To Improve Literacy, Improve Equality in Education, Not Large Language Models

Bibliographic Data

ID7150355
AuthorsSamuel H Forbe (0000-0003-1022-4676, Department of Psychology Durham University, corresponding author), Olivia Guest (0000-0002-1891-0972, Donders Institute for Brain, Cognition, and Behaviour Radboud University)
Year2025
Volume49
Issue4
Pagese70058-e70058
Publication date2025-04-01
Open AccessYes
TypeARTICLE
VenueCognitive Science (JOURNAL)
Journal identifiersISSN: 0364-0213 • E-ISSN: 1551-6709
PublisherWiley (PUBLISHER • GB)
DOI10.1111/cogs.70058
PMID40168502
OpenAlexW4409099048
LanguageEN
Citations received2
References cited29

Huettig and Christiansen in an earlier issue argue that large language models (LLMs) are beneficial to address declining cognitive skills, such as literacy, through combating imbalances in educational equity. However, we warn that this technosolutionism may be the wrong frame. LLMs are labor intensive, are economically infeasible, and pollute the environment, and these properties may outweigh any proposed benefits. For example, poor quality air directly harms human cognition, and thus has compounding effects on educators' and pupils' ability to teach and learn. We urge extreme caution in facilitating the use of LLMs, which like much of modern academia run on private technology sector infrastructure, in classrooms lest we further normalize: pupils losing their right to privacy and security, reducing human contact between learner and educator, deskilling teachers, and polluting the environment. Cognitive scientists instead can learn from past mistakes with the petrochemical and tobacco industries and consider the harms to cognition from LLMs

Cognition · Deskilling · Literacy · Pedagogy · Political science · Educational Strategies and Epistemologies · Engineering · Law · Misinformation and Its Impacts · Psychology · Social Media and Politics

Tools

Open DOIOpen Access