Faculty Engagement: Developing A Shared Approach to Equitable Gen AI Use

Kelly Conn, Teaching Professor

Joan Giblin, Associate Teaching Professor, EdD Program Director

Dan Serig, Assistant Teaching Professor

Chris Unger, Teaching Professor

A number of faculty were already using generative AI in their research and teaching practice. During one of the early sessions, these faculty demonstrated how they used AI tools and talked about their experience with it, including what they found valuable and why they thought it was important to address generative AI at the program level. This included discussion of how their individual philosophies of education shaped their thinking about generative AI, and included opportunities for uncovering diverse perspectives and approaches.

The stated purpose of this session was to build a common understanding of how Gen AI can be integrated equitably into the doctoral program’s curriculum. Prior to this Engagement Session, faculty were given several readings to inform their thinking about AI, for example the federal Office of Technology’s 2023 report on Artificial Intelligence and the Future of Teaching and Learning.

Faculty participated in breakout discussions, with a note-taker embedded in each group. Those notes were synthesized into a document that was shared back with the participants. The synthesis document served as a mechanism for authoring a guidance document on ethical use of generative AI that represented a range of perspectives and shared understanding. An outcomes document was generated following each session, which was shared with faculty prior to the next gathering – share examples in “Related Materials” below.

For example, this meeting generated five “outcomes” statements that were summarized and explained in the document:

1. There is a recognition of the need to integrate responsible use of Gen AI into both doctoral and master’s programs’ curricula.
2. There is a concern about helping students develop an ethical perspective on when and why it might be inappropriate to use Gen AI.
3. There is an emphasis on preparing graduates to become agents of change in a professional landscape that includes AI.
4. There is a recognition of the need for policies related to Gen AI use.
5. Students enter programs with varying levels of prior Gen AI expertise.

Faculty experimented with the use of generative AI tools in support of dissertation deliverables that are core to the program, specifically the literature review. Faculty members used several generative AI tools to perform key literature review processes, with actual dissertation topics, with several generative AI tools.

The group discussed how the process of literature review had changed over the years with the development of new technologies (from card catalogs and microfiche to generative AI). Faculty voiced their concerns and the opportunities they envisioned for how generative AI might connect to the literature review process.

The discussion led to the development of statements of concern that informed future work of the group. The full set of statements can be accessed in the March Outcomes document in Related Materials below.

Sample Statement:

There is a concern that our students understand Gen AI and its potential applications, as well as the ethical considerations involved, such as the importance of understanding use agreements, intellectual property issues, and the use of Gen AI databases without acknowledgement.

Skills are essential to effectively using generative AI in literature reviews, and that falls within our purview as faculty members to clarify those essential skills for students, base them on unique human abilities, and design our projects to elicit/demonstrate them.

Faculty explored and experimented with the use of generative AI for qualitative research, a methodology that is foundational to the program. Many tools designed to support qualitative analysis are increasingly AI integrated, and general tools such as ChatGPT can be leveraged for this purpose. The faculty identified concerns (e.g., data ethics, potential for bias, use of AI that shortcuts the generative process of qualitative analysis) and also considered the potential benefits of using AI appropriately in support of the analysis process.

An outcome of this discussion was the idea of developing a sequence of activities across all research courses to scaffold and assist the development of student capacities in relation to the use of generative AI in support of qualitative research, and student understanding of what constitutes appropriate and inappropriate use of AI in this aspect of their doctoral work. This served to also advance efforts that are underway to systematically integrate generative AI across the doctoral program curriculum.

Faculty met for a summer retreat, and as part of that gathering they reviewed session outcome documents and discussed next steps. The EdD Program Director drafted a statement that faculty had an opportunity to review and revise. This statement was also vetted by the college’s Associate Dean for Academic Affairs. The full statement is available for download in “Related Materials” at the bottom of this page.

Excerpt from Statement:

We assert that generative AI is most useful for those who have expertise.

Our responsibility as a faculty is to prepare you to become scholar-practitioners in accordance with the Program Learning Outcomes. Becoming a scholar-practitioner requires developing expertise in the knowledge, skills, abilities, and competencies required for scholarly research.

In accordance with these beliefs, we discourage the use of generative AI early in the learning process so that you develop expertise in research design. Later, we will encourage you to use generative AI as a constructive collaborator, as a tool to improve your original work, and as a tool for efficiency. AI should never be the main author or creator of any work you claim as your own.