Author Archives: Naomi Aguiar

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.

A White Paper Series Provides Actionable Insights in Course Design

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By Naomi R. Aguiar & Tianhong Shi

For eight straight years, OSU’s online bachelor’s programs have been ranked in the top 10 in the nation by U.S. News & World Report. How do we achieve that? One way is through collaboration between faculty and online learning design experts. Ecampus instructional designers support and partner with faculty to ensure that evidence-based research is applied in the courses we develop. 

Another way is through research conducted by our very own faculty at OSU. In the Research Unit at Ecampus, faculty can receive funding to conduct research in online teaching and learning through the Research Fellows Program. In this program, funded faculty members conduct original research with students and instructors over the course of a 15- to 18-month period. At the conclusion of their projects, faculty write white papers, which are open access articles published by the Research Unit and catalogued in the OSU Scholars Archive. These white papers provide actionable insights that both instructional designers and faculty members can directly apply to course design. Below, we share two specific examples from recently published white papers that can reinforce or inform decisions in course design.

Visual Design Matters

In a recently published white paper, researchers Yuzhi Sun and David Nembhard conducted an experiment examining how graphical displays of information and the use of highlighting can impact a learner’s engagement with the material, their “cognitive load” (i.e., the amount of information a learner is tasked with), and their learning performance. Using EEG (a method that measures different types of brain waves by placing electrodes on the scalp), Sun and Nembhard found that presenting information as tables rather than dot graphs increased students’ cognitive load, but also increased their engagement with the material and their retention of the information. Highlighting the most essential information also had a positive impact on students’ ability to retain learned information.

Sun and Nembhard’s (2022) findings support the Ecampus Essentials guidelines for online course design; specifically, visually representing course content in multiple ways that align with weekly and course outcomes. In many of our Ecampus STEM courses, data are presented in tables and explained by instructors in several different ways, such as in narrated PowerPoints, light board learning glass videos, or whiteboard screencast videos. 

Here is a specific example of how this is used in one of our courses, the Differential Calculus for Engineers and Scientists (MTH251). The figure below shows a table used in a math problem-solving video that helps students categorize information into what is already known and what needs to be solved for. 

https://media.oregonstate.edu/media/t/1_rpfn2l96

Another example comes from a habitat analysis course (Habitat Analysis 1), in which information is highlighted in videos to bring students’ attention to important content information. The red highlighted areas in the figure below clearly indicate a grizzly bear’s habitat.

https://media.oregonstate.edu/media/t/1_bcwtpg1c

On Writing Intensive Courses

In another white paper, Andrew Bouwma describes a study he conducted to help instructors provide timely feedback to students in writing intensive online courses. In a Zoology 349 course, Bouwma examined students’ acceptance of a web-based peer review tool, Peerceptiv, as well as the extent to which using this approach could improve students’ writing skills. Overall, Bouwma found that students’ acceptance of this peer review tool was high. Most students agreed that the Peerceptiv tool was easy to use and that comments from their peers were helpful. Students also agreed that the Peerceptiv assignments enabled them to think more critically about the subject matter and helped them produce better writing assignments. And beyond positive perceptions of the tool, students further demonstrated significant writing gains in both their thesis statements and in their essay structures.

Bouwma has since held several information sharing sessions about Peerceptiv tool for other instructors. Now, more biology and STEM courses are using the Peerceptiv tool, and it has even been creatively adapted into a Business Applications Development course (BA272) that teaches python coding. In this course, coding assignments were once assigned to students individually and debugging errors in coding could be a daunting task. With the use of the Peerceptiv, peer reviewing has assisted students in identifying coding errors more quickly. One instructor also noted that the use of this tool has improved the quality of peer feedback, as well as provided students with insights to improve their programming skills. The successful use of this tool in other courses highlights the broad application of Bouwma’s original study to other online learning contexts.

Want to Learn More?

Each year, the Oregon State University Ecampus Research Unit funds projects, up to $25,000 each, to support the research, development and scholarship efforts of faculty and/or departments in the area of online education through the OSU Ecampus Research Fellows program.

This program aims to:

  • Fund research that is actionable and impacts student online learning
  • Provide resources and support for research leading to external grant applications
  • Promote effective assessment of online learning
  • Encourage the development of a robust research pipeline on online teaching and learning at Oregon State

Fellows program applications are due Nov. 1 each year. If you are interested in submitting an application, reach out to Naomi Aguiar, the OSU Ecampus Assistant Director of Research. Research Unit staff are available to help you design a quality research project and maximize your potential for funding.

References

Bouwma, A. M. (2021). Testing the Efficacy and Student Acceptance of a Peer-Review Writing Program in an Online Course. White Paper. Corvallis, OR: Oregon State University Ecampus Research Unit.

Sun, Y., & Nembhard, D. A. (2022). Modeling online learning performance with biometrics: Current study and future directions. White Paper. Corvallis, OR: Oregon State University Ecampus Research Unit.