Large Language Models as Research Assistants: A Cross-Disciplinary Systematic Literature Review

Large language models have been absorbed into research practice across nearly every discipline in under four years, faster than the evidence base needed to evaluate them could be assembled. This review synthesises 124 sources published between January 2020 and June 2026, identified through structured searches of seven databases, and organises the evidence by research lifecycle stage rather than by model architecture. Randomised evidence shows substantial gains on bounded, verifiable, language-intensive work - a forty per cent reduction in task time in one experiment, twelve per cent more tasks completed at twenty-five per cent greater speed in another - with effects concentrated among initially lower-performing users, alongside sharp degradation on tasks just beyond the model's competence. Reliability failures are structural rather than incidental: fabricated bibliographic references fell from roughly fifty-five per cent in GPT-3.5 to eighteen per cent in GPT-4, an improvement far short of what unverified citation practice requires, while corpus analysis indicates at least 13.5 per cent of 2024 biomedical abstracts show signs of machine assistance. Governance has converged on three norms - no machine authorship, mandatory disclosure, and exclusion from confidential peer review - while leaving verification, provenance and researcher training largely unaddressed. The review proposes a five-level scheme of research autonomy and a verification protocol calibrated to the cost of undetected error, and argues that the field's central deficit is evaluative: benchmarks measure question answering, not whether assisted research is better research.