Category Archives: AI

AI Disclosure Matters: Closing the alignment gap between online instructors and students

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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:

  1. 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.
  2. 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.
  3. 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”).
  4. 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.
  5. 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.

Whose Work Is It Anyway? Writing an AI Statement Worth Reading

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Last year, as the instructional designer for a software engineering course, I knew the instructor had leaned on AI throughout the course’s development. I suggested he be transparent about it with his students. After all, he was asking the same of them.

What he wrote back went further than I expected. His syllabus statement opens not with rules for students but with an accounting of his own use. He names Claude the second author of his course, separates his own contribution from what emerged from that collaboration, and only then turns to what students may do. After getting his permission, I shared it with my team. Within minutes, colleagues were asking to keep it as an example. One had just come from a webinar where participants argued faculty shouldn’t have to disclose their own AI use at all.

Their reaction was telling. Instructional designers see a lot of syllabus statements, and this one stopped people. It fit what I keep hearing from faculty: they are no longer asking whether to address AI in the syllabus, but how to do it well.

Plenty of instructors still open with a different question: can I just ban it? I understand the impulse, but it is the wrong place to start. AI is already in your course, in your students’ hands, whether or not your syllabus mentions it. When Bowen and Watson (2026) talked with students, some said that if an instructor never mentioned AI, they took the silence as a “don’t ask, don’t tell” policy. Students brainstorm with it, run drafts past it, and ask it to re-explain what you covered last week. Ban it outright, and you’ve got a policy you can’t enforce and students who use it quietly anyway. I made that case at more length in Students Don’t Want AI Bans, They Want a Seat at the Table. What your syllabus has to sort out is where AI helps students learn and where it gets in the way, and then say which is which in plain terms.

What changed is not that AI arrived. It is what AI can do.

A couple of years ago, using AI meant asking a chatbot for a paragraph and pasting the result. That is still common. What is different now is scope. Generative AI answers a prompt. Agentic AI takes a goal and works through the steps to reach it. Point it at an assignment, and it can plan, draft, and revise the work. It hands back something finished, with little from the student.

This shift matters for your syllabus, because it changes what doing the work means. A policy written for a student who copies one answer from an AI tool is aimed at the wrong target. That does not mean everything is permitted; it means deciding on purpose which uses belong in your course and which do not, rather than letting silence decide for you. Here is the question your syllabus has to answer: in this course, what part of the work has to be the student’s own, and how will they show it?

A good statement does two jobs.

The first is obvious. Tell students what they may and may not do with AI. Do it assignment by assignment, since a tool that undermines one task may be right for another. Tie the rule to the point of the assignment. “Do not use AI to draft your reflection, because the thinking is the assignment” gives students a reason they can act on. A blanket ban does not.

The second is easier to forget. Say how you use AI too. If you used it to build a study guide, draft feedback, or write practice questions, tell students. It models the honest, purposeful use you are asking of them. At OSU, Ecampus collects AI tools and faculty support resources, including sample syllabus and assignment statements you can adapt.

Write it like you mean it.

The statements that work read like a person wrote them, not a policy office. A good one names what students can do that a model cannot, and makes building those skills the point of the course. Remind students that their own thinking and voice are worth developing; that does more to shape how they work than any list of don’ts. And if your campus hands you boilerplate policy language, treat it as a floor rather than the finished statement, then build your own on top.

The software engineering instructor I opened with is the clearest example I have. His statement spends several paragraphs on his own use of AI before it says a word about students’. He marks off what is his: the structure, the pedagogy, the assessments, and the running software example he wrote for his own textbook. Then he names three ways he worked with AI: as a conversational partner, as a second author on the course materials, and as a software developer. He is specific about the tool and the timeline, down to which modules he wrote before starting to use it. And he is clear that the collaboration made the course better, more engaging and more technically sound, rather than easier.

“I want to be completely transparent about how I have used GenAI to author this course.”

Only then does he turn to students:

“I strongly encourage you to use GenAI in this course as thoughtfully, conscientiously, and deliberately as I have used it to develop this course.”

Chris Hundhausen, software engineering. Shared with permission.

That last line works because of everything before it. He earned the right to ask by putting his own record on the table.

Whatever you write, plan to revisit it. The tools will change before next term. So will the way your students use them. A statement you can update in ten minutes each term beats a perfect one you write once and forget.

Offer a seat, not a rulebook.

So decide which part of the work must be your students’ own, and how they will demonstrate it. Be clear with them about what learning in your course requires. You can ask your students to help, since they are much more likely to honor and understand a policy they helped build.

A note on process: the argument, examples, and choices here are mine. I used an AI assistant to pressure-test the structure and tighten the wording, the same transparency this piece asks of you.

Reference

Bowen, J. A., & Watson, C. E. (2026). Teaching with AI: A practical guide to a new era of human learning (2nd ed.). Johns Hopkins University Press.