Making Generative AI Expectations Visible: Extending the TILT Framework for Transparent Assignment Design
DOI:
https://doi.org/10.24059/olj.v30i3.6398Keywords:
TILTai Framework, genAI transparency, generative artificial intelligence, transparent assignment design, scale-based measurementAbstract
Instructor practices in higher education for setting assignment-level expectations have been meaningfully shaped by the Transparency in Learning and Teaching (TILT) Framework (Winkelmes, 2023a, 2023b) which encourages the development of clear guidance for students around each assignment’s purpose, core tasks, and assessment criteria. The TILT Framework has been widely adopted by faculty and instructional support professionals (Winkelmes et al., n.d.) as a methodology that can align instructor expectations with student understanding and effort on a given assignment. The line of applied research contained in this article extends the existing TILT model to provide students with transparent genAI guidance. The authors introduce their TILTai Framework that is inclusive of instructors’ intentions surrounding genAI use. They then describe a pilot faculty development model that was implemented using their TILTai Framework (Wiss et al., 2025). The authors introduce a new and preliminarily validated instrument for measuring student perceptions of a particular assignment’s transparency related to genAI (the TILTai Scale) and describe a research study conducted across a series of graduate-level courses in which the instrument was piloted. This study also considered the influence that student perceptions of genAI transparency may have on commonly held concerns regarding genAI (Singer-Freeman et al., 2025). Results from the TILTai Scale, perceptions of genAI transparency as measured by the TILTai Scale, their influence on commonly held student concerns, and the efficacy and potential future applications of the TILTai Framework are discussed.
References
Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman.
Barba, L. A. (2025). Experience embracing genAI in an engineering computations course: What went wrong and what next. https://doi.org/10.1109/MC.2025.3572654
Brown, E. A., Kronenburg, M., Steeley, A., & Tagbor, D. (2025). A case study using the transparency framework and artificial intelligence to promote effective writing and student success in a writing-intensive course. Journal of Health Administration Education, 41(2), 183–210.
Brown, E. A. (2024, March). Using the transparency framework and artificial intelligence to promote effective writing and student success in a writing-intensive course [Conference presentation]. 2024 ASPPH Annual Meeting for Academic Public Health, Arlington, VA, United States.
Chirikov, I., Smirnov, I., & Kizilcec, R. F. (2026). Generative AI use and misuse call for assessment reform in higher education. Science, 392(6800), 818–820.
Cotelli Kureth, S., Paliot, E., & Zink, S. (2025). Fostering transparency: a critical introduction of generative AI in students’ assignments. Language Learning in Higher Education, 15(1), 63–85. https://doi.org/10.1515/cercles-2024-0105
Corbin, T., Dawson, P., Nicola-Richmond, K., & Partridge, H. (2025). “Where’s the line? It’s an absurd line”: Towards a framework for acceptable uses of AI in assessment. Assessment & Evaluation in Higher Education, 50(5), 705–717. https://doi.org/10.1080/02602938.2025.2456207
Damm, C., & Eaton, L. (2026, March 18). From prompt to practice: A framework for transparent GenAI use in higher education. EDUCAUSE Review. https://er.educause.edu/articles/2026/3/from-prompt-to-practice-a-framework-for-transparent-genAI-use-in-higher-education
Du, Y., & He, H. (2026). Enabling and inhibitory pathways of university students' willingness to disclose AI use: A cognition-affect-conation perspective. arXiv preprint. https://arxiv.org/pdf/2604.21733
Friberg-Fort, J. C., & Giovagnoli, D. (2026). Radical transparency as an antidote to unclear expectations for generative AI use. In N. Pritts (Ed.), Empowering educational development and faculty growth with AI (pp. 273–288). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-4129-3.ch010
Furze, L., Perkins, M., Roe, J., & MacVaugh, J. (2024). The AI Assessment Scale (AIAS) in action: A pilot implementation of GenAI-supported assessment. Australasian Journal of Educational Technology, 40(4), 38–55. https://doi.org/10.14742/ajet.9434
Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6. https://doi.org/10.3390/soc15010006
Gonsalves, C. (2026). The transparency trap: Generative AI and the rise of interpretive load. Review of Education, 14, e70139. https://doi.org/10.1002/rev3.70139
Habib, S., Vogel, T., Anli, X., & Thorne, E. (2024). How does generative artificial intelligence impact student creativity? Journal of Creativity, 34(1). https://doi.org/10.1016/j.yjoc.2023.100072
Huberman, M., & Miles, M. B. (2002). The qualitative researcher’s companion. Sage.
Lewis Miller, C., Barth, D., Gay, K., Scherer, F., & Herron, J. (2025). Faculty development and GenAI playbook: Evidence-based best practices. Online Learning Consortium. https://onlinelearningconsortium.org/publication/2025/playbook/faculty-development-genAI-playbook/
Lipuma, J., Leon, C., & Meng, Y. (2025). From digital natives to AI-natives: Rethinking assessment and instructional design in the age of autonomous AI tools. STEM for Success Resources. 121. https://digitalcommons.njit.edu/stemresources/121
Maxwell, J. A. (2009). Designing a qualitative study. In L. Bickman & D. J. Rog (Eds.), The SAGE handbook of applied social research methods (2nd ed., pp. 214–253). Sage. https://doi.org/10.4135/9781483348858.n7
Orfanidis, C. (2025). Moral diversity in institutional policies governing the student usage of generative AI: An international comparison. Higher Education Quarterly, 79(4), e70051. https://doi.org/10.1111/hequ.70051
Parker, L., Loper, A. J., Carter, C., Hayes, J., & Karakas, A. (2026). Longitudinal insights into AI in education: Usage, ethics, and policy development in higher education. Computers and Education Open, 100329.
Richards, L., & Morse, J. M. (2012). README FIRST for a User's Guide to Qualitative Methods. Sage.
Saldaña, J. (2011). Fundamentals of qualitative research. Oxford University Press.
Sanz-Tejeda, A., Domínguez-Oller, J. C., Baldaquí-Escandell, J. M., Gómez-Díaz, R., & García-Rodríguez, A. (2026). The impact of generative AI on academic reading and writing: a synthesis of recent evidence (2023–2025). Front. Educ. 10:1711718. https://doi.org/10.3389/feduc.2025.1711718
Singer-Freeman, K. E. (2025). Structuring effective learning with GenAI: Lessons from group work. The National Teaching and Learning Forum. https://doi.org/10.1002/ntlf.30607
Singer-Freeman, K. E., Verbeke, K., & Barre, B. (2025). Generative AI usage among university students depends on academic level and task. Higher Learning Research Communications, 15(2), 1–24. https://doi.org/10.18870/hlrc.v15i2.1616
Singer-Freeman, K. E., Payne, C., & Zimmerman, S. C. (2026). Applying collaborative learning principles to teaching with GenAI: A pre-calculus assignment example. International Journal of Education in Mathematics, Science and Technology (IJEMST), 14(3), 971–985. https://doi.org/10.46328/ijemst.5542
Watson, C. E., & Rainie, L. (2026). The AI Challenge: How College Faculty Assess the Present and Future of Higher Education in the Age of AI. American Association of Colleges and Universities. https://www.aacu.org/research/the-ai-challenge
Winkelmes, M. A. (2023a). Introduction: The story of TILT and its emerging uses in higher education. In M. A. Winkelmes, A. Boye, & S. Tapp (Eds.), Transparent design in higher education teaching and leadership: A guide to implementing the transparency framework institution-wide to improve learning and retention (pp. 1–14). Routledge. https://doi.org/10.4324/9781003448396-1
Winkelmes, M. A. (2023b). Introduction to transparency in learning and teaching. Perspectives In Learning, 20(1). https://csuepress.columbusstate.edu/pil/vol20/iss1/2
Winkelmes, M. A., Bernacki, M., Butler, J., Zochowski, M., Golanics, J., & Harriss Weavil, K. (2016). A teaching intervention that increases underserved college students' success. Peer Review, 18(1/2), 31–36. https://www.proquest.com/docview/1805184428?sourcetype=Scholarly%20Journals
Winkelmes, M. A., Christopher, K., Copeland, D., Gravett, E., LaFleur, J., Palmer, M., … Yong, D. (n.d.). TILT Higher Ed examples and resources. www.TILTHigherEd.org
Wiss, A., Singer-Freeman, K., Thorpe, J. H., Heminger, C., Quinlan, S., & Pattershall-Geide, J. (2025, November 19). Supporting learning-centered usage of GenAI tools: Adapting the Transparency in Learning and Teaching (TILT) framework [Conference presentation]. OLC Accelerate 2025, Orlando, FL, United States.
Yee, K., Uttich, L., Giltner, L., & Bojanowski, A. (2025). Coach for the approach: The educator’s new role in the age of AI. UCF Created OER Works.
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Copyright (c) 2026 Andrew Wiss, Karen Singer-Freeman, Jennifer Pattershall-Geide, Kyle Dobbeck, Christina Heminger, Scott Quinlan, Joy Volarich, George Gray
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