Scaling Laws for Educational AI Agents
This work addresses the challenge of improving educational AI agents for K-12 learning by introducing a novel structured approach, though it is incremental in applying scaling concepts to a specific domain.
The paper tackles the problem of scaling educational AI agents by proposing a structured scaling law based on dimensions like role definition and skill depth, rather than just model size, and demonstrates its effectiveness with over 330 agent profiles and 1,100+ skill modules, showing predictable performance scaling with profile richness.
While scaling laws for Large Language Models (LLMs) have been extensively studied along dimensions of model parameters, training data, and compute, the scaling behavior of LLM-based educational agents remains unexplored. We propose that educational agent capability scales not merely with the underlying model size, but through structured dimensions that we collectively term the Agent Scaling Law: role definition clarity, skill depth, tool completeness, runtime capability, and educator expertise injection. Central to this framework is AgentProfile, a structured JSON-based specification that serves as the mechanism enabling systematic capability growth of educational agents. We present EduClaw, a profile-driven multi-agent platform that operationalizes this scaling law, demonstrating its effectiveness through the construction and deployment of 330+ educational agent profiles encompassing 1,100+ skill modules across K-12 subjects. Our empirical observations suggest that educational agent performance scales predictably with profile structural richness. We identify two complementary scaling axes -- Tool Scaling and Skill Scaling -- as future directions, arguing that the path to more capable educational AI lies not solely in larger models, but in stronger structured capability systems.