ISD-Agent-Bench: A Comprehensive Benchmark for Evaluating LLM-based Instructional Design Agents

• URL: http://arxiv.org/abs/2602.10620v1
• Date: Wed, 11 Feb 2026 08:11:31 GMT
• Title: ISD-Agent-Bench: A Comprehensive Benchmark for Evaluating LLM-based Instructional Design Agents
• Authors: YoungHoon Jeon, Suwan Kim, Haein Son, Sookbun Lee, Yeil Jeong, Unggi Lee,
• Abstract summary: We present ISD-Agent-Bench, a comprehensive benchmark comprising 25,795 scenarios generated via a Context Matrix framework.<n>We compare existing ISD agents with novel agents grounded in classical ISD theories such as ADDIE, Dick & Carey, and Rapid Prototyping ISD.<n>Experiments on 1,017 test scenarios demonstrate that integrating classical ISD frameworks with modern ReAct-style reasoning achieves the highest performance.
• Score: 0.6181816879349377
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large Language Model (LLM) agents have shown promising potential in automating Instructional Systems Design (ISD), a systematic approach to developing educational programs. However, evaluating these agents remains challenging due to the lack of standardized benchmarks and the risk of LLM-as-judge bias. We present ISD-Agent-Bench, a comprehensive benchmark comprising 25,795 scenarios generated via a Context Matrix framework that combines 51 contextual variables across 5 categories with 33 ISD sub-steps derived from the ADDIE model. To ensure evaluation reliability, we employ a multi-judge protocol using diverse LLMs from different providers, achieving high inter-judge reliability. We compare existing ISD agents with novel agents grounded in classical ISD theories such as ADDIE, Dick \& Carey, and Rapid Prototyping ISD. Experiments on 1,017 test scenarios demonstrate that integrating classical ISD frameworks with modern ReAct-style reasoning achieves the highest performance, outperforming both pure theory-based agents and technique-only approaches. Further analysis reveals that theoretical quality strongly correlates with benchmark performance, with theory-based agents showing significant advantages in problem-centered design and objective-assessment alignment. Our work provides a foundation for systematic LLM-based ISD research.

Related papers

• RADAR: Revealing Asymmetric Development of Abilities in MLLM Pre-training [59.493415006017635]
  Pre-trained Multi-modal Large Language Models (MLLMs) provide a knowledge-rich foundation for post-training.<n>Current evaluation relies on testing after supervised fine-tuning, which introduces laborious additional training and autoregressive decoding costs.<n>We propose RADAR, an efficient ability-centric evaluation framework for Revealing Asymmetric Development of Abilities in MLLM pRe-training.
  arXiv  Detail & Related papers  (2026-02-13T12:56:31Z)
• Understanding Multi-Agent LLM Frameworks: A Unified Benchmark and Experimental Analysis [2.903627214446312]
  We introduce an architectural taxonomy for systematically comparing multi-agent LLM frameworks along fundamental dimensions.<n>We develop a unified evaluation suite that integrates existing benchmarks under a standardized execution pipeline.<n>Our results show that framework-level design choices alone can increase latency by over 100x, reduce planning accuracy by up to 30%, and lower coordination success from above 90% to below 30%.
  arXiv  Detail & Related papers  (2026-02-03T05:37:56Z)
• MAS-ProVe: Understanding the Process Verification of Multi-Agent Systems [59.20800753428596]
  We present MAS-ProVe, a systematic empirical study of process verification for multi-agent systems (MAS)<n>Our study spans three verification paradigms (LLM-as-a-Judge, reward models, and process reward models)<n>We find that process-level verification does not consistently improve performance and frequently exhibits high variance.
  arXiv  Detail & Related papers  (2026-02-03T03:30:36Z)
• On Generalization in Agentic Tool Calling: CoreThink Agentic Reasoner and MAVEN Dataset [16.921428284844684]
  Generalization across Agentic tool-calling environments remains a key unsolved challenge in developing reliable reasoning systems.<n>We present a framework that augments large language models with a lightweight symbolic reasoning layer for structured decomposition and adaptive tool orchestration.
  arXiv  Detail & Related papers  (2025-10-27T00:58:48Z)
• Can Agents Judge Systematic Reviews Like Humans? Evaluating SLRs with LLM-based Multi-Agent System [1.3052252174353483]
  Systematic Literature Reviews ( SLRs) are foundational to evidence-based research but remain labor-intensive and prone to inconsistency across disciplines.<n>We present an LLM-based SLR evaluation copilot built on a Multi-Agent System (MAS) architecture to assist researchers in assessing the overall quality of the systematic literature reviews.<n>Unlike conventional single-agent methods, our design integrates a specialized agentic approach aligned with PRISMA guidelines to support more structured and interpretable evaluations.
  arXiv  Detail & Related papers  (2025-09-21T21:17:23Z)
• STARec: An Efficient Agent Framework for Recommender Systems via Autonomous Deliberate Reasoning [54.28691219536054]
  We introduce STARec, a slow-thinking augmented agent framework that endows recommender systems with autonomous deliberative reasoning capabilities.<n>We develop anchored reinforcement training - a two-stage paradigm combining structured knowledge distillation from advanced reasoning models with preference-aligned reward shaping.<n>Experiments on MovieLens 1M and Amazon CDs benchmarks demonstrate that STARec achieves substantial performance gains compared with state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-08-26T08:47:58Z)
• Aligning MLLM Benchmark With Human Preferences via Structural Equation Modeling [17.092510377905814]
  evaluating multimodal large language models (MLLMs) remains a fundamental challenge due to a lack of structured, interpretable, and theoretically grounded benchmark designs.<n>We propose a novel framework for aligning MLLM benchmark based on Structural Equation Modeling (SEM) to analyze and quantify the internal validity, dimensional separability, and contribution of benchmark components.<n> Experimental results demonstrate that the proposed benchmark exhibits stronger interpretability, reduced indicator redundancy, and clearer cognitive consistency compared to existing approaches.
  arXiv  Detail & Related papers  (2025-06-13T08:04:56Z)
• BEATS: Bias Evaluation and Assessment Test Suite for Large Language Models [0.0]
  We introduce BEATS, a novel framework for evaluating Bias, Ethics, Fairness, and Factuality in Large Language Models (LLMs)<n>We present a bias benchmark for LLMs that measure performance across 29 distinct metrics.<n>These metrics span a broad range of characteristics, including demographic, cognitive, and social biases, as well as measures of ethical reasoning, group fairness, and factuality related misinformation risk.
  arXiv  Detail & Related papers  (2025-03-31T16:56:52Z)
• ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning [53.817538122688944]
  We introduce Reinforced Meta-thinking Agents (ReMA) to elicit meta-thinking behaviors from Reasoning of Large Language Models (LLMs)<n>ReMA decouples the reasoning process into two hierarchical agents: a high-level meta-thinking agent responsible for generating strategic oversight and plans, and a low-level reasoning agent for detailed executions.<n> Empirical results from single-turn experiments demonstrate that ReMA outperforms single-agent RL baselines on complex reasoning tasks.
  arXiv  Detail & Related papers  (2025-03-12T16:05:31Z)
• Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation [51.99752147380505]
  This paper presents a benchmark self-evolving framework to dynamically evaluate Large Language Models (LLMs) We utilize a multi-agent system to manipulate the context or question of original instances, reframing new evolving instances with high confidence. Our framework widens performance discrepancies both between different models and within the same model across various tasks.
  arXiv  Detail & Related papers  (2024-02-18T03:40:06Z)
• AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents [74.16170899755281]
  We introduce AgentBoard, a pioneering comprehensive benchmark and accompanied open-source evaluation framework tailored to analytical evaluation of LLM agents.<n>AgentBoard offers a fine-grained progress rate metric that captures incremental advancements as well as a comprehensive evaluation toolkit.<n>This not only sheds light on the capabilities and limitations of LLM agents but also propels the interpretability of their performance to the forefront.
  arXiv  Detail & Related papers  (2024-01-24T01:51:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.