Theory-based agents significantly outperform non-theory agents on Problem-centered design (d = 0.28) and Activation (d = 0.29), but non-theory agents score higher on Demonstration (d = -0.23)¶

Claim · All claims
Evidence · 1 study (2 entries) · causal r1 · q2 · i1 small
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i1 Theory-based agents significantly outperform non-theory agents on Problem-centered design (d = 0.28) and Activation of prior knowledge (d = 0.29), both p < 0.001. → YoungHoon Jeon 2026 q2 i1 Non-theory agents score higher on Demonstration (d = -0.23, p < 0.001), suggesting a tradeoff between systematic coverage and depth in concrete examples. → YoungHoon Jeon 2026 (2)

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 · i1 · causal · r1

Group comparison of theory-based agents (React-ADDIE, ADDIE-Agent, Dick-Carey, RPISD) versus non-theory agents (Baseline, EduPlanner) across MPI dimensions from the 5,345-output labeling analysis; the printed effect sizes are d = 0.28 and d = 0.29, both p < 0.001.

"theory-based agents significantly outperform non-theory agents on Problem-centered design (P:𝑑= 0.28) and Activation of prior knowledge (A: 𝑑= 0.29), both𝑝< 0.001."

YoungHoon Jeon 2026 (2)¶

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

q2 · i1 · design · r2

Same MPI dimension comparison; on Demonstration the non-theory group scored higher with d = -0.23, p < 0.001, which the article reads as a tradeoff between systematic coverage and depth in concrete examples.

"non-theory agents score higher on Demonstration (D:𝑑=− 0.23,𝑝< 0.001), suggesting a tradeoff between systematic coverage and depth in concrete examples."

Discussion¶

Related Claims¶

• Theory-based agents show significantly better Objective-Assessment alignment (d = 0.32), while Content-Activity alignment favors non-theory agents (d = -0.19) — related
• Theoretical quality of agent outputs strongly correlates with benchmark performance (r = 0.656) — related
• Theoretical quality of both theory-based and non-theory agents remains stable across Easy, Medium, and Hard scenario difficulty levels — related
• 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