Shattuck, J. (2012) · Educational Researcher · doi:10.3102/0013189x11428813 · Cited by 2,368 · see citing works
Design-based research and technology-enhanced learning environments
Wang, F., & Hannafin, M. J. (2005) · Educational Technology Research and Development · doi:10.1007/bf02504682 · Cited by 2,241 · see citing works
Voogt, J., & Roblin, N. P. (2012) · Journal of Curriculum Studies · doi:10.1080/00220272.2012.668938 · Cited by 1,561 · see citing works
Envisioning AI for K-12: What Should Every Child Know about AI?
Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019795 · Cited by 918 · see citing works
Artificial intelligence and computer science in education: From kindergarten to university
Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S., & Huber, P. (2016) · 2016 IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie.2016.7757570 · Cited by 437 · see citing works
Situating methods in the magic of Big Data and AI
Elish, M. C., & boyd, D. (2017) · Communication Monographs · doi:10.1080/03637751.2017.1375130 · Cited by 414 · see citing works
Riina, V., Punie, Y., Carretero, S., & Godelieve, V. D. B. (2016) · RePEc: Research Papers in Economics · doi:10.2791/607218 · Cited by 353 · see citing works
"Hey Google is it OK if I eat you?": Initial Explorations in Child-Agent Interaction
Druga, S., Williams, R., Breazeal, C., & Resnick, M. (2017) · Proceedings of the 2017 Conference on Interaction Design and Children · doi:10.1145/3078072.3084330 · Cited by 330 · see citing works
Learning machine learning with very young children: Who is teaching whom?
Vartiainen, H., Tedre, M., & Valtonen, T. (2020) · International Journal of Child-Computer Interaction · doi:10.1016/j.ijcci.2020.100182 · Cited by 221 · see citing works
Systematic Review of Design-Based Research Progress
McKenney, S. E., & Reeves, T. C. (2013) · Educational Researcher · doi:10.3102/0013189x12463781 · Cited by 206 · see citing works
Artificial intelligence, computational thinking, and mathematics education
Gadanidis, G. (2017) · International Journal of Information and Learning Technology · doi:10.1108/ijilt-09-2016-0048 · Cited by 121 · see citing works
Designing One Year Curriculum to Teach Artificial Intelligence for Middle School
Sabuncuoğlu, A. (2020) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3341525.3387364 · Cited by 119 · see citing works
Druga, S., Williams, R., Park, H. W., & Breazeal, C. (2018) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3202185.3202741 · Cited by 93 · see citing works
Manuel d'analyse qualitative. Analyser sans compter ni classer
Lejeune, C. (2014) · Cited by 76 · see citing works
Latour, B. (2000) · Réseaux · doi:10.3406/reso.2000.2211 · Cited by 71 · see citing works
Proceedings of IEEE Frontiers in Education Conference - FIE '93
(1993) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie.1993.405583 · Cited by 54 · see citing works
Eaton, E., Koenig, S., Schulz, C., Maurelli, F., Lee, J. R., Eckroth, J., Crowley, M., Freedman, R. G., Cardona-Rivera, R. E., Machado, T., et al. (2018) · AI Matters · doi:10.1145/3175502.3175509 · Cited by 47 · see citing works
Machine learning for middle-schoolers: Children as designers of machine-learning apps
Vartiainen, H., Toivonen, T., Jormanainen, I., Kahila, J., Tedre, M., & Valtonen, T. (2020) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie44824.2020.9273981 · Cited by 42 · see citing works
Fastrez, P. (2011) · Recherches en Communication · doi:10.14428/rec.v33i33.51793 · Cited by 31 · see citing works
Designing Digital Literacy Activities: An Interdisciplinary and Collaborative Approach
Henry, J., Hernalesteen, A., & Collard, A.-S. (2020) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie44824.2020.9274165 · Cited by 29 · see citing works
Self-citationDeveloping a Critical Robot Literacy for Young People from Conceptual Metaphors Analysis
Boraita, F., Henry, J., & Collard, A.-S. (2020) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie44824.2020.9273959 · Cited by 23 · see citing works
Self-citationMorale et technique : la fin des moyens
Latour, B. (1983) · SPIRE (Sciences Po) · Cited by 15 · see citing works
Kotilainen, S., & Kupiainen, R. (2015) · Cited by 2 · see citing works
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AI literacy in K-12: a systematic literature review
Casal-Otero, L., Catala, A., Fernández-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023) · International Journal of STEM Education · doi:10.1186/s40594-023-00418-7 · Cited by 724 · see citing works
“It should also address issues related to the ethical dimension, which is fundamental to the literacy of K-12 students as it enables them to understand the basic principles of AI (Henry et al., 2021).”
BackgroundIn search of artificial intelligence (AI) literacy in teacher education: A scoping review
Sperling, K., Stenberg, C.-J., McGrath, C., Åkerfeldt, A., Heintz, F., & Stenliden, L. (2024) · Computers and Education Open · doi:10.1016/j.caeo.2024.100169 · Cited by 498 · see citing works
Pedagogical Design of K-12 Artificial Intelligence Education: A Systematic Review
Yue, M., Jong, M. S.-Y., & Dai, Y. (2022) · Sustainability · doi:10.3390/su142315620 · Cited by 176 · see citing works
Lee, S. J., & Kwon, K. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100211 · Cited by 169 · see citing works
Chee, H., Ahn, S., & Lee, J. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13556 · Cited by 158 · see citing works
Systematic review of research on artificial intelligence in K–12 education (2017–2022)
Martin, F., Zhuang, M., & Schaefer, D. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100195 · Cited by 132 · see citing works
Kong, S.-C., Cheung, W. M.-Y., & Tsang, O. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11408-7 · Cited by 127 · see citing works
AI Literacy in K-12 and Higher Education in the Wake of Generative AI: An Integrative Review
Gu, X., & Ericson, B. J. (2025) · arXiv · doi:10.48550/arxiv.2503.00079 · Cited by 73
Gulen, H. N., & Kucuk, S. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-14020-1 · Cited by 3 · see citing works
- Authors
- Henry, J., Hernalesteen, A., & Collard, A.-S.
- Affiliations
- University of Namur (BE)
- Corresponding author
- Henry, Julie
- Venue
- KI - Künstliche Intelligenz
- Published
- 2021-06
- Volume
- 35
- Issue
- 2
- Pages
- 171-179
- ISSN
- 0933-1875 (ISSN-L)
- 1610-1987
- OpenAlex
- W3175422852
- Cited by
- 80
- References
- 29
- Citations by year
- 2026: 15 · 2025: 21 · 2024: 25 · 2023: 9 · 2022: 10
- FWCI
- 9.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Künstliche Intell. (publishedVersion)
- Repository of the University of Namur (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Arts and Humanities › Literature and Literary Theory › Literacy, Media, and Education
- Keywords
- artificial intelligence in education, K-12 education, role-playing games, concept of intelligence, AI literacy, machine learning, media literacy, computer science education, critical citizenship education, teacher professional development, interdisciplinary curriculum, junior high school students, elementary school students
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