Teaching and Learning with AI Conference

Synthetic content and “AI Slop” continue to saturate the media landscape, leading to confusion about all kinds of information. Last fall on a Fulbright teaching project, I surveyed students from Florida, Slovakia, Northern Ireland, and Poland about generative AI, higher education and the need for media literacy. Respondents clamored for guidelines for appropriate use of AI in coursework, and for spotting misinformation in AI-generated or manipulated content. After sharing results, I will provide a scalable media literacy framework to supplement emerging AI literacy skills. This strategy allows instructors of any discipline to draw upon their expertise, and show how it's useful when evaluating what we encounter online. #medialiteracy #cross-curricular-pedagogy #knowledge-is-good

Over the past two to three years, many college faculty have been actively experimenting with AI alongside their students, rethinking assignments, assessment, and academic integrity in real time. But during this same period, what were K–12 students and teachers actually doing with AI? Drawing on five lessons learned from working with over 200 K–12 teachers in Northwest Florida through Florida State University’s InSPIRE Initiative, this session examines how uneven access, policy constraints, and instructional choices have shaped students’ AI experiences before college and how those experiences will increasingly impact teaching and learning in higher education.

Generative AI can enhance the development of competency-based education (CBE) courses, which prioritize personalized learning and mastery over traditional time-based methods. A major challenge to widespread CBE adoption is the administrative burden of aligning industry-specific skills with learning outcomes. We introduce a "Prompt to Competency" framework that uses AI to streamline course design through three key stages: (1) identifying current labor market demands, (2) aligning these demands with competencies using Bloom's Taxonomy, and (3) creating adaptive, rubric-based assessments. Preliminary results show that utilizing AI in curriculum development can reduce design time by about 60% while ensuring strong alignment with industry standards, thus enhancing the scalability and effectiveness of CBE models.

AI literacy is quickly becoming a stated goal for first-year students, but translating that goal into clear learner outcomes and assessable work is far from simple. This short, discussion-driven session shares the structure of an undergraduate AI literacy course, focusing on its learner outcomes and assessments. Participants will collaboratively reflect on whether these outcomes reflect what AI literacy should mean for freshmen and whether it is even possible to assess such learning in authentic ways.