Socratic Mediation Patterns in AI–Student Interactions: A Content Analysis of a Conversational Agent in Distance Higher Education

This study identifies and characterises the Socratic mediation patterns enacted by MIA, an AI-based conversational agent used in distance higher education. A deductive content analysis was conducted on 737 conversations using six categories: exploration of prior knowledge, contextual adjustment, linkage to experiences, autonomy-oriented prompts, dialogic progression, and verification prompts. The categorical framework achieved full expert content-validity agreement (S-CVI/Ave = 1.00). Contextual Adjustment (77%), Verification Prompts (76%), and Autonomy-Oriented Prompts (74%) were the most frequently observed categories. Sixty of the 64 theoretically possible category combinations occurred in the corpus, and Linkage to Experiences appeared more frequently in personal conversations (33.7%) than in academic conversations (18.3%). The distribution of categories also varied according to conversation length, with longer exchanges containing a broader range of coded dialogic moves. These findings describe the conversational repertoire through which MIA operationalised features associated with Socratic mediation. Because the study did not include independent measures of student satisfaction, learning, engagement, or self-regulation, the results should not be interpreted as evidence of educational effectiveness or causal effects. The study contributes an operational framework for analysing Socratic features in AI–student interactions and identifies directions for outcome-based research.