New Publication: Documenting & Disclosing AI
A collaboration with a friend and colleague, Carol Damm.
I’m sharing a new publication with y’all that is co-authored by Carol Damm and myself.
From Prompt to Practice: A Framework for Transparent GenAI Use in Higher Education in EDUCAUSE Review dropped this week and we’re excited to share it with the world! The piece explores the question of documenting and disclosing working with GenAI in educational development work (Instructional designers, faculty developers, instructional technologists, etc). Specifically, we share a framework for consideration for folks to consider as you and your teams start to integrate AI into workflows more directly.
In this post, we’re going to share a little bit about our thoughts about the piece and why we wanted to write it. We threw together a few questions and you can see our answers below. We wrote these separately but wanted to place them together as a kind of look at where we both came from in this work.
Where did the idea come from?
Carol: This initial idea came to me when refining our quality assurance standards for online courses as I saw a gap in our rubric about identifying intellectual property standards and concurrently the use of GenAI in relation to IP. I wanted to develop a mechanism that would enable me to “grade” IP usage (including confirmation that fair use limitations of any copyrighted materials are respected).
I then met up with Lance on a visit back to Boston and, per usual, we started to talk about it further and immediately got excited about thrashing it out together. Lance has been studying the ethics of AI and data privacy years before this became a mainstream topic, so I knew he would be a valuable collaborator. Plus, there was the added benefit of having an excuse to meet up regularly and talk shop.
Lance: It started, as so many things do, with being in conversation with friends and colleagues. Carol began sharing something about AI and its place in educational development work that was gnawing at her: how do we make sense of, track, and make sense of how AI is showing up in this work.
Her ideas registered something in me that I was already starting to practice, which was sharing chatlogs when my work was deeply intertwined with AI or even keeping versions of certain things that intersected with AI. For instance, I talked about how I’m continuing to update my version of Custom Instructions for my AI Tools in this post. I’ve also been taking a copy of Claude’s active memory of me nearly every week for the last 6 months. That’s definitely a future post!
Why was it important to write about?
Lance: Once Carol started to share her ideas, it really stuck with me about how all of this is changing and also, at times, being implemented. With the speed of AI and unintentional (or undocumented) implementation, we risk losing sense of some of the decisions and ways in which the field of educational development is changing.
Carol: There is prolific use of GenAI, across the board, with little to no transparency in what portion of an author’s work is original or which ideas are conceived by the author and which originate from GenAI output. The consequences are an inherent lack of trust of anything that we see and read, which has become problematic in higher education as well as most industries. Transparency in how an author uses AI is a powerful mechanism for rebuilding that trust. And, as we write about in this article, transparency enables us to mentor young adults in ethical use of AI as well as reinforce, and hopefully ingrain, the principle that we should continually strive to build our own cognitive functions by using GenAI in nuanced ways, as an assistant and not a replacement.
What are you most proud of in this work?
Carol: I am most proud of the comprehensive understanding that Lance and I came to in developing this transparency framework, thanks to how our writing practice was structured. I refer to how Lance harnessed the use of AI to free up our capacity to let our minds roam freely and quickly make connections that might not have happened if we were mainly writing back and forth. He had a two-step process:
The development of prompts/questions by AI to start the conversation.
The transcription of our conversation by AI enabled us to talk and ramble at times and quickly spark new ideas without having to stop and capture them if we had been taking notes or writing.
This experience also captured the unique dynamics that Lance and I have developed in our work together at Brandeis (long ago, it feels like) where we brainstormed together, got overly geeky about our work, and inspired each other.
Lance: I think what we put forward was a bit of a challenge for our work. You can use our framework or not, but use something: figure out how to document and disclose this work so that we understand what we are doing.
Within that, there is an alignment of our work with institutions that, if we are asking students to disclose and document their usage, that line should follow through the entire institution. It doesn’t have to be exactly the same, but we do need a more systematic way to articulate all of this.
And, of course, that it is a collaboration with Carol. We first met working together at Brandeis University, and while it’s been years since either of us worked there, it’s a testament to our work together and our friendship. Over the years, I’ve learned much from Carol, and so getting to create something with her was also really meaningful.
Has anything changed in terms of what you wrote when you wrote it and now?
Lance: I think the big thing that I’m thinking about and starting to see is the possibility of interoperability and record-keeping among AI tools. ChatGPT now has an export of data feature, and Claude, too, has an import feature. These features are still nascent, but it helps me to think about what documenting looks like when we can actually export our work with AI into our own archives in ways that might be easier than copy and paste or print-to-PDF. These are grounds for new considerations and maybe a follow-up.
Carol: What has changed has only been more recent. I have been waiting for confirmation of the printing of this article before starting to apply this framework more openly. I use it for my own self-checks, but have been waiting for the validation of mainstream publication to start promoting it more widely. I like the framework very much, and do not see the need to update it, at this point in time. I hope that it will be adopted in some form or another in the field of education/curriculum development. I like that both Lance and I are not overly concerned about copyright and are happy to have others use it through a Creative Commons license.
About Carol Damm
I am an American living in Bremen, Germany, working at Constructor University with students from over 110 countries. I lead a team of learning designers and media producers focused on learning experience design and sharing effective teaching practices informed by the learning sciences. I love to apply what I know through teacher training and have the joy of teaching about “Advanced Teaching Strategies” to educators from Kazakhstan and soon from other countries.
The Update Space
Upcoming Sightings & Shenanigans
EDUCAUSE Online Program: Teaching with AI. Virtual. Facilitating sessions: ongoing
Recent Recordings, Resources, & Writings:
Damm, C., & Eaton, L. (2026, March). From prompt to practice: A framework for transparent GenAI use in higher education. EDUCAUSE Review.
Eaton, L., Nemeroff, A., & Sun, X. (2026). AI-assisted course design and development. In K. S. Ives, M. Cini, & R. Schroeder (Eds.), AI applications in online higher education administration: Strategies for maximizing returns and improving outcomes. Routledge.
Margin of Thought with Priten: Season 1, Episode 5: How Can We Center Pedagogy During the AI Tech Wave? (February 2026)
Online Learning in the Second Half with John Nash and Jason Johnston: EP 39 - The Higher Ed AI Solution: Good Pedagogy (January 2026)
The Peer Review Podcast with Sarah Bunin Benor and Mira Sucharov: Authentic Assessment: Co-Creating AI Policies with Students (December 2025)
David Bachman interviewed me on his Substack, Entropy Bonus (November 2025)
The AI Diatribe Podcast with Jason Low (November): Episode 17: Can Universities Keep Pace With AI?
The Opposite of Cheating Podcast with Dr. Tricia Bertram Gallant (October 2025): Season 2, Episode 31.
The Learning Stack Podcast with Thomas Thompson (August 2025). “(i)nnovations, AI, Pirates, and Access”.
Intentional Teaching Podcast with Derek Bruff (August 2025). Episode 73: Study Hall with Lance Eaton, Michelle D. Miller, and David Nelson.
Dissertation: Elbow Patches To Eye Patches: A Phenomenographic Study Of Scholarly Practices, Research Literature Access, And Academic Piracy
AI Syllabi Policy Repository: 210+ policies (always looking for more- submit your AI syllabus policy here)
Finally, if you are doing interesting things with AI in the teaching and learning space, particularly for higher education, consider being interviewed for this Substack or even contributing. Complete this form, and I’ll get back to you soon!
We periodically host small-group workshops and leadership sessions for higher ed teams. You can learn more about our current offerings here.
AI+Edu=Simplified by Lance Eaton is licensed under Attribution-ShareAlike 4.0 International
The transparency question is so interesting to me, especially the point about alignment across an institution. If we're asking students to document and disclose how they use AI, it makes sense that the expectation should extend to the people designing their learning experiences too. I'd love to see this thinking trickle down to K-12, where parents are navigating AI in their kids' classrooms with almost no transparency about how schools themselves are using it behind the scenes.
The transparency piece is one that doesn't get enough airtime in AI policy discussions. There's a huge difference between banning AI use, allowing it without acknowledgment, and actively requiring disclosure. The disclosure framework actually builds the kind of metacognitive habits that matter - students who have to articulate what AI did and what they did on top of it are doing something genuinely different from students who just submit outputs. Really valuable contribution to the conversation here.