Making Generative AI Expectations Visible: Extending the TILT Framework for Transparent Assignment Design

Citation

Wiss, A., Singer-Freeman, K., Pattershall-Geide, J., Dobbeck, K., Heminger, C., Quinlan, S., Volarich, J., Gray, G. (2026). Making Generative AI Expectations Visible: Extending the TILT Framework for Transparent Assignment Design. Online Learning, 30(S1), 55–77. https://doi.org/10.24059/olj.v30i3.6398

Abstract

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.

Authors

  • Dobbeck, Kyle
  • Gray, George
  • Heminger, Christina
  • Pattershall-Geide, Jennifer
  • Quinlan, Scott
  • Singer-Freeman, Karen
  • Volarich, Joy
  • Wiss, Andrew

Reference Type

Journal

Keywords

  • Artificial Intelligence
  • Instructional design
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