From Text to Discovery: How Are LLMs Reshaping Scientific and Humanistic Research?

2026-06-07

AI Summary

This study addresses the current lack of interdisciplinary understanding regarding the integration pathways, efficacy boundaries, and systemic risks of large language models (LLMs) across natural sciences, social sciences, and humanities. Through a systematic literature review and illustrative case analyses, it critically evaluates the deployment of LLMs throughout the research lifecycle—including hypothesis generation, literature synthesis, data analysis, and scholarly writing. The work identifies ten previously underappreciated systemic risks, such as diminished researcher autonomy, AI-induced confirmation bias, ambiguous authorship, and inequitable access to technology. It further demonstrates how LLMs, while enhancing efficiency, simultaneously introduce challenges like hallucination, irreproducibility, data bias, and model opacity. To guide responsible adoption, the study proposes an interdisciplinary governance framework and a roadmap for explainable AI research in scholarly contexts.

Abstract

Large Language Models (LLMs) are rapidly reshaping academic research across the natural sciences, social sciences, and humanities, yet the scientific community lacks a comprehensive, cross-disciplinary account of how these tools are being integrated, what they deliver, and where they fall short. This paper addresses that gap by mapping their current state and outlining an agenda for their responsible integration into scientific research. Our analysis reveals a consistent pattern: LLMs meaningfully accelerate research workflows -- from hypothesis generation and literature synthesis to data analysis and scientific writing -- while introducing serious challenges related to hallucination, reproducibility, dataset bias, and model opacity. Beyond technical limitations, we identify ten underexplored challenges, including the erosion of researcher autonomy, AI-driven confirmation bias, authorship ambiguity, and unequal access to these technologies -- systemic risks that demand interdisciplinary governance frameworks, robust validation standards, and expanded explainability research.

Problem

• Large Language Models
• scientific research
• interdisciplinary integration
• research reproducibility
• AI ethics

Innovation

• Large Language Models
• scientific research
• research integrity
• AI governance
• interdisciplinary challenges