Chatbot Memory: Uncovering How Mental Effort and Chabot Interactions Affect Short-Term Learning

Developments in artificial intelligence (AI) are transforming everyday tasks, including accessing information, learning, and decision making. Generative AI is representative of these changes as it can generate content traditionally reserved for humans with increased efficiency and reduced effort. This includes technologies like ChatGPT and other tools that exploit large language models, typically taking the form of conversational agents (chatbots). These technologies can be useful for self-regulated learning as is the case for Web browsing. It is, however, unclear whether learning with chatbots may be efficient as opposed to other Web-based approaches given the reduced effort related to chatbot interactions. This study assessed how interacting with a chatbot may affect short-term learning and the role of mental effort. Memory performance was equivalent across participants who either interacted with a chatbot or browsed the Internet to find information for answering essay questions. Differences in self-reported workload were, however, found across conditions.

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Publication Details

Journal
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Published
2025-07-13
DOI
https://doi.org/10.1177/10711813251358242
Citations
1
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
2.48

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article

Chatbot Memory: Uncovering How Mental Effort and Chabot Interactions Affect Short-Term Learning

Noémie Lavoie, Alexandre Marois, Isabelle Lavallée, Gabrielle Boily et al.
1 citations
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
AI in Service Interactions
2.48
article

Chatbot Memory: Uncovering How Mental Effort and Chabot Interactions Affect Short-Term Learning

Noémie Lavoie, Alexandre Marois, Isabelle Lavallée, Gabrielle Boily, Jonay Ramón Alamán, Bérénice Desrosiers
article en
1 citations

Abstract

Developments in artificial intelligence (AI) are transforming everyday tasks, including accessing information, learning, and decision making. Generative AI is representative of these changes as it can generate content traditionally reserved for humans with increased efficiency and reduced effort. This includes technologies like ChatGPT and other tools that exploit large language models, typically taking the form of conversational agents (chatbots). These technologies can be useful for self-regulated learning as is the case for Web browsing. It is, however, unclear whether learning with chatbots may be efficient as opposed to other Web-based approaches given the reduced effort related to chatbot interactions. This study assessed how interacting with a chatbot may affect short-term learning and the role of mental effort. Memory performance was equivalent across participants who either interacted with a chatbot or browsed the Internet to find information for answering essay questions. Differences in self-reported workload were, however, found across conditions.

Proceedings of the Human Factors and Ergonomics Society Annual MeetingVol. 69(1)
Université de Sherbrooke (CA), University of Lancashire (GB), Université Laval (CA)
Université Laval, Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 10%
AI in Service Interactions
2.48
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Chatbot Memory: Uncovering How Mental Effort and Chabot Interactions Affect Short-Term Learning — Noémie Lavoie, Alexandre Marois, et al. · Proceedings of the Human Factors and Ergonomics Society Annual Meeting (2025) | TGRS Research Map | TGRS