ArguAgent: AI-Supported Real-Time Grouping for Productive Argumentation in STEM Classrooms

• URL: http://arxiv.org/abs/2604.23449v1
• Date: Sat, 25 Apr 2026 21:38:31 GMT
• Title: ArguAgent: AI-Supported Real-Time Grouping for Productive Argumentation in STEM Classrooms
• Abstract summary: ArguAgent is an AI-powered system that creates groups based on students' stances and argumentation skills.<n>It constrains argumentation quality differences to +/-1 level on a validated learning progression.<n>It achieves 95.4% of groups that meet both design criteria, a 3.2x improvement over random assignment.
• Score: 5.090665056048702
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Argumentation is a core practice in STEM education, but its productivity depends on who participates and how they interact. Higher-achieving students often dominate the talk and decision-making, while lower-achieving peers may disengage, defer, or comply without contributing substantive reasoning. Forming groups strategically based on students' stances and argumentation skills could help foster inclusive, evidence-based discourse. In practice, however, teachers are constrained in implementing this grouping strategy because it requires real-time insight into students' positions and the quality of their argumentation, information that is difficult to assess reliably and at scale during instruction. We present a generative AI-powered system, ArguAgent, that creates groups optimizing for stance heterogeneity while constraining argumentation quality differences to +/-1 level on a validated learning progression. ArguAgent uses a two-component assessment pipeline: first scoring student arguments on a 0-4 rubric, then clustering positions via semantic analysis. We validated the scoring component against human expert consensus (Krippendorff's ααα = 0.817) using 200 expert-generated scores. Testing three OpenAI models (GPT-4o-mini, GPT-5.1, GPT-5.2) with identical calibrated prompts, we found that systematic prompt engineering informed by human disagreement analysis contributed 89% of scoring improvement (QWK: 0.531 to 0.686), while model upgrades contributed an additional 11% (QWK: 0.686 to 0.708). Simulation testing across 100 classes demonstrated that the grouping algorithm achieves 95.4% of groups that meet both design criteria, a 3.2x improvement over random assignment. These results suggest ArguAgent can enable real-time, theoretically grounded grouping that promotes productive STEM argumentation in classrooms.

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