Research quality evaluation by AI in the era of Large Language Models: Advantages, disadvantages, and systemic effects

TL;DR

This paper examines how AI, especially Large Language Models, are transforming research quality evaluation by offering advantages over traditional bibliometrics but also raising concerns about biases, transparency, and systemic effects on researcher behavior.

Contribution

It provides a comprehensive review of AI-based research quality scores, comparing them to bibliometrics, and discusses their technical, systemic, and ethical implications.

Findings

LLMs often outperform bibliometrics in accuracy and coverage

Current LLM biases in research evaluation are unknown

AI-based indicators may influence researcher and journal behaviors

Abstract

Artificial Intelligence (AI) technologies like ChatGPT now threaten bibliometrics as the primary generators of research quality indicators. They are already used in at least one research quality evaluation system and evidence suggests that they are used informally by many peer reviewers. Since using bibliometrics to support research evaluation continues to be controversial, this article reviews the corresponding advantages and disadvantages of AI-generated quality scores. From a technical perspective, generative AI based on Large Language Models (LLMs) equals or surpasses bibliometrics in most important dimensions, including accuracy (mostly higher correlations with human scores), and coverage (more fields, more recent years) and may reflect more research quality dimensions. Like bibliometrics, current LLMs do not "measure" research quality, however. On the clearly negative side, LLM biases are currently unknown for research evaluation, and LLM scores are less transparent than citation counts. From a systemic perspective, the key issue is how introducing LLM-based indicators into research evaluation will change the behaviour of researchers. Whilst bibliometrics encourage some authors to target journals with high impact factors or to try to write highly cited work, LLM-based indicators may push them towards writing misleading abstracts and overselling their work in the hope of impressing the AI. Moreover, if AI-generated journal indicators replace impact factors, then this would encourage journals to allow authors to oversell their work in abstracts, threatening the integrity of the academic record.