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

Forbes, Samuel H.; Guest, Olivia

Abstract

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.

Citation

Forbes, S. H., & Guest, O. (2025). To Improve Literacy, Improve Equality in Education, Not Large Language Models. Cognitive Science: A Multidisciplinary Journal, 49(4), Article e70058. https://doi.org/10.1111/cogs.70058

• Journal Article Type: Letter
• Acceptance Date: Mar 11, 2025
• Publication Date: 2025-04
• Deposit Date: Apr 10, 2025
• Publicly Available Date: Apr 11, 2025
• Journal: Cognitive Science
• Print ISSN: 0364-0213
• Electronic ISSN: 1551-6709
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 49
• Issue: 4
• Article Number: e70058
• DOI: https://doi.org/10.1111/cogs.70058
• Public URL: https://durham-repository.worktribe.com/output/3785717