AI as a Socratic Opponent: Comparative network analyses from a college psychology course - Tilak, Shantanu; Brown, Baylee; Madanyan, Hovhannes; Washington, Gabriella; Collier, Jazzmin; Ragnedda, Rebecca; Mitchell, Jaiden; Brewington, Jasha; Hall, Briana; Barnum, Kristopher; Moore, Courtney; Harris, Allure; et al. (Journal of Sociocybernetics, 2025)
This mixed methods participatory study was co-authored by 19 undergraduate students and their instructor in an introductory psychology class, with help from two research assistants. Participant observers evaluated and reflected upon the use of artificial intelligence (AI) language models as surrogate agents to support classroom discussion forums. The study forms a practical example of the use of generative AI in collaborative learning where human agents take the dominant role in conversation, acting as an applied effort to bring life to contemporary theoretical literature in educational technology. An M- and P-individual framework rooted in Gordon Pask’s cybernetics is used to structure out human-computer interaction feedback loops occurring during class discussions. Live chats were held during each lecture on a Google community, wherein students would respond to a weekly prompt posted by the instructor and respond to peers. Two of these sessions were held on the Character.AI and DeepAI platforms. Four groups of students interacted with language models of Freud and Piaget during sessions related to human consciousness and development, with one student “driver” prompting the AI following group brainstorming. Comparable discussions from the business-as-usual classes on the nervous system and human learning are compared to AI discussions, using the igraph network analysis package in RStudio. Comparative network visualizations highlight the possibility to create transitive distributed discussions using AI in college classrooms. To better understand student-to-student interactions guiding the driver’s prompting in AI chats, qualitative insights are shared from each group.