Coarse-grained tool design (5 phase-level tools) outperforms fine-grained decomposition (14 tools) for LLM-based ISD agents¶

Claim · All claims
Evidence · 1 study · causal r1 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? A 5 phase-level-tool agent (React-ADDIE) scored higher than the same-theory 14-tool agent (ADDIE-Agent: 82.96). → YoungHoon Jeon 2026

Evidence¶

YoungHoon Jeon 2026¶

YoungHoon Jeon, Suwan Kim, Haein Son, Sookbun Lee, Yeil Jeong, Unggi Lee. (2026). ISD-Agent-Bench: A Comprehensive Benchmark for Evaluating LLM-based Instructional Design Agents. Preprint. https://arxiv.org/abs/2602.10620 doi:10.48550/arxiv.2602.10620

q2 · i? · causal · r1

Comparison within the benchmark experiment between React-ADDIE (5 phase-level tools) and ADDIE-Agent (14 specialized tools with fine-grained phase decomposition), both grounded in ADDIE; the conclusion states the coarse-grained design "outperforms fine-grained decomposition (14 tools)".

"A key finding is that coarse-grained tool design (5 phase-level tools) outperforms fine-grained decomposition (14 tools), suggesting that holistic reasoning with systematic structure yields optimal results."

Discussion¶

Related Claims¶

• React-ADDIE, integrating the ADDIE framework with ReAct-style reasoning, achieves the highest benchmark performance (86.49), outperforming pure theory-based agents and technique-only baselines — related
• Theoretical quality of both theory-based and non-theory agents remains stable across Easy, Medium, and Hard scenario difficulty levels — related
• Theoretical quality of agent outputs strongly correlates with benchmark performance (r = 0.656) — related