ArguAgent: AI-Supported Real-Time Grouping for Productive Argumentation in STEM Classrooms
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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Computer Science > Artificial Intelligence arXiv:2604.23449 (cs) [Submitted on 25 Apr 2026] Title:ArguAgent: AI-Supported Real-Time Grouping for Productive Argumentation in STEM Classrooms Authors:Jennifer Kleiman, Yizhu Gao, Xin Xia, Zhaoji Wang, Zipei Zhu, Jongchan Park, Xiaoming Zhai View a PDF of the paper titled ArguAgent: AI-Supported Real-Time Grouping for Productive Argumentation in STEM Classrooms, by Jennifer Kleiman and 6 other authors View PDF HTML (experimental) 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 {\alpha}\alpha {\alpha} = 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. Comments: Full paper accepted to the 27th International Conference on AI in Education (AIED 2026). AIED Proceedings to be released Summer 2026 Subjects: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) Cite as: arXiv:2604.23449 [cs.AI] (or arXiv:2604.23449v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2604.23449 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journal reference: International Conference on Artificial Intelligence in Education, AIED 2026 Submission history From: Jennifer Kleiman [view email] [v1] Sat, 25 Apr 2026 21:38:31 UTC (34 KB) Full-text links: Access Paper: View a PDF of the paper titled ArguAgent: AI-Supported Real-Time Grouping for Productive Argumentation in STEM Classrooms, by Jennifer Kleiman and 6 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-04 Change to browse by: cs cs.HC References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic…
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