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¶
Claim · All claims
Evidence · 1 study · causalr1·q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? On 1,017 test scenarios evaluated with three LLMs, React-ADDIE achieves the highest mean score (86.49) versus ADDIE-Agent (82.96) and Baseline (84.07). → 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
Benchmark experiment on 1,017 test scenarios with three base LLMs (Gemini-3-Flash, GPT-5-mini, Solar-Pro3), scored by a cross-provider multi-judge protocol. The results section reports "React-ADDIE achieves the highest mean score (86.49)" with the named comparison agents' scores as printed.
"React-ADDIE's integration of ADDIE theory with ReAct-style reasoning outperforms both pure theory-based agents (ADDIE-Agent: 82.96, which uses the same ADDIE framework but with fine-grained 14-tool decomposition) and technique-only approaches (Baseline: 84.07)."
Discussion¶
Related Claims¶
- Coarse-grained tool design (5 phase-level tools) outperforms fine-grained decomposition (14 tools) for LLM-based ISD agents — related
- Agentic frameworks built on GPT-3.5 and GPT-4 show significant performance gains on the HumanEval benchmark over zero-shot baselines — a broader claim this one bears on
- Culture has been mostly ignored as a design construct in the field of instructional design — related
- 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) — related
- Theoretical quality of agent outputs strongly correlates with benchmark performance (r = 0.656) — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
react-addie-highest-isd-benchmark-performance |
title |
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 |
description |
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 |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
generated |
by process:wiki-ingest on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — younghoon-jeon-2026 |
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