Useful for Exploration, Risky for Precision: Evaluating AI Tools in Academic Research

TL;DR

This study evaluates AI tools for academic research, highlighting their usefulness for exploration but limitations in precision, transparency, and reproducibility, emphasizing the need for human-centered assessment.

Contribution

It introduces a benchmarking framework combining human-centered and computer-centered metrics to evaluate AI research tools, revealing their strengths and weaknesses.

Findings

Q&A tools provide useful overviews but lack reliability for precise info.

Explainable AI accuracy was low, affecting trust and verification.

Literature review tools support exploration but lack reproducibility and transparency.

Abstract

Artificial intelligence (AI) tools are being incorporated into scientific research workflows with the potential to enhance efficiency in tasks such as document analysis, question answering (Q&A), and literature search. However, system outputs are often difficult to verify, lack transparency in their generation and remain prone to errors. Suitable benchmarks are needed to document and evaluate arising issues. Nevertheless, existing benchmarking approaches are not adequately capturing human-centered criteria such as usability, interpretability, and integration into research workflows. To address this gap, the present work proposes and applies a benchmarking framework combining human-centered and computer-centered metrics to evaluate AI-based Q&A and literature review tools for research use. The findings suggest that Q&A tools can offer valuable overviews and generally accurate summaries; however, they are not always reliable for precise information extraction. Explainable AI (xAI) accuracy was particularly low, meaning highlighted source passages frequently failed to correspond to generated answers. This shifted the burden of validation back onto the researcher. Literature review tools supported exploratory searches but showed low reproducibility, limited transparency regarding chosen sources and databases, and inconsistent source quality, making them unsuitable for systematic reviews. A comparison of these tool groups reveals a similar pattern: while AI tools can enhance efficiency in the early stages of the research workflow and shallow tasks, their outputs still require human verification. The findings underscore the importance of explainability features to enhance transparency, verification efficiency and careful integration of AI tools into researchers' workflows. Further, human-centered evaluation remains an important concern to ensure practical applicability.