by Kate Cammack, CfT Co-Director
How are we, as Sewanee faculty, thinking and feeling about GenAI?
Where are our strengths, and what are our opportunities as we move forward?
A few months ago, we were invited to help pilot a new scale, developed by a research team in Minnesota, assessing our readiness and attitudes toward using and implementing GenAI. Last week, we received our site’s data. Thanks to the 26 Sewanee educators who took this survey — that’s a decent sample for this pilot dataset! While they didn’t collect disciplinary data (which likely matters), this provides us some initial, valuable insight into dimensions of our GenAI use that we feel more comfortable and less comfortable with.
Each of the five dimensions are listed below; here’s how they’re organized:
- First is the researchers’ brief explanation. Each questions within that dimension is listed on the figure below each explanation. Responses were on a Likert scale of 1-5, with 5 = strongly agree.
- Then, I’ve graphed Sewanee’s responses, with the overall total scores within that dimension.
- Finally, I’ve added a quick summary of these data. While there are many ways that we might interpret or speculate about these data, I’ve tried to keep my summaries purely descriptive.
GenAI Ethics
- Topics that help educators understand implications of AI related to bias, privacy, integrity, and responsible use. Examples:
- Identifying and mitigating bias in GenAI writing and assessment tools
- Understanding student data privacy when using AI-enabled platforms
- Designing ethical AI use guidelines for courses
- Applying transparency and citation when using GenAI tools
- Data time!
- Sewanee faculty scored the highest on this dimension, suggesting this is an area of strength on our campus. Responses were also fairly consistent across all questions. Mean = 4.3, SD = 0.6; Median = 4; Mode = 4
GenAI Critical Thinking
- Topics that develop educators’ ability to evaluate and question the quality, accuracy, and appropriateness of GenAI output. Examples:
- Detecting hallucinations, inaccuracies, and biased reasoning in AI responses
- Evaluating whether AI‑generated content is developmentally appropriate for learners
- Teaching students to critique and refine AI‑assisted work
- Analyzing when AI‑generated explanations oversimplify or distort disciplinary concepts
- Data time!
- Sewanee faculty also scored relatively high on this dimension, suggesting this is another area of strength on our campus. One question, about our comfort with the ambiguity associated with GenAI use, was lower than the other questions, suggesting that this is an area for all of us to explore and pay attention to. Mean = 3.8, SD = 0.5; Median = 4; Mode = 4
GenAI Pedagogical Proficiency
- Topics that help educators integrate GenAI into learning experiences, including design, facilitation, and assessment. Examples:
- Designing assignments that incorporate AI responsibly and creatively
- Using GenAI to scaffold writing, problem‑solving, or feedback workflows
- Redesigning assessments to leverage GenAI while maintaining rigor
- Using GenAI to model disciplinary thinking (e.g., revision, lab procedures)
- Data time!
- Sewanee scores were fairly bimodally distributed within this dimension, with similar proportions of faculty disagreeing and agreeing to most statements. This is also the dimension where we saw the most strong disagreement. Everyone agreed that they recognized teaching challenges related to GenAI. Mean = 3.1, SD = 0.7; Median = 3; Mode = 3
Technology Self-Efficacy
- Topics that build general confidence in learning and applying new technologies beyond AI. Examples:
- Developing digital confidence with LMS features (analytics, rubrics, templates)
- Learning new educational technologies (e.g., video feedback tools, annotation tools)
- Troubleshooting and experimenting with unfamiliar teaching technologies
- Building a growth mindset for tech adoption in the classroom
- Data time!
3. Sewanee faculty scored relatively high on this dimension, suggesting this is another area of strength on our campus. There was a bit of hesitation about integrating new technologies if we didn’t consider ourselves an expert. Mean = 4.0, SD = 0.6; Median = 4; Mode = 4
GenAI Self-Efficacy
- Topics that help educators feel capable using GenAI tools and influencing output quality. Examples:
- Prompt‑crafting basics for educators new to GenAI
- Improving AI output using iterative prompting strategies
- Comparing different GenAI tools for instructional use
- Understanding limitations and boundaries of current GenAI systems
- Data time!
3. Sewanee faculty were again fairly divided on most of these questions. Many of us do not feel confident using GenAI tools in our teaching, and also reported that it was hard for us to use new GenAI tools. All of us recognized that GenAI capabilities differed from other educational technologies. Mean = 3.1, SD = 0.4; Median = 3; Mode = 3
Moving forward
We at the CfT are grateful to those of you who responded, as it provides some concrete areas for us to focus on in our GenAI-related programming next year.
Again, we’d love to hear what you’re thinking about, reading or trying, at any time and as your thinking/work evolves — the form is set to continuously accept responses, so please help us build this new community of practice!
AI was not used for any aspect of this blog post.