extended-abstract
- Authors:
- Eric Greenwald Lawrence Hall of Science, University of California, Berkeley, United States
Lawrence Hall of Science, University of California, Berkeley, United States
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- Ari Krakowski Lawrence Hall of Science, University of California, Berkeley, United States
Lawrence Hall of Science, University of California, Berkeley, United States
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- Timothy Hurt Lawrence Hall of Science, University of California, United States
Lawrence Hall of Science, University of California, United States
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- Kelly Grindstaff Lawrence Hall of Science, UC Berkeley, United States
- Ning Wang Institute for Creative Technologies, University of Southern California, United States
Institute for Creative Technologies, University of Southern California, United States
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IDC '24: Proceedings of the 23rd Annual ACM Interaction Design and Children ConferenceJune 2024Pages 789–793https://doi.org/10.1145/3628516.3659395
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IDC '24: Proceedings of the 23rd Annual ACM Interaction Design and Children Conference
It's like I'm the AI: Youth Sensemaking About AI through Metacognitive Embodiment
Pages 789–793
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ABSTRACT
The increasing presence and importance of Artificial Intelligence (AI) in our society has led to calls for its inclusion at all levels of education. However, the field is only beginning to understand what how AI learning experiences may be designed to be effective and developmentally appropriate, especially for young children. One challenge children encounter is in conceptualizing the “intelligence” of AI while they are still developing a metacognitive model of their own human intelligence. To investigate potential ways to address this, we developed a strategy, metacognitive embodiment, through which children are supported to (a) elicit a mental model of their own intelligent performance on a task and (b) compare that elicited model to an AI designed to accomplish the same task. From this study we found evidence suggesting that engaging children in metacognitive tasks in coordination with AI learning experiences (where the AI performs an analogous task) better positioned them for sensemaking about the AI’s intelligence.
References
- AI4K12. 2022. Big idea 4 – natural interaction. https://ai4k12.org/big-idea-4-natural-interaction/Google Scholar
- Safinah Ali, Daniella DiPaola, Irene Lee, Jenna Hong, and Cynthia Breazeal. 2021. Exploring generative models with middle school students. In Proceedings of the 2021 CHI conference on human factors in computing systems. 1–13.Google ScholarDigital Library
- Sue Allen, PatriciaB Campbell, LynnD Dierking, BarbaraN Flagg, AlanJ Friedman, Cecilia Garibay, and DavidA Ucko. 2008. Framework for evaluating impacts of informal science education projects. In Report from a National Science Foundation Workshop. The National Science Foundation, Division of Research on Learning in Formal and Informal Settings.Google Scholar
- Yun Dai, Ziyan Lin, Ang Liu, and Wenlan Wang. 2024. An embodied, analogical and disruptive approach of AI pedagogy in upper elementary education: An experimental study. British Journal of Educational Technology 55, 1 (2024), 417–434.Google ScholarCross Ref
- SusanneA Denham. 2006. Social-emotional competence as support for school readiness: What is it and how do we assess it?Early education and development 17, 1 (2006), 57–89.Google Scholar
- SusanneA Denham. 2007. Dealing with feelings: how children negotiate the worlds of emotions and social relationships.Cognitie, Creier, Comportament/Cognition, Brain, Behavior 11, 1 (2007).Google Scholar
- Paul Dourish. 2004. Where the action is: the foundations of embodied interaction. MIT press.Google Scholar
- Stefania Druga, SarahT Vu, Eesh Likhith, and Tammy Qiu. 2019. Inclusive AI literacy for kids around the world. In Proceedings of FabLearn 2019. 104–111.Google ScholarDigital Library
- Eric Greenwald, Maxyn Leitner, and Ning Wang. 2021. Learning Artificial Intelligence: Insights into How Youth Encounter and Build Understanding of AI Concepts. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol.35. 15526–15533.Google ScholarCross Ref
- Clint Heinze, Janet Haase, and Helen Higgins. 2010. An Action Research Report from a Multi-Year Approach to Teaching Artificial Intelligence at the K-6 Level. Proceedings of the National Conference on Artificial Intelligence 3 (07 2010), 1890–1895. https://doi.org/10.1609/aaai.v24i3.18830Google ScholarCross Ref
- McKinseyGlobal Institute. 2017. A future that works: automation, employment, and productivity. https://www.mckinsey.com//media/mckinsey/featured%20insights/Digital%20Disruption/Harnessing%20automation%20for%20a%20future%20that%20works/MGI-A-future-that-works-Executive-summary.ashxGoogle Scholar
- Vanessa LoBue and Cat Thrasher. 2015. The Child Affective Facial Expression (CAFE) set: Validity and reliability from untrained adults. Frontiers in psychology 5 (2015), 1532.Google Scholar
- Duri Long and Brian Magerko. 2020. What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI conference on human factors in computing systems. 1–16.Google ScholarDigital Library
- Duri Long, Aadarsh Padiyath, Anthony Teachey, and Brian Magerko. 2021. The Role of Collaboration, Creativity, and Embodiment in AI Learning Experiences. In Creativity and Cognition. 1–10.Google Scholar
- Lorraine McLeod. 1997. Young children and metacognition: Do we know what they know they know? And if so, what do we do about it?Australasian Journal of Early Childhood 22, 2 (1997), 6–11.Google Scholar
- WouterJ Rijke, Lars Bollen, TessaHS Eysink, and JosLJ Tolboom. 2018. Computational thinking in primary school: An examination of abstraction and decomposition in different age groups. Informatics in education 17, 1 (2018), 77–92.Google Scholar
- Gregory Schraw and David Moshman. 1995. Metacognitive theories. Educational psychology review 7, 4 (1995), 351–371.Google Scholar
- Pratim Sengupta, Amanda Dickes, and Amy Farris. 2018. Toward a phenomenology of computational thinking in STEM education. Computational thinking in the STEM disciplines (2018), 49–72.Google Scholar
- Karen Sullenger. 2006. Beyond School Walls: Informal Education and the Culture of Science.Education Canada 46, 3 (2006), 15–18.Google Scholar
- LucyG Sullivan. 1995. Myth, metaphor and hypothesis: how anthropomorphism defeats science. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 349, 1328 (1995), 215–218.Google Scholar
- Henriikka Vartiainen, Matti Tedre, and Teemu Valtonen. 2020. Learning machine learning with very young children: Who is teaching whom?International journal of child-computer interaction 25 (2020), 100182.Google ScholarDigital Library
- MarcelVJ Veenman, BernadetteHAM Van Hout-Wolters, and Peter Afflerbach. 2006. Metacognition and learning: Conceptual and methodological considerations. Metacognition and learning 1 (2006), 3–14.Google Scholar
- David Whitebread, Penny Coltman, DeborahPino Pasternak, Claire Sangster, Valeska Grau, Sue Bingham, Qais Almeqdad, and Demetra Demetriou. 2009. The development of two observational tools for assessing metacognition and self-regulated learning in young children. Metacognition and learning 4, 1 (2009), 63–85.Google Scholar
- LisaA Williams, SarahF Brosnan, and Zanna Clay. 2020. Anthropomorphism in comparative affective science: Advocating a mindful approach. Neuroscience & Biobehavioral Reviews 115 (2020), 299–307.Google ScholarCross Ref
- CliveDL Wynne. 2007. What are animals? Why anthropomorphism is still not a scientific approach to behavior. Comparative Cognition & Behavior Reviews 2 (2007).Google Scholar
- Weipeng Yang. 2022. Artificial Intelligence education for young children: Why, what, and how in curriculum design and implementation. Computers and Education: Artificial Intelligence 3 (2022), 100061.Google ScholarCross Ref
- Abigail Zimmermann-Niefield, Makenna Turner, Bridget Murphy, ShaunK Kane, and RBenjamin Shapiro. 2019. Youth learning machine learning through building models of athletic moves. In Proceedings of the 18th ACM international conference on interaction design and children. 121–132.Google ScholarDigital Library
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It's like I'm the AI: Youth Sensemaking About AI through Metacognitive Embodiment
Applied computing
Education
Interactive learning environments
Social and professional topics
Professional topics
Computing education
Informal education
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IDC '24: Proceedings of the 23rd Annual ACM Interaction Design and Children Conference
June 2024
1049 pages
ISBN:9798400704420
DOI:10.1145/3628516
Copyright © 2024 Owner/Author
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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- Published: 17 June 2024
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- AI literacy
- K-5 education
- embodied interaction
- informal learning
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