Evidence map›Paper›PMID 40117496›Full record

ArticleJMIR formative research2025

The Impact of ChatGPT Exposure on User Interactions With a Motivational Interviewing Chatbot: Quasi-Experimental Study.

Jiading Zhu, Alec Dong, Cindy Wang, Scott Veldhuizen, Mohamed Abdelwahab, Andrew Brown, Peter Selby, Jonathan Rose

Abstract read
In one paragraph

Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Jiading ZhuThe Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada.ORCID 0009-0006-8997-0995
Alec Dong *Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada.ORCID 0009-0009-9883-8939
Cindy Wang *The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada.ORCID 0009-0005-3798-3981
Scott VeldhuizenINTREPID Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada.ORCID 0000-0003-3969-2756
Mohamed AbdelwahabThe Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada.ORCID 0009-0001-8825-7577
Andrew BrownThe Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada.ORCID 0000-0003-2918-634X
Peter SelbyINTREPID Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada.ORCID 0000-0001-5401-2996
Jonathan RoseThe Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-3551-2175

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The worldwide introduction of ChatGPT in November 2022 may have changed how its users perceive and interact with other chatbots. This possibility may confound the comparison of responses to pre-ChatGPT and post-ChatGPT iterations of pre-existing chatbots, in turn affecting the direction of their evolution. Before the release of ChatGPT, we created a therapeutic chatbot, MIBot, whose goal is to use motivational interviewing to guide smokers toward making the decision to quit smoking. We were concerned that measurements going forward would not be comparable to those in the past, impacting the evaluation of future changes to the chatbot. Objective: The aim of the study is to explore changes in how users interact with MIBot after the release of ChatGPT and examine the relationship between these changes and users' familiarity with ChatGPT. Methods: We compared user interactions with MIBot prior to ChatGPT's release and 6 months after the release. Participants (N=143) were recruited through a web-based platform in November of 2022, prior to the release of ChatGPT, to converse with MIBot, in an experiment we refer to as MIBot (version 5.2). In May 2023, a set of (n=129) different participants were recruited to interact with the same version of MIBot and asked additional questions about their familiarity with ChatGPT, in the experiment called MIBot (version 5.2A). We used the Mann-Whitney U test to compare metrics between cohorts and Spearman rank correlation to assess relationships between familiarity with ChatGPT and other metrics within the MIBot (version 5.2A) cohort. Results: In total, 83(64.3%) participants in the MIBot (version 5.2A) cohort had used ChatGPT, with 66 (51.2%) using it on a regular basis. Satisfaction with MIBot was significantly lower in the post-ChatGPT cohort (U=11,331.0; P=.001), driven by a decrease in perceived empathy as measured by the Average Consultation and Relational Empathy Measure (U=10,838.0; P=.01). Familiarity with ChatGPT was positively correlated with average response length (ρ=0.181; P=.04) and change in perceived importance of quitting smoking (ρ=0.296; P<.001). Conclusions: The widespread reach of ChatGPT has changed how users interact with MIBot. Post-ChatGPT users are less satisfied with MIBot overall, particularly in terms of perceived empathy. However, users with greater familiarity with ChatGPT provide longer responses and demonstrated a greater increase in their perceived importance of quitting smoking after a session with MIBot. These findings suggest the need for chatbot developers to adapt to evolving user expectations in the era of advanced generative artificial intelligence.

Indexed as

Motivational InterviewingSmoking CessationAdultFemaleGenerative Artificial IntelligenceHumansMaleMiddle Agedartificial intelligencechatbotChatGPTconversational agentdigital healthexperimentallarge language modelsmotivational interviewingnatural language processingsmoking cessation

Identifiers

PMID40117496
PMCPMC11952273

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LicenceCC BY
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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.