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Cited by 396Artificial Intelligence in EducationEthics and Social Impacts of AITeaching and Learning Programming

Integrating Ethics and Career Futures with Technical Learning to Promote AI Literacy for Middle School Students: An Exploratory Study

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Zhang, Helen, Lee, Irene, Ali, Safinah, DiPaola, Daniella, Cheng, Yihong, Breazeal, Cynthia·2023·International Journal of Artificial Intelligence in Education
DOI 10.1007/s40593-022-00293-3PMC PMC9084886
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Abstract

The rapid expansion of artificial intelligence (AI) necessitates promoting AI education at the K-12 level. However, educating young learners to become AI literate citizens poses several challenges. The components of AI literacy are ill-defined and it is unclear to what extent middle school students can engage in learning about AI as a sociotechnical system with socio-political implications. In this paper we posit that students must learn three core domains of AI: technical concepts and processes, ethical and societal implications, and career futures in the AI era. This paper describes the design and implementation of the Developing AI Literacy (DAILy) workshop that aimed to integrate middle school students' learning of the three domains. We found that after the workshop, most students developed a general understanding of AI concepts and processes (e.g., supervised learning and logic systems). More importantly, they were able to identify bias, describe ways to mitigate bias in machine learning, and start to consider how AI may impact their future lives and careers. At exit, nearly half of the students explained AI as not just a technical subject, but one that has personal, career, and societal implications. Overall, this finding suggests that the approach of incorporating ethics and career futures into AI education is age appropriate and effective for developing AI literacy among middle school students. This study contributes to the field of AI Education by presenting a model of integrating ethics into the teaching of AI that is appropriate for middle school students.

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  • K-12 education prioritizes AI ethics to address ethical issues and dilemmas in AI development and deployment, promoting responsible AI usage.

    “More importantly, K-12 education prioritizes AI ethics to address ethical issues and dilemmas in AI development and deployment, promoting responsible AI usage (Kong et al., 2023;Zhang et al., 2023).”

    From A Competency Framework for AI Literacy: Variations by Different Learner Groups and an Implied Learning Pathway (2025) · cites this paper as Zhang et al., 2023

    In the Theoretical Framework section, the paper says most K–12 AI teaching focuses on technical aspects and few programs emphasize ethics; it reports that only 13 of 49 reviewed curricula taught ethics. The Introduction argues that ethics should be included to help students address ethical issues, but that recommendation does not support the claim that K–12 education already prioritizes it. (LLM, conf=0.88)

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  • Understanding AI is as crucial as reading, writing, and arithmetic.

    “It represents a fundamental shift in education, where understanding AI becomes as crucial as reading, writing, and arithmetic (Zhang et al., 2023).”

    From Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education (2024) · cites this paper as Zhang et al., 2023

    The Introduction (page not provided) argues that AI’s expansion makes K–12 AI education necessary and that all students should be prepared for the AI era. It does not say that understanding AI is as crucial as reading, writing, and arithmetic; that comparison overstates the source. (LLM, conf=0.83)

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Cites

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3 of the 50 references couldn’t be matched to a known paper.

Research works printed but not linked: 5

  1. Badham, R., Clegg, C., & Wall, T. (2000). Socio-technical theory. Handbook of Ergonomics. John Wiley.
  2. Coeckelbergh M AI ethics 2020 MIT Press
  3. Emery FE Trist EL Churchman CW Verhulst M Socio-technical systems Management science models and techniques 1960 Pergamon Press
  4. Payne, B. H. (2020). Can my algorithm be my opinion?: An AI+ ethics curriculum for middle school students (Unpublished master’s thesis). Massachusetts Institute of Technology, Cambridge.
  5. Torrey, L. (2012). Teaching problem-solving in algorithms and AI. 3rd Symposium on Educational Advances in Artificial Intelligence.

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Authors
Zhang, H., Lee, I., Ali, S., DiPaola, D., Cheng, Y., & Breazeal, C.
Affiliations
  • Boston College (US)
  • Massachusetts Institute of Technology (US)
  • MIT Media Lab (US)
Corresponding author
Zhang, Helen
ORCID
Venue
International Journal of Artificial Intelligence in Education
Publisher
Elsevier BV
Published
2023-06
Volume
33
Issue
2
Pages
290-324
ISSN
1560-4292 (ISSN-L)
DOI
10.1007/s40593-022-00293-3
PMC
PMC9084886
OpenAlex
W4229441880
Cited by
396
References
50
Citations by year
2026: 106 · 2025: 137 · 2024: 107 · 2023: 39 · 2022: 7
FWCI
71.75
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Artificial Intelligence in Education (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
Funders
Directorate for Education and Human Resources (2022502)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
Keywords
AI literacy, artificial intelligence in education, junior high school students, AI ethics, machine learning bias, bias mitigation, supervised learning, logical systems, K-12 education, socio-technical systems

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