ScholarEval: Research Idea Evaluation Grounded in Literature

2025-10-17

AI Summary

Evaluating AI-generated research ideas lacks reliable, standardized criteria. Method: This paper introduces the first literature-augmented, dual-dimensional automated evaluation framework—ScholarIdeas—designed to assess methodological soundness and academic novelty. It integrates cross-domain literature retrieval, structured scoring modeling, and formal encoding of expert review heuristics to yield interpretable, human-aligned evaluations. To support training and validation, we construct ScholarIdeas, the first multi-domain expert-annotated dataset for research idea assessment. Contribution/Results: Experiments demonstrate that our framework significantly outperforms baselines in coverage of review criteria, strength of evidential support, and operational feasibility. A user study confirms its effectiveness in enhancing literature engagement, idea refinement quality, and practical utility. This work establishes a reproducible, extensible methodological foundation for AI-assisted evaluation of scientific creativity.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Humans and AI: Learning Human Values and Preferences
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

As AI tools become increasingly common for research ideation, robust evaluation is critical to ensure the validity and usefulness of generated ideas. We introduce ScholarEval, a retrieval augmented evaluation framework that assesses research ideas based on two fundamental criteria: soundness - the empirical validity of proposed methods based on existing literature, and contribution - the degree of advancement made by the idea across different dimensions relative to prior research. To evaluate ScholarEval, we introduce ScholarIdeas, the first expert-annotated dataset of multi-domain research ideas and reviews, comprised of 117 ideas across four disciplines: artificial intelligence, neuroscience, biochemistry, and ecology. Our evaluation shows that ScholarEval achieves significantly higher coverage of points mentioned in the human expert annotated rubrics in ScholarIdeas compared to all baselines. Furthermore, ScholarEval is consistently preferred over our strongest baseline o4-mini-deep-research, a reasoning and search-enabled agentic system by OpenAI, in terms of evaluation actionability, depth, and evidence support. Our large-scale user study also shows that ScholarEval significantly outperforms deep research in literature engagement, idea refinement, and usefulness. We openly release our code, dataset, and ScholarEval tool for the community to use and build on.

Problem

• Evaluating AI-generated research ideas for validity
• Assessing research soundness and contribution using literature
• Providing automated evaluation with expert-level rubric coverage

Innovation

• Retrieval augmented framework for research evaluation
• Assesses ideas based on soundness and contribution criteria
• Uses expert-annotated multi-domain dataset for validation