Evidence map›Paper›PMID 40438548›Full record

ArticleCurrent psychology (New Brunswick, N.J.)2025

Using large language models to facilitate academic work in the psychological sciences.

Aamir Sohail, Lei Zhang

Abstract read
In one paragraph

Article in Current psychology (New Brunswick, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Aamir SohailCentre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, B15 2TT UK.ORCID 0009-0000-6584-4579
Lei ZhangCentre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, B15 2TT UK.ORCID 0000-0002-9586-595X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Large Language Models (LLMs) have significantly shaped working practices across a variety of fields including academia. Demonstrating a remarkable versatility, these models can generate responses to prompts with information in the form of text, documents, and images, show ability to summarize documents, perform literature searches, and even more, understand human behavior. However, despite providing many clear benefits, barriers remain toward their integration into academic work. Ethical and practical concerns regarding their suitability for various tasks further complicate their appropriate use. Here, we summarize recent advances assessing the capacity of LLMs for different components of academic research and teaching, focusing on three key areas in the psychological sciences: education and assessment, academic writing, and simulating human behavior. We discuss how LLMs can be used to aid each area, describe current challenges and good practices, and propose future directions. In doing so, we aim to increase the awareness and proper use of LLMs in various components of academic work, which will only feature more heavily over time.

Indexed as

AcademiaEducationHuman behaviorLarge Language Models (LLMs)PsychologyTeaching

Identifiers

PMID40438548
PMCPMC12106154

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.