Voting Protocols as Coordination Mechanisms for Role-Constrained Multi-Agent Tutoring Systems
On the map: LLM agents and tool use → Educational agents and math problem solving
Abstract (arXiv)
Agentic tutoring systems introduce a coordination challenge: multiple agents may propose different but reasonable interventions, yet only one response can be delivered to the learner. In this paper, we study how voting protocols shape cooperation among four role-constrained pedagogical agents responsible for scaffolding, misconception, motivation, and metacognition. We compare four voting protocols -- simple, ranked, cumulative, and approval voting -- across two simulated tutoring environments on SciQ and HumanEval benchmarks. Rather than using voting as a simple aggregation step, we use it to analyze how collective decision rules shape coordination under partial pedagogical conflict. Across 1,200 simulated interactions, we find that agent deliberation and voting protocol type frequently change which response ultimately wins, showing that both meaningfully shape the collective decision. Different voting rules also produce distinct coordination behaviors, and even brief tutoring turns show measurable learning gains in simulated students. Overall, we show that protocol choice is associated with distinct coordination patterns among role-specialized pedagogical agents.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use. Builds on 10 of its 21 references that are in the map. First posted on arXiv 6 Jun 2026.
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- 2025Embracing Imperfection: Simulating Students with Diverse Cognitive Levels Using LLM-based Agents26 May
- 2024TeachTune: Reviewing Pedagogical Agents Against Diverse Student Profiles with Simulated Students5 Oct
Other references · 8
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- 2017Crowdsourcing Multiple Choice Science Questionscited by 765
- 2025Voting or Consensus? Decision-Making in Multi-Agent Debatecited by 59
- 2024Towards the Pedagogical Steering of Large Language Models for Tutoring: A Case Study with Modeling Productive Failurecited by 24
- 2022A Transparency Index Framework for AI in Educationcited by 13
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