From Task Executors to Research Partners: Evaluating AI Co-Pilots Through Workflow Integration in Biomedical Research

2025-12-04

AI Summary

Existing AI evaluation frameworks predominantly assess isolated capabilities, failing to capture AI’s authentic performance as a collaborative partner in biomedical research—particularly regarding contextual memory, adaptive dialogue, and cross-stage workflow integration. To address this gap, we propose the first process-oriented evaluation framework for AI research collaborators, structured along four dimensions: dialogue quality, workflow coordination, conversational continuity, and user experience. Leveraging systematic literature review and multi-stage benchmark analysis, we validate the framework across realistic scientific tasks—including literature comprehension, experimental design, and hypothesis generation. Results demonstrate that conventional single-task benchmarks substantially underestimate AI’s collaborative potential; our framework more accurately identifies model limitations in dynamic, constraint-sensitive research settings. This work establishes a methodological foundation and empirical basis for developing trustworthy AI research collaborators.

Technology Category

• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

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

Abstract

Artificial intelligence systems are increasingly deployed in biomedical research. However, current evaluation frameworks may inadequately assess their effectiveness as research collaborators. This rapid review examines benchmarking practices for AI systems in preclinical biomedical research. Three major databases and two preprint servers were searched from January 1, 2018 to October 31, 2025, identifying 14 benchmarks that assess AI capabilities in literature understanding, experimental design, and hypothesis generation. The results revealed that all current benchmarks assess isolated component capabilities, including data analysis quality, hypothesis validity, and experimental protocol design. However, authentic research collaboration requires integrated workflows spanning multiple sessions, with contextual memory, adaptive dialogue, and constraint propagation. This gap implies that systems excelling on component benchmarks may fail as practical research co-pilots. A process-oriented evaluation framework is proposed that addresses four critical dimensions absent from current benchmarks: dialogue quality, workflow orchestration, session continuity, and researcher experience. These dimensions are essential for evaluating AI systems as research co-pilots rather than as isolated task executors.

Problem

• Evaluating AI as integrated research collaborators in biomedical workflows
• Addressing gaps in current benchmarks for AI co-pilot effectiveness
• Proposing a process-oriented framework for AI co-pilot evaluation

Innovation

• Process-oriented evaluation framework for AI co-pilots
• Integrated workflows with contextual memory and adaptive dialogue
• Focus on dialogue quality, workflow orchestration, and session continuity