Using our Pedagogical Knowledge to Disrupt AI in the Classroom

• In Event: Teaching and Learning Symposium Roundtable Session
  In Refereed Roundtable (90 minute): Table 3: Navigating AI and Digital Literacy

Abstract

Artificial Intelligence (AI) itself is certainly disrupting the status quo. In turn, we as sociologists need to use our own pedagogical knowledge to disrupt AI in the classroom. We can offer solutions and interventions to what many see as a critical social problem in higher education. Recent conferences and trainings on AI in higher education reveal a growing divide in faculty responses, ranging from outright rejection to calls for universal adoption. Many faculty, however, fall somewhere in between and are grappling with how to meaningfully integrate AI into their teaching. While universities are responding with introductory, self-paced AI trainings, these efforts often fail to address the more difficult challenge of curricular integration aligned with programmatic learning outcomes. In a survey conducted by the American Association of Colleges & Universities and Elon University, 83% of non-user respondents reported that “faculty unfamiliarity with the tools is a challenge to adoption in the courses in their departments” (Watson & Rainie, 2026). One option is the development of course-based exercises that faculty can adapt within existing sociology curricula. This presentation will focus on exercises from methodology courses using specific examples, such as the use of Claude to produce graphic presentations and the use of ChatGPT to assist in survey design. These exercises are designed to be modular and aligned with existing course objectives rather than requiring wholesale curricular redesign. The presentation will include time for attendees to share their own exercises and activities from other areas within in sociology and will further highlight strategies for sharing and scaling all this work through venues such as the ASA TRAILS resources or future textbook editions. Attendees will leave with a framework for developing a single, discipline-appropriate AI exercise and for thinking programmatically about AI integration within the constraints of time, institutional guidance, and faculty capacity.

Author

• Norah Peters Shultz, San Diego State University