In online education, transparency is foundational to high-quality learning experiences (Ecampus Essentials, 2026). Transparency about who the instructor is, how the course set-up, what tools are available, and how learning will be assessed helps students trust the learning process. The emergence of generative artificial intelligence (AI) tools in higher education raises important questions about transparency in online courses, particularly regarding instructor and student disclosure of AI use.
Disclosure of AI use in online teaching and learning can be challenging for a number of reasons. First, it may be difficult to know what constitutes “use.” For example, is a Google search that resulted in an AI generated summary to gather information for course development considered “use”? What about a student using generative AI tools to correct spelling, grammar or punctuation in a term paper? Second, it may also be difficult to assess what level of use warrants disclosure. Does using AI tools to generate ideas for the structure of a video lecture need to be disclosed, even if the content of the lecture was generated by the instructor without further AI assistance?
There are also real concerns about how disclosure might impact perceptions of the person using the tools and the quality of their work. Recent studies have found that in general, disclosing AI use across a range of tasks erodes trust (Schilke & Reimann, 2025). The “trust penalty” is particularly strong for women who disclose their use of generative AI tools in professional contexts; research has shown that women are disproportionately viewed as “lazier” and “less competent” (Chatoo, 2026; Gai, Hou, & Tu, 2025).
These research findings might explain why in a survey of faculty we conducted in the Ecampus Research Unit, 32% of online instructors reported never disclosing their AI use to students, even though 59% were using these tools for work (Aguiar et al., 2026). This stands in stark contrast to faculty expectations of their online students. About half of faculty respondents indicated that they allowed students to use AI tools in their online courses, provided that students properly disclosed their use.
This misalignment between faculty disclosure practices and expectations of students could disrupt the relational trust between instructor and student that is essential for high-quality online learning. Lack of disclosure may also result in additional work burdens for online students and faculty alike. Students may spend significantly more time documenting how they did not use AI to complete their assignments (Luo, 2024). And in our faculty survey, one respondent indicated that their grading time had increased by “300%.” Another instructor commented, “…it takes a lot of faculty time to grade AI outputs and/or screen them for falsified information” (Aguiar et al., 2025). According to this same instructor, the additional work burdens for online students and faculty creates a “lose-lose” situation.
While faculty disclosure of AI use clearly comes with some reputational risks, the long-term benefits likely outweigh current risks. When instructors model disclosure of AI use for students, it contributes to a culture of openness, honesty, and professional vulnerability that positively impacts trust between faculty and students in the online learning environment (Luo, 2024). This in turn leads to a higher quality learning experience for online students. And when faculty and students are open, honest, and transparent about their use of generative AI tools, this can also alleviate additional work burdens and subsequent feeling of anxiety and/or stress.
So, how can instructors model disclosure of AI use for students in online courses? Based on our research with online students and faculty, here are some recommendations to get started:
- Define what AI use means– for you and your students. What tools are considered AI tools (e.g., Grammarly vs. ChatGPT), and what level of use is appropriate in a given context? This will help both you and your students clearly understand what use means and what may need to be disclosed.
- Clearly articulate when disclosure is required– for you and your students. Talk to colleagues about what use cases they think require disclosure. Provide students with specific examples from assignments in your courses to help reduce everyone’s feelings of uncertainty and stress.
- Provide specific examples of how to disclose. If you used AI tools to generate course materials, model disclosure by describing what tools you used and how you used them. A recent blog post by Ecampus instructional designer, Deb Mundorff, provides specific examples of how faculty have chosen to disclose their AI use with online students. If you did not use any generative AI tools, you can also let your students know in a disclosure statement (e.g., “No generative AI tools were used in the development of my course materials”).
- Openly discuss challenges and solutions related to disclosing AI use. Conversations about professional reputational risk are valuable to have with colleagues and with your students. These conversations can help build a culture of honesty and transparency in the use of AI tools.
- Iterate and revise disclosure guidelines and practices based on feedback from colleagues and students. AI tools and related applications are rapidly changing. Working together to reconsider approaches in real-time will help mitigate challenges and stressors as they arise. Inviting students to contribute to disclosure guidelines can inform current and future policies that work for both instructors and students.
Disclosing use of generative AI tools is an evolving challenge that can feel fraught and overwhelming. Disclosure may come with reputational risks that can disproportionately harm some individuals more than others. That is why building a culture of transparency with AI use is essential and something we can and should do together. It will require practice, patience, and iteration. Sometimes we will make mistakes, but what will matter most is our willingness to be open and honest about our AI use and support others to do the same. This supports trusting relationships that promote a healthy online teaching and learning environment.
Check out some additional resources from both Ecampus and the Center for Teaching and Learning; and reach out to share your thoughts with us. We’d love to hear your approach to disclosing AI use in your work.
References:
Aguiar, N. R., Coltharp, A., Dello Stritto, M. E., Jorgensen, J., Kronser, Z. E., Pearson, A., Piacenza, S., & Reese, D. (2026). Online Faculty Perceptions of Generative AI. Oregon State University Ecampus Research Unit. https://ecampus.oregonstate.edu/ research/publications/
codeforgoodnow. (2026). The AI Judgement Penalty. https://codeforgoodnow.com/wp-content/uploads/2026/06/Research-Report-The-AI-Judgment-Penalty.pdf
Ecampus Essentials. (2026, August 11). Policies & Standards- Oregon State University Ecampus. Retrieved August 2026 from https://ecampus.oregonstate.edu/faculty/standards-principles/ecampus-essentials/
Gai, P. J., Hou, J, & Tu, Y. (2025). Competence Penalty Is a Barrier to the Adoption of New Technology (May 11, 2025). Available at SSRN: https://ssrn.com/abstract=5255039 or http://dx.doi.org/10.2139/ssrn.5255039
Lou, J. (2025). How does GenAI affect trust in teacher-student relationships? Insights from students’ assessment experiences. Teaching in Higher Education, 30(4), 991-1006. https://doi.org/10.1080/13562517.2024.2341005
Mundorff, D. (2026, August 10). Whose Work Is It Anyway? Writing an AI Statement Worth Reading. Ecampus Course Development & Training Blog. https://ecampus.oregonstate.edu/faculty/inspire/2026/08/10/whose-work-is-it-anyway-writing-an-ai-statement-worth-reading/
Schilke, O. & Reimann, M. (2025). The transparency dilemma: How AI disclosure erodes trust. Organizational Behavior and Human Decision Processes, 188, 104405. https://doi.org/10.1016/j.obhdp.2025.104405.
AI disclosure statement: No generative AI tools were used in the development, writing, and review of this blog.