Cognitive ease at a cost: LLMs reduce mental effort but compromise depth in student scientific inquiry

  • This study explores the cognitive load and learning outcomes associated with using large language models (LLMs) versus traditional search engines for information gathering during learning. A total of 91 university students were randomly assigned to either use ChatGPT3.5 or Google to research the socio-scientific issue of nanoparticles in sunscreen to derive valid recommendations and justifications. The study aimed to investigate potential differences in cognitive load, as well as the quality and homogeneity of the students' recommendations and justifications. Results indicated that students using LLMs experienced significantly lower cognitive load. However, despite this reduction, these students demonstrated lower-quality reasoning and argumentation in their final recommendations compared to those who used traditional search engines. Further, the homogeneity of the recommendations and justifications did not differ significantly between the two groups, suggesting that LLMs did notThis study explores the cognitive load and learning outcomes associated with using large language models (LLMs) versus traditional search engines for information gathering during learning. A total of 91 university students were randomly assigned to either use ChatGPT3.5 or Google to research the socio-scientific issue of nanoparticles in sunscreen to derive valid recommendations and justifications. The study aimed to investigate potential differences in cognitive load, as well as the quality and homogeneity of the students' recommendations and justifications. Results indicated that students using LLMs experienced significantly lower cognitive load. However, despite this reduction, these students demonstrated lower-quality reasoning and argumentation in their final recommendations compared to those who used traditional search engines. Further, the homogeneity of the recommendations and justifications did not differ significantly between the two groups, suggesting that LLMs did not restrict the diversity of students’ perspectives. These findings highlight the nuanced implications of digital tools on learning, suggesting that while LLMs can decrease the cognitive burden associated with information gathering during a learning task, they may not promote deeper engagement with content necessary for high-quality learning per se.show moreshow less

Download full text files

Export metadata

Statistics

Number of document requests

Additional Services

Share in Twitter Search Google Scholar
Metadaten
Author:Matthias Stadler, Maria Bannert, Michael SailerORCiDGND
URN:urn:nbn:de:bvb:384-opus4-1146732
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/114673
ISSN:0747-5632OPAC
Parent Title (English):Computers in Human Behavior
Publisher:Elsevier BV
Type:Article
Language:English
Year of first Publication:2024
Publishing Institution:Universität Augsburg
Release Date:2024/08/02
Volume:160
First Page:108386
DOI:https://doi.org/10.1016/j.chb.2024.108386
Institutes:Philosophisch-Sozialwissenschaftliche Fakultät
Philosophisch-Sozialwissenschaftliche Fakultät / Empirische Bildungsforschung
Philosophisch-Sozialwissenschaftliche Fakultät / Empirische Bildungsforschung / Lehrstuhl für Learning Analytics and Educational Data Mining
Dewey Decimal Classification:1 Philosophie und Psychologie / 15 Psychologie / 150 Psychologie
3 Sozialwissenschaften / 37 Bildung und Erziehung / 370 Bildung und Erziehung
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)