Publications
Cole, K. K. (2026). AI literacy is not one thing: Conceptual, structural, and operative levels for higher education governance. Pedagogies: An International Journal, forthcoming. The framework page sets out the three levels, runs them on one decision, and offers a decision tool by audience.
Cole, K. K. (in progress). The Glitch Curriculum: How universities read themselves in the AI moment [Book manuscript]. The book reads institutional responses to generative AI as rhetoric, drawing on more than eighty governance documents. It develops glitch as a method for reading institutional texts, and strategic abstraction as a name for governance which disavows its own political work.
Anson, C. M., & Cole, K. K. (2026). Generative AI does all the work for the writer ✨ Writing with AI requires human rhetorical agency. In C. Basgier, A. Mills, M. Olejnik, M. Rodak, & S. Sharma (Eds.), Bad ideas about AI and writing: Generative practices for teaching, learning, and communication (pp. 119–128). The WAC Clearinghouse; University Press of Colorado. https://doi.org/10.37514/PER-B.2026.2777.2.15
Association for Writing Across the Curriculum (2025). Statement on Artificial Intelligence Writing Tools. Kirsti Cole is a co-author.
Presentations
The Unmediated Writer: What writing programs do when cognition is distributed. Carolinas WPA 19th Fall Retreat, Wildacres, Session C.1, October 6, 2026. The session page holds the slide deck, the companion document, the evidence, and the works cited.
Research behind the programs
The AI Policy Lab reads a corpus of 80 AI governance documents from 24 institutions, produced between 2023 and 2026. The policy repository workbook annotates the corpus and collects new documents for it.
The AI Literacies extension and the C²TC certificate build on the three-level framework. WOLF follows the University of Minnesota's Writing-Enriched Curriculum model, described on the About page.
Scholarship the framework draws on
A selected list of the sources behind the three-level framework, the AI Policy Lab, and the AI Literacies workbook. Each entry has been checked against its Crossref, publisher, or library catalog record. Preprints are marked.
Literacy as practice
Brandt, D. (1998). Sponsors of literacy. College Composition and Communication, 49(2), 165–185. https://doi.org/10.2307/358929
Goody, J., & Watt, I. (1963). The consequences of literacy. Comparative Studies in Society and History, 5(3), 304–345. https://doi.org/10.1017/S0010417500001730
Keefe, E. B., & Copeland, S. R. (2011). What is literacy? The power of a definition. Research and Practice for Persons with Severe Disabilities, 36(3–4), 92–99. https://doi.org/10.2511/027494811800824507
Scribner, S. (1984). Literacy in three metaphors. American Journal of Education, 93(1), 6–21. https://doi.org/10.1086/443783
Scribner, S., & Cole, M. (1978). Unpackaging literacy. Social Science Information, 17(1), 19–40. https://doi.org/10.1177/053901847801700102
Street, B. (2003). What's "new" in New Literacy Studies? Current Issues in Comparative Education, 5(2), 77–91. https://doi.org/10.52214/cice.v5i2.11369
Multiliteracies and critical literacy
Cope, B., & Kalantzis, M. (2009). "Multiliteracies": New literacies, new learning. Pedagogies: An International Journal, 4(3), 164–195. https://doi.org/10.1080/15544800903076044
Elmborg, J. (2006). Critical information literacy: Implications for instructional practice. The Journal of Academic Librarianship, 32(2), 192–199. https://doi.org/10.1016/j.acalib.2005.12.004
Janks, H. (2002). Critical literacy: Beyond reason. Australian Educational Researcher, 29(1), 7–26. https://doi.org/10.1007/BF03219767
New London Group. (1996). A pedagogy of multiliteracies: Designing social futures. Harvard Educational Review, 66(1), 60–93. https://doi.org/10.17763/haer.66.1.17370n67v22j160u
Selber, S. A. (2004). Multiliteracies for a digital age. Southern Illinois University Press.
Willis, A. I. (2023). Critical literacies: Ever-evolving. Literacy, 57(2), 198–205. https://doi.org/10.1111/lit.12331
Writing studies
Basgier, C., Mills, A., Olejnik, M., Rodak, M., & Sharma, S. (Eds.). (2026). Bad ideas about AI and writing: Generative practices for teaching, learning, and communication. The WAC Clearinghouse; University Press of Colorado. https://doi.org/10.37514/PER-B.2026.2777
Emig, J. (1977). Writing as a mode of learning. College Composition and Communication, 28(2), 122–128. https://doi.org/10.58680/ccc197716382
Inoue, A. B. (2015). Antiracist writing assessment ecologies: Teaching and assessing writing for a socially just future. The WAC Clearinghouse; Parlor Press. https://doi.org/10.37514/PER-B.2015.0698
Sharma, S. (2026). AI knows everything ✨ AI can perpetuate ignorance, prejudices, and epistemic-rhetorical harms globally. In C. Basgier, A. Mills, M. Olejnik, M. Rodak, & S. Sharma (Eds.), Bad ideas about AI and writing: Generative practices for teaching, learning, and communication (pp. 23–28). The WAC Clearinghouse; University Press of Colorado. https://doi.org/10.37514/PER-B.2026.2777.2.02
Yancey, K. B., Robertson, L., & Taczak, K. (2014). Writing across contexts: Transfer, composition, and sites of writing. Utah State University Press. https://doi.org/10.7330/9780874219388
Leveled models of literacy
National Research Council. (1999). Being fluent with information technology. National Academies Press. https://doi.org/10.17226/6482
Nutbeam, D. (2000). Health literacy as a public health goal. Health Promotion International, 15(3), 259–267. https://doi.org/10.1093/heapro/15.3.259
AI literacy frameworks
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). https://doi.org/10.1145/3313831.3376727
Miao, F., Shiohira, K., & Lao, N. (2024). AI competency framework for students. UNESCO. https://doi.org/10.54675/JKJB9835
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041
Tully, S. M., Longoni, C., & Appel, G. (2025). Lower artificial intelligence literacy predicts greater AI receptivity. Journal of Marketing, 89(5), 1–20. https://doi.org/10.1177/00222429251314491
AI literacy research and measurement
Almatrafi, O., Johri, A., & Lee, H. (2024). A systematic review of AI literacy conceptualization, constructs, and implementation and assessment efforts (2019–2023). Computers and Education Open, 6, Article 100173. https://doi.org/10.1016/j.caeo.2024.100173
Arias Hernández, R., & Rockembach, M. (2025). Building trustworthy AI solutions: Integrating artificial intelligence literacy into records management and archival systems. AI & Society, 40(6), 4265–4282. https://doi.org/10.1007/s00146-025-02194-0
Bewersdorff, A., Hornberger, M., Nerdel, C., & Schiff, D. S. (2025). AI advocates and cautious critics: How AI attitudes, AI interest, use of AI, and AI literacy build university students' AI self-efficacy. Computers and Education: Artificial Intelligence, 8, Article 100340. https://doi.org/10.1016/j.caeai.2024.100340
Birhanu, E. T., Tefera, G. W., Tilwani, S. A., Terefe, B., & Shiferaw, Y. G. (2026). Adapting and validating an artificial intelligence literacy scale for Ethiopian higher education using structural equation modeling. Discover Artificial Intelligence, 6(1), Article 544. https://doi.org/10.1007/s44163-026-01149-8
Bozkurt, A. (2024). Why generative AI literacy, why now and why it matters in the educational landscape? Kings, queens and GenAI dragons. Open Praxis, 16(3), 283–290. https://doi.org/10.55982/openpraxis.16.3.739
Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). MAILS - Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies. Computers in Human Behavior: Artificial Humans, 1(2), Article 100014. https://doi.org/10.1016/j.chbah.2023.100014
Casal-Otero, L., Catala, A., Fernández-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023). AI literacy in K-12: A systematic literature review. International Journal of STEM Education, 10(1), Article 29. https://doi.org/10.1186/s40594-023-00418-7
Chiu, T. K. F. (2026). Human-Centric Artificial Intelligence Pedagogy (HCAP) framework developed from TPACK through integration of artificial intelligence literacy and competency. Interactive Learning Environments. Advance online publication. https://doi.org/10.1080/10494820.2026.2615818
Chiu, T. K. F., Chen, Y., Yau, K. W., Chai, C.-s., Meng, H., King, I., Wong, S., & Yam, Y. (2024). Developing and validating measures for AI literacy tests: From self-reported to objective measures. Computers and Education: Artificial Intelligence, 7, Article 100282. https://doi.org/10.1016/j.caeai.2024.100282
Conrad, K., & Kamperman, S. (2025). Building critical AI literacy: An approach to generative AI. Thresholds in Education, 48(2), 142–158. https://eric.ed.gov/?id=EJ1483967
Druga, S., Otero, N., & Ko, A. J. (2022). The landscape of teaching resources for AI education. In Proceedings of the 27th ACM Conference on Innovation and Technology in Computer Science Education (Vol. 1, pp. 96–102). https://doi.org/10.1145/3502718.3524782
Druga, S., Vu, S. T., Likhith, E., & Qiu, T. (2019). Inclusive AI literacy for kids around the world. In Proceedings of FabLearn 2019 (pp. 104–111). https://doi.org/10.1145/3311890.3311904
García-Peñalvo, F. J. (2023). La percepción de la inteligencia artificial en contextos educativos tras el lanzamiento de ChatGPT: Disrupción o pánico [The perception of artificial intelligence in educational contexts after the launch of ChatGPT: Disruption or panic]. Education in the Knowledge Society, 24, Article e31279. https://doi.org/10.14201/eks.31279
Hackl, V., Müller, A. E., & Sailer, M. (2026). The AI literacy heptagon: A structured approach to AI literacy in higher education. Computers and Education: Artificial Intelligence, 10, Article 100540. https://doi.org/10.1016/j.caeai.2026.100540
Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S., & Huber, P. (2016). Artificial intelligence and computer science in education: From kindergarten to university. In 2016 IEEE Frontiers in Education Conference (FIE) (pp. 1–9). https://doi.org/10.1109/FIE.2016.7757570
Koch, M. J., Carolus, A., Wienrich, C., & Latoschik, M. E. (2024). Meta AI literacy scale: Further validation and development of a short version. Heliyon, 10(21), Article e39686. https://doi.org/10.1016/j.heliyon.2024.e39686
Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal, 54(2), 537–550. https://doi.org/10.1177/00336882231162868
Kong, S.-C., Cheung, W. M.-Y., & Zhang, G. (2021). Evaluation of an artificial intelligence literacy course for university students with diverse study backgrounds. Computers and Education: Artificial Intelligence, 2, Article 100026. https://doi.org/10.1016/j.caeai.2021.100026
Lintner, T. (2024). A systematic review of AI literacy scales. npj Science of Learning, 9(1), Article 50. https://doi.org/10.1038/s41539-024-00264-4
Ng, D. T. K., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu, S. K. W. (2024). Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach. British Journal of Educational Technology, 55(3), 1082–1104. https://doi.org/10.1111/bjet.13411
Roe, J., Furze, L., & Perkins, M. (2026). Digital plastic: A metaphorical framework for critical AI literacy in the multiliteracies era. Pedagogies: An International Journal, 21(2), 185–199. https://doi.org/10.1080/1554480X.2025.2557491
Roe, J., Perkins, M., & Furze, L. (2025). Reflecting reality, amplifying bias? Using metaphors to teach critical AI literacy. Journal of Interactive Media in Education, 2025(1), Article 18. https://doi.org/10.5334/jime.961
Shiri, A. (2024). Artificial intelligence literacy: A proposed faceted taxonomy. Digital Library Perspectives, 40(4), 681–699. https://doi.org/10.1108/DLP-04-2024-0067
Sperling, K., Stenberg, C.-J., McGrath, C., Åkerfeldt, A., Heintz, F., & Stenliden, L. (2024). In search of artificial intelligence (AI) literacy in teacher education: A scoping review. Computers and Education Open, 6, Article 100169. https://doi.org/10.1016/j.caeo.2024.100169
Su, J., Chen, X., Chu, S. K. W., & Hu, X. (2025). A scoping review of empirical research on AI literacy assessments. Educational Technology Research and Development, 73(5), 3105–3130. https://doi.org/10.1007/s11423-025-10515-9
Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019). Envisioning AI for K-12: What should every child know about AI? Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9795–9799. https://doi.org/10.1609/aaai.v33i01.33019795
Wang, H., Dang, A., Wu, Z., & Mac, S. (2024). Generative AI in higher education: Seeing ChatGPT through universities' policies, resources, and guidelines. Computers and Education: Artificial Intelligence, 7, Article 100326. https://doi.org/10.1016/j.caeai.2024.100326
Yim, I. H. Y., & Su, J. (2025). Artificial intelligence (AI) learning tools in K-12 education: A scoping review. Journal of Computers in Education, 12, 93–131. https://doi.org/10.1007/s40692-023-00304-9
Detection and naming
Barry, I., & Stephenson, E. (2025). The gendered, epistemic injustices of generative AI. Australian Feminist Studies, 40(123), 1–21. https://doi.org/10.1080/08164649.2025.2480927
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). ACM. https://doi.org/10.1145/3442188.3445922
de Roock, R. S. (2024). To become an object among objects: Generative artificial "intelligence," writing, and linguistic white supremacy. Reading Research Quarterly, 59(4), 590–608. https://doi.org/10.1002/rrq.569
Hunter, L. (2025). Compulsion beyond fairness: Towards a critical theory of technological abstraction in neural networks. AI & Society, 40(4), 2927–2936. https://doi.org/10.1007/s00146-024-02035-6
Inie, N., Zukerman, P., & Bender, E. M. (2026). De-anthropomorphizing "AI": From wishful mnemonics to accurate nomenclature. First Monday, 31(2). https://doi.org/10.5210/fm.v31i2.14366
Jabarian, B., & Imas, A. (2025). Artificial writing and automated detection (NBER Working Paper No. 34223). National Bureau of Economic Research. https://doi.org/10.3386/w34223
Jones, R. H. (2025). Culture machines. Applied Linguistics Review, 16(2), 753–762. https://doi.org/10.1515/applirev-2024-0188
Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7), 100779. https://doi.org/10.1016/j.patter.2023.100779
Shumailov, I., Shumaylov, Z., Zhao, Y., Papernot, N., Anderson, R., & Gal, Y. (2024). AI models collapse when trained on recursively generated data. Nature, 631(8022), 755–759. https://doi.org/10.1038/s41586-024-07566-y
Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., Popoola, O., Šigut, P., & Waddington, L. (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19(1), Article 26. https://doi.org/10.1007/s40979-023-00146-z
AI and learning
Abdulhai, M., White, I., Wan, Y., Qureshi, I., Leibo, J. Z., Kleiman-Weiner, M., & Jaques, N. (2026). How LLMs distort our written language (arXiv:2603.18161) [Preprint]. arXiv. https://arxiv.org/abs/2603.18161
Chen, Y., Wang, Y., Wüstenberg, T., Kizilcec, R. F., Fan, Y., Li, Y., Lu, B., Yuan, M., Zhang, J., Zhang, Z., Geldsetzer, P., Chen, S., & Bärnighausen, T. (2025). Effects of generative artificial intelligence on cognitive effort and task performance: Study protocol for a randomized controlled experiment among college students. Trials, 26(1), Article 244. https://doi.org/10.1186/s13063-025-08950-3
Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2025). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, 56(2), 489–530. https://doi.org/10.1111/bjet.13544
Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X.-H., Beresnitzky, A. V., Braunstein, I., & Maes, P. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task (arXiv:2506.08872) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2506.08872
Liu, G., Christian, B., Dumbalska, T., Bakker, M. A., & Dubey, R. (2026). AI assistance reduces persistence and hurts independent performance (arXiv:2604.04721) [Preprint]. arXiv. https://arxiv.org/abs/2604.04721
Marcoccia, C., Quattrociocchi, W., & Capraro, V. (2026). AI advice suppresses people's willingness to say "I don't know," even when the advice is wrong and accuracy is incentivized [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/5y6m4_v1
Shaw, S. D., & Nave, G. (2026). Thinking—fast, slow, and artificial: How AI is reshaping human reasoning and the rise of cognitive surrender [Preprint]. SSRN. https://doi.org/10.2139/ssrn.6097646
Strömberg, D., Lei, V., & Wu, Y. (2026). The generative AI learning penalty: Evidence from Chinese secondary education (CEPR Discussion Paper No. 21577). Centre for Economic Policy Research. https://cepr.org/publications/dp21577
Glitch and refusal
Benjamin, R. (2019). Race after technology: Abolitionist tools for the New Jim Code. Polity.
Benjamin, R. (2024). Imagination: A manifesto. W. W. Norton & Company.
Boyle, C. (2015). The rhetorical question concerning glitch. Computers and Composition, 35, 1–12. https://doi.org/10.1016/j.compcom.2015.01.003
Campt, T. M. (2017). Listening to images. Duke University Press. https://doi.org/10.1215/9780822373582
Lin, K. (2021). Do abstractions have politics? Toward a more critical algorithm analysis. In 2021 Conference on Research in Equitable and Sustained Participation in Engineering, Computing, and Technology (RESPECT). IEEE. https://doi.org/10.1109/RESPECT51740.2021.9620635
McPherson, T. (2012). Why are the digital humanities so white? or Thinking the histories of race and computation. In Debates in the digital humanities (pp. 139–160). University of Minnesota Press. https://doi.org/10.5749/minnesota/9780816677948.003.0017
McPherson, T. (2018). Feminist in a software lab: Difference + design. Harvard University Press.
Menkman, R. (2011). The glitch moment(um) (Network Notebooks 04). Institute of Network Cultures. https://doi.org/10.25969/mediarep/19296
Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press.
Rodríguez, Y. (2019). Pedagogies of refusal: What it means to (un)teach a student like me. Radical Teacher, 115, 5–12. https://doi.org/10.5195/rt.2019.672
Russell, L. (2020). Glitch feminism: A manifesto. Verso.
Simpson, A. (2014). Mohawk interruptus: Political life across the borders of settler states. Duke University Press. https://doi.org/10.1215/9780822376781
Steyerl, H. (2009). In defense of the poor image. e-flux journal, (10). https://www.e-flux.com/journal/10/61362/in-defense-of-the-poor-image/
Winner, L. (1980). Do artifacts have politics? Daedalus, 109(1), 121–136. https://www.jstor.org/stable/20024652
Method
Bowker, G. C., & Star, S. L. (1999). Sorting things out: Classification and its consequences. MIT Press. https://doi.org/10.7551/mitpress/6352.001.0001
Jabareen, Y. (2009). Building a conceptual framework: Philosophy, definitions, and procedure. International Journal of Qualitative Methods, 8(4), 49–62. https://doi.org/10.1177/160940690900800406
Schreiber, F., & Cramer, C. (2022). Towards a conceptual systematic review: Proposing a methodological framework. Educational Review. Advance online publication. https://doi.org/10.1080/00131911.2022.2116561
Smith, D. E. (2005). Institutional ethnography: A sociology for people. AltaMira.