The DOI matches its Crossref record; 47 of the 50 references it cites were found in a registry or an index.
Integrating Ethics and Career Futures with Technical Learning to Promote AI Literacy for Middle School Students: An Exploratory Study
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source doesn’t support the claim as written.
In the citing paper
“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
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Designing and Conducting Mixed Methods Research
Robinson, P. M. (2007) · Australian and New Zealand Journal of Public Health · doi:10.1111/j.1753-6405.2007.00096.x · Cited by 23,362
Whose culture has capital? A critical race theory discussion of community cultural wealth
Yosso *, T. J. (2005) · Race Ethnicity and Education · doi:10.1080/1361332052000341006 · Cited by 8,663 · cited 1×
“This provides further evidence of student engagement as he was active in leveraging existing familial and aspirational capital (Yosso, 2005) to make sense of AI and refine his existing perceptions of AI.”
Background(2006) · Cited by 7,081
(2018) · Cited by 3,510
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 2×
“This approach also aligns well with the use of embodied interaction proposed in the AI Literacy framework (Design Consideration 2: Embodied Interactions, Long & Magerko, 2020) where learners can “put themselves in the agent’s shoes as a way of making sense of the agent’s reasoning process” (p. 598).”
BackgroundMethodThe Psychology of Working Theory.
Duffy, R. D., Blustein, D. L., Diemer, M. A., & Autin, K. L. (2016) · Journal of Counseling Psychology · doi:10.1037/cou0000140 · Cited by 1,209
Planning Early for Careers in Science
Tai, R. H., Liu, C. Q., Maltese, A. V., & Fan, X. (2006) · Science · doi:10.1126/science.1128690 · Cited by 1,102
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
Coeckelbergh, M. (2020) · The MIT Press eBooks · doi:10.7551/mitpress/12549.001.0001 · Cited by 865
Wang, Y., & Wang, Y. (2019) · Interactive Learning Environments · doi:10.1080/10494820.2019.1674887 · Cited by 830
Research works printed but not linked: 4
- Ali, S., Payne, B. H., Williams, R., Park, H. W., & Breazeal, C. (2019). Constructionism, ethics, and creativity: Developing primary and middle school artificial intelligence education. In International workshop on education in artificial intelligence K-12 (EDUAI’19) (pp. 1–4).
- Badham, R., Clegg, C., & Wall, T. (2000). Socio-technical theory. Handbook of Ergonomics. John Wiley.
- 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.
- Torrey, L. (2012). Teaching problem-solving in algorithms and AI. 3rd Symposium on Educational Advances in Artificial Intelligence.
Cited by
Sorted by citations
Walter, Y. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00448-3 · Cited by 1,113
“It represents a fundamental shift in education, where understanding AI becomes as crucial as reading, writing, and arithmetic (Zhang et al., 2023). current state of AI literacy in education reflects a burgeoning field, ripe with potential yet facing the challenges of early adoption.”
What are artificial intelligence literacy and competency? A comprehensive framework to support them
Chiu, T. K. F., Ahmad, Z., Ismailov, M., & Sanusi, I. T. (2024) · Computers and Education Open · doi:10.1016/j.caeo.2024.100171 · Cited by 570
Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2023.100014 · Cited by 488
Ng, D. T. K., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu, S. K. W. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13411 · Cited by 390
Ahmad, S. F., Han, H., Alam, M. M., Rehmat, M. K., Irshad, M., Arraño-Muñoz, M., & Ariza‐Montes, A. (2023) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-01787-8 · Cited by 553
Retracted
Metadata
- 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
- 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)
- PMC
- PMC9084886
- OpenAlex
- W4229441880
- Cited by
- 396
- References
- 50
- Citations by year
- 2026: 106 · 2025: 137 · 2024: 107 · 2023: 39
- FWCI
- 71.75
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- 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
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review