More Correcting or Confounding? When an AI Arguing Agent Informed by Common Student Misconceptions Joins Case-Based Peer-Argumentation in a Real Course

Peer-argumentation is an effective and widely adopted learning activity, yet it remains difficult to enact at scale. While pedagogical conversational agents (PCAs) have been extensively studied, their role as arguing peers that engage students in dialogic argumentation is underexplored. To address this gap, we present ArguBot, a didactic AI arguing partner designed to support scalable case-based peer-argumentation activities. ArguBot deliberately adopts an opposing stance, challenging students' correct claims with common student misconceptions and countering incorrect claims with arguments grounded in course lecture materials, supported by Retrieval-Augmented Generation (RAG). We conducted a semester-long field study in a graduate robotics course of 172 students, in which both in-class peer-peer argumentation and after-class peer-AI argumentation with ArguBot were offered under regular instructional conditions. Analyses draw on pre-post multiplechoice question responses from students who voluntarily participated in in-class activities (n = 85) and/or engaged with ArguBot after class (n = 68), as well as post-semester perception surveys (n = 57). Results show no statistically significant differences between conditions in error correction or correctness preservation. In contrast, students rated the AI arguing partner significantly lower than a human peer, including reporting lower levels of interest/enjoyment. These findings provide early, ecologically valid evidence on the opportunities and challenges of deploying AI arguing agents in higher education to scale argumentation-based learning.