For seminar
Lenses, cases & questions
EPA 701 lenses
What each lens surfaces
Policy as political, not technical
Ball, 1998; Horsford et al., 2019
Chan and Hughes et al. treat AI policy as design; Dunnigan et al. show boards, teachers, parents, and firms shaping a non-decision.
Markets and edu-business
Ball, 1998; Horsford et al., 2019
EO 14277 relies on public-private partnerships; universities buy licenses; Dunnigan et al. name a “neoliberal landscape.”
Policy transfer and the Western horizon
Silova et al., 2020
Chan builds from UNESCO guidance; EU rules travel; Dunnigan et al. flag an “English language, western culture orientation.”
Racialized organizations and expectations
Rist, 1970; Ray, 2019
Detectors flagged non-native English essays as AI-written 61.3% of the time, on average (Liang et al., 2023).
Critical policy analysis and intersectionality
Diem et al., 2014; Cho et al., 2013
Who benefits, and who is left out? Equity runs on one axis, and students rarely count as knowers.
Taken for granted
Five assumptions worth questioning
Policy is a written rule
Dunnigan et al.'s leaders made policy by not writing one: the acceptable-use policy “covers it.”
The stakeholders are adults
Only Chan surveyed students, and no framework gives them a role.
Equity means access to tools
Price, language, and disability cut differently and together (Cho et al., 2013).
English and Western defaults are neutral
“ChatGPT is mainly based on literature written in English” (participant in Hughes et al., 2025).
What counts as “human” is fixed
Henriksen et al. hedge: GenAI “cannot yet replicate” it.
Cases · composite, not real events
Four policy cases
A
Policy by Friday
An Arizona K-8 charter board orders an AI policy within a week. The principal says the acceptable-use policy already covers it.
Dunnigan et al. · Chan
B
Flagged
A detector flags a multilingual ninth grader's essay as AI-written. The teacher has to decide what happens next.
Chan · Henriksen et al.
C
The license
A state funds an AI tutor for districts that apply. Districts without grant writers never apply.
Dunnigan et al. · Hughes et al.
D
Borrowed framework
A U.S. university adopts Chan's Hong Kong framework word for word, owners and all.
Chan · Hughes et al.
At your table
- How is the problem defined, and by whom?
- Which reading would the decision-maker reach for? Which reading objects?
- Who is missing, and what changes if they decide?
The empty chair
Every reading names equity. None gives students a seat.
Chan surveyed 457 students, then gave them no dimension of her framework. Students with disabilities appear once: a leader's curiosity about “neurodiverse learners.” Hughes et al.'s seven structural-model experts were all academics.
Close
Questions to take with us
1
When is not making AI policy itself a policy, and who pays for the delay?
2
If firms and universities sign the deals, where does public accountability live?
3
What would evidence of a good AI policy look like to the students it governs?
Sources
References
- Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20, Article 38. https://doi.org/10.1186/s41239-023-00408-3
- Dunnigan, J., Henriksen, D., Mishra, P., & Lake, R. (2023). “Can we just please slow it all down?” School leaders take on ChatGPT. TechTrends, 67, 878–884. https://doi.org/10.1007/s11528-023-00914-1
- Henriksen, D., Woo, L., Mishra, P., Oster, N., & McCaleb, L. (2026). The human side of human-AI creative collaboration: Expert perspectives on creativity, agency, and learning. In Proceedings of SITE 2026 (pp. 2091–2095).
- Hughes, L., Malik, T., Dettmer, S., Al-Busaidi, A. S., & Dwivedi, Y. K. (2025). Reimagining higher education: Navigating the challenges of generative AI adoption. Information Systems Frontiers. https://doi.org/10.1007/s10796-025-10582-6
- Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7), Article 100779. https://doi.org/10.1016/j.patter.2023.100779
- Al Ali, A., Helcl, J., & Libovický, J. (2026). Different time, different language: Revisiting the bias against non-native speakers in GPT detectors [Preprint]. arXiv. https://arxiv.org/abs/2602.05769
- Ball, S. J. (1998). Big policies/small world: An introduction to international perspectives in education policy. Comparative Education, 34(2), 119–130.
- Cho, S., Crenshaw, K. W., & McCall, L. (2013). Toward a field of intersectionality studies: Theory, applications, and praxis. Signs, 38(4), 785–810.
- Diem, S., Young, M. D., Welton, A. D., Mansfield, K. C., & Lee, P.-L. (2014). The intellectual landscape of critical policy analysis. International Journal of Qualitative Studies in Education, 27(9), 1068–1090.
- Horsford, S. D., Scott, J. T., & Anderson, G. L. (2019). The politics of education policy in an era of inequality: Possibilities for democratic schooling. Routledge.
- Ray, V. (2019). A theory of racialized organizations. American Sociological Review, 84(1), 26–53.
- Rist, R. C. (1970). Student social class and teacher expectations: The self-fulfilling prophecy in ghetto education. Harvard Educational Review, 40(3), 411–451.
- Silova, I., Rappleye, J., & Auld, E. (2020). Beyond the western horizon: Rethinking education, values, and policy transfer. In Handbook of education policy studies (Vol. 1, pp. 3–29). Springer.
- Exec. Order No. 14277. (2025, April 23). Advancing artificial intelligence education for American youth. The White House.
- Arizona Institute for Education and the Economy. (2024). Generative artificial intelligence education guidance for Arizona educators. Northern Arizona University.
- Palmer, K. (2025, February 5). Tech giants partner with Cal State system to advance “equitable” AI training. Inside Higher Ed.