Voting Protocols as Coordination Mechanisms for Role-Constrained Multi-Agent Tutoring Systems
arXiv:2606.08030v1 Announce Type: cross Abstract: 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
The rapid advancement in AI agent capabilities is demanding more sophisticated coordination mechanisms to handle complex tasks like AI tutoring effectively.
This research provides a framework for managing multi-agent interactions, which is critical for scaling autonomous AI systems beyond simple, single-task applications.
The explicit application of voting protocols to AI agent coordination introduces a structured approach to resolving conflicting interventions in complex multi-agent environments.
- · AI education platforms
- · Multi-agent system developers
- · EdTech sector
- · AI governance researchers
- · Monolithic AI system architectures
- · Simple AI tutoring systems
Improved performance and reliability of multi-agent AI systems, particularly in educational contexts.
Expansion of AI agent applications to other fields requiring complex coordination and decision-making among specialized agents.
Potential for new 'AI economies' where agents negotiate and vote on actions, requiring novel regulatory and ethical frameworks.
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Read at arXiv cs.AI