Teaching and Learning with AI Conference

Faculty are eager to use AI tools to save time, but not all information is safe to enter into generative AI. We’ll focus on FERPA and protecting student data. Participants will work through realistic academic scenarios and decide whether the actions keep data secure or create a FERPA violations. The discussion will clarify gray areas and provide clear takeaways for faculty who want to use AI responsibly without putting student information at risk.

As generative AI becomes increasingly embedded in creative and academic workflows, students must learn not only how to use AI tools—but how to interpret and interrogate them. This session presents a classroom–library collaboration that reframes AI image analysis as a form of pedagogical feedback, positioning machine interpretation as a new rhetorical “audience” in multimodal composition. At Goldey-Beacom College, students in Critical Writing I create visual arguments responding to the common reading Advocate by Eddie Ahn, using either traditional artistic methods or AI image generators. After composing written interpretations of their work, images are analyzed using JSTOR’s Seeklight, an AI tool that generates descriptive readings of visual content. Students then compare the AI’s interpretation with their original intent, examining where meaning aligns, diverges, or is misread. Rather than treating AI output as evaluative or authoritative, this assignment reframes machine description as reverse evaluation: students analyze what the AI “sees,” uncover implicit rhetorical assumptions, identify ambiguity or bias, and revise their writing to clarify meaning for both human and machine audiences. This create → deconstruct → reflect → revise cycle transforms AI from a shortcut into a site of critical inquiry. The session will demonstrate how AI misreadings can deepen students’ understanding of visual rhetoric, authorship, ethical design, and multimodal clarity—while reinforcing academic integrity by centering reflection, revision, and intentional meaning-making. 

This Faculty Learning Community (FLC) brought together instructors who were eager to push the boundaries of how emerging AI tools could reimagine open educational resources (OER). The participants dug into their selected courses, teaming up with AI to create, adapt, and elevate their materials in imaginative new ways. Throughout the experience, the instructors tinkered with model parameters, crafted inventive prompting strategies, and transformed familiar assets—syllabi, slides, lecture notes—into lively, shareable OER that now feel genuinely new. With support from UF experts in AI and instructional design across the university, participants will share their innovative outputs with the UF teaching community at two university conferences this spring.

Explore how Sharpen Advantage, McGraw Hill’s AI study and academic success app, combines an extensive library of vetted academic resources with your institution’s content to create personalized, responsible AI study support that students can access right on their phones. Learn how faculty and administrators use instant insights into student performance and predictive outcomes to drive early intervention, lower DFW rates, and boost student success. 

This presentation explores activities that help students examine how AI impacts credibility, agency, and rhetorical choices regarding audience, purpose, context, and writer. Central to these assignments is a course outcome: analyzing how systemic inequalities shape the formal and informal rules defining “good” and “bad” writing. The assignments ask students to consider: whose rules does AI learn, enforce, and normalize? Students analyze AI use in dating apps and social media activism to see how audiences perceive both content and writer, then transfer these insights to academic sources and their own writing.