AI Book Club: An Innovative Professional Development Model for AI Education
Open accessAbstract
This paper describes an AI Book Club as an innovative 20-hour professional development (PD) model designed to prepare teachers with AI content knowledge and an understanding of the ethical issues posed by bias in AI that are foundational to developing AI-literate citizens. The design of the intervention was motivated by a desire to manage the cognitive load of AI learning by spreading the PD program over several weeks and a desire to form and maintain a community of teachers interested in AI education during the COVID-19 pandemic. Each week participants spent an hour independently reading selections from an AI book, reviewing AI activities, and viewing videos of other educators teaching the activities, then met online for 1 hour to discuss the materials and brainstorm how they might adapt the materials for their classrooms. The participants in the AI Book Club were 37 middle school educators from 3 US school districts and 5 youth-serving organizations. The teachers are from STEM disciplines as well as Social Studies and Art. Eighty-nine percent were from underrepresented groups in STEM and CS. In this paper we describe the design of the AI Book Club, its implementation, and preliminary findings on teachers' impressions of the AI Book Club as a form of PD, thoughts about teaching AI in classrooms, and interest in continuing the book club model in the upcoming year. We conclude with recommendations for others interested in implementing a book club PD format for AI learning.
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Three courses (30%) specifically focused on ethical issues in AI and attempted to address aspects such as algorithmic bias or 'AI as a black box'.
“Three courses (30%) also specifically focused on ethical issues in AI and attempt to address aspects such as algorithmic bias or”
From Artificial intelligence literacy in higher and adult education: A scoping literature review (2022) · cites this paper as Lee et al., 2022
Section 5.2 reports that teachers considered ethics and bias important topics in AI education, and describes one teacher’s plan to teach about bias. The paper does not report that three courses (30%) focused on ethics or that those courses addressed AI as a black box. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe literature lacks consensus on how AI training programs for pre-service teachers should be developed.
“However, the literature still lacks consensus on how these training programs should be developed (Lee et al., 2022).”
From Integrating artificial intelligence into preservice teacher education: enhancing AI literacy and digital material design competency (2026) · cites this paper as Lee et al., 2022
The Introduction (section 1; page not provided) says little is known about how to prepare teachers to offer AI education, supporting the claim that guidance on developing training is limited. But the paper discusses professional development for K–12 teachers, not specifically programs for pre-service teachers, and it does not assess consensus in that literature. (LLM, conf=0.85)
Experimental — accuracy not yet measuredArticles mostly either described curricular developments or specific courses trying to foster AI literacy (11 studies, 37%).
“We found that articles mostly either described curricular developments (e.g., plans on how to structure curricula regarding AI literacy in university) or specific courses trying to foster AI literacy (11 studies, 37%; Lee et al., 2022;Lee, 2021a;Kandlhofer et al., 2016;Xu & Babaian, 2021;Park & Suh, 2021;Kong et al., 2021;Vazhayil et al., 2019;Rodríguez-García et al., 2020;Shih et al., 2021;Lin et”
From Artificial intelligence literacy in higher and adult education: A scoping literature review (2022) · cites this paper as Lee et al., 2022
The United States of America and Canada make up 43% of all research published in this field.
“The United States of America and Canada make up for 43% of all research published in this field (Charow et al., 2021;Faruqe et al., 2021;Lee et al., 2022;Long & Magerko, 2020;McCoy et al., 2020;Pucchio et al., 2021;Register & Ko, 2020;Teng et al., 2022;Vazhayil et al., 2019;Wiljer et al., 2021;Wiljer & Hakim, 2019;Wood et al., 2021;Xu & Babaian, 2021).”
From Artificial intelligence literacy in higher and adult education: A scoping literature review (2022) · cites this paper as Lee et al., 2022
The Method section describes a study of educators’ experiences with the AI Book Club, and §3.2 reports the study’s participant demographics. A search for “43%” and for geographic distributions of research publications found no passage assessing the share of research published by the United States and Canada. (LLM, conf=0.76)
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Cites
Cognitive Load During Problem Solving: Effects on Learning
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Cited by
Sorted by citations
Artificial intelligence literacy in higher and adult education: A scoping literature review
Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100101 · Cited by 658 · see citing works
Gulen, H. N., & Kucuk, S. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-14020-1 · Cited by 3 · see citing works
- Authors
- Lee, I., Zhang, H., Moore, K., Zhou, X., Perret, B., Cheng, Y., Zheng, R., & Pu, G.
- Affiliations
- Massachusetts Institute of Technology (US)
- Boston College (US)
- University of Rochester (US)
- Venue
- Proceedings of the 53rd ACM Technical Symposium on Computer Science Education
- Publisher
- ACM
- Published
- 2022-02-22
- Pages
- 202-208
- OpenAlex
- W4293227534
- Cited by
- 37
- References
- 3
- Citations by year
- 2026: 4 · 2025: 17 · 2024: 14 · 2023: 2
- FWCI
- 10.76
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Technical Symposium on Computer Science Education (SIGCSE) (publishedVersion, cc-by-nc-sa)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Funders
- National Science Foundation (DRL-2048746)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Education › Teacher Education and Leadership Studies
- Keywords
- teacher professional development, AI literacy, artificial intelligence in education, algorithmic bias, AI ethics, cognitive load, junior high school teachers, underrepresented groups in STEM, online teacher communities, COVID-19 pandemic, teacher perceptions
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