Evidence map›Paper›PMID 41306682›Full record

ArticleProceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting2025

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

Alexandre Marois, Isabelle Lavallée, Gabrielle Boily, Jonay Ramon Alaman, Bérénice Desrosiers, Noémie Lavoie

Abstract read
In one paragraph

Article in Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Alexandre MaroisÉcole de Psychologie, Université Laval, Québec, Canada.ORCID https://orcid.org/0000-0002-4127-4134
Isabelle LavalléeDépartement de Psychologie, Université de Sherbrooke, Sherbrooke, Canada.
Gabrielle BoilyÉcole de Psychologie, Université Laval, Québec, Canada.
Jonay Ramon AlamanÉcole de Psychologie, Université Laval, Québec, Canada.ORCID https://orcid.org/0000-0002-8642-0422
Bérénice DesrosiersÉcole de Psychologie, Université Laval, Québec, Canada.
Noémie LavoieÉcole de Psychologie, Université Laval, Québec, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligencechatbotlearningmental effortweb-based learning

Identifiers

PMID41306682
PMCPMC12646388

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.