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Andrew Franze School of Psychology, University of South Australia, Australia

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Christina R. Galanis College of Education, Psychology & Social Work, Flinders University, Australia

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Daniel L. King College of Education, Psychology & Social Work, Flinders University, Australia

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Abstract

Social chatbots powered by artificial intelligence (AI) may be particularly appealing to individuals with social deficits or conditions that affect their social functioning. In this letter, we discuss some of the noteworthy characteristics of social chatbots and how they may influence adaptive and maladaptive behaviors, including the potential for ‘dependency’ on chatbots. We call for more independent studies to evaluate the potential developmental and therapeutic effects of this increasingly popular technology.

Abstract

Social chatbots powered by artificial intelligence (AI) may be particularly appealing to individuals with social deficits or conditions that affect their social functioning. In this letter, we discuss some of the noteworthy characteristics of social chatbots and how they may influence adaptive and maladaptive behaviors, including the potential for ‘dependency’ on chatbots. We call for more independent studies to evaluate the potential developmental and therapeutic effects of this increasingly popular technology.

The advent and rapid progression of artificial intelligence has major implications for human development. One area of growing interest is the socialization potential of chatbots, particularly for individuals with less developed social skills, fewer social opportunities, and/or conditions that limit their social functioning. Social chatbots are online conversational agents powered by complex artificial intelligence (AI) and language-processing models which allow human-like text interactions between users and the chatbot (Pentina, Hancock, & Xie, 2023). These chatbots were built to instantaneously produce natural linguistic text responses to queries and prompts inputted by the user (Pentina et al., 2023). Social chatbots have recently been adopted by mainstream social networking platforms, following the release of the ChatGPT chatbot application in November 2022 (Haque, 2022). Some companies have already adapted the ChatGPT software to create their own social chatbots for their users, as demonstrated by the release of Snapchat's ‘My AI’ chatbot in April 2023 (Heath, 2023). According to Snapchat CEO Evan Spiegel, the company envisions that “in addition to talking to our friends and family every day, we're going to talk to AI every day” (Heath, 2023).

Notably, among diverse populations with social deficits, social chatbot applications may be particularly appealing to individuals on the autism spectrum, who may view the technology as a viable, and in some cases preferable, alternative to human interaction. Studies have shown that adolescents with autism spectrum disorder (ASD) tend to report having more difficulties with social interactions and fewer friendships compared to their typically-developed peers (Chaturvedi, Verma, Das, & Dwivedi, 2023; Petrina, Carter, & Stephenson, 2014). These young people report having less contact with friends outside of school hours compared to other students (Petrina et al., 2014). This may be due to difficulties in reading social cues in face-to-face interactions, as well as their susceptibility to feeling socially anxious or fearing negative evaluation from peers (Chaturvedi et al., 2023). For these reasons, individuals with ASD may gravitate toward companionship with online social chatbots because it offers a safe means of rehearsing social interaction with limited to no risk of negative judgment based on appearance or communication style (Ali, Zhang, Tauni, & Shahzad, 2023).

In our view, research should examine the important features of chatbots and chatbot interactions that make them appealing to individuals with ASD and other conditions that involve social deficits or that limit social functioning (e.g., social anxiety). Some industry-linked research has already promoted the benefits of commercial chatbot applications (e.g., Woebot); however, independent evaluation of the social skill training and therapeutic potential of social chatbots is currently lacking. There is a need for studies that investigate the ways in which social chatbots may empower some individuals to overcome social deficits, as well as studying the conditions under which individuals may engage with chatbot technology in unhealthy ways, including excessive use, dependency, and withdrawal from human socializing. Some researchers have proposed that chatbots may assist individuals to develop useful skills to initiate conversations and practice conversational skills. However, there may be some social risks, including the possibility that certain chatbot interactions may reinforce poor social habits and faulty social beliefs and expectations.

To highlight some noteworthy chatbot characteristics, it is observed that some chatbots have a generally servile quality and are receptive to almost all forms of user input, and require minimal input or attention to social etiquette to provide extensive responses in conversation. Some individuals may therefore become accustomed to: controlling the conversation and steering it toward any topic of the user's choosing; ‘interrupting’ or ‘skipping’ through a conversation partner's responses; and electing to converse repeatedly and repetitiously on a single topic. Chatbot conversations may also be paused, delayed, or terminated by the user without any social consequences. In this way, certain chatbot interactions may be counterproductive to social skill development and maintenance, and further development may be necessary for chatbots to be a useful and therapeutic tool.

Given the recency of advanced chatbots (e.g., GPT-4 technology), the research literature on human-chatbot relationships and their social outcomes is quite limited (Pentina et al., 2023). Recent survey studies have found that a social chatbot can provide feelings of companionship and decrease feelings of loneliness (Ali et al., 2023). However, there is a dearth of research on how different populations at different levels of social functioning may be affected by their use of the technology, and their potential risk of prioritizing chatbots for social interaction in unhealthy ways. Research suggests that individuals with ASD, for example, are more likely to become excessively involved in online chatting and that social anxiety may contribute to problematic levels of social media use (Chaturvedi et al., 2023; Westby, 2018). Ali et al. (2023) propose that social chatbots may relieve the distress of social anxiety and fear of negative evaluation, but that this relief may develop into a form of dependency that negatively impacts on real-world relationships. Similarly, Westby (2018) reports that increased digital screen time among individuals with ASD may interfere with social and communication skills, such as maintaining eye contact, reading facial expressions, and verbal communication. These propositions warrant further empirical investigation using controlled and longitudinal designs, and researchers should consider leveraging the insights and support of parents, other family members, teachers, and therapists to a gain a holistic understanding of the users' relationship to the technology.

As chatbots and AI-based social technologies continue to advance, it is critical that researchers investigate the myriad ways that these technologies may influence vulnerable users who may be particularly drawn to them. A growing industry-led discourse and public conversation has referred to many positive applications and outcomes of AI-driven chat technologies, therefore it is important that these technologies are subject to independent scientific evaluation. Strong evidence is needed to guide safe and responsible use of chatbot technology, to inform AI policies and practice involving chatbots (e.g., school-based and health interventions), as well as inform the design of social chatbot applications in ways that maximize their benefit for users.

Funding sources

The authors received no financial support for the research, authorship, and/or publication of this article.

Authors’ contributions

AF wrote the first draft of the paper with substantial contributions from all co-authors. All authors provided edits and have approved the final submitted version of the manuscript.

Conflict of interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

References

  • Ali, F., Zhang, Q., Tauni, M. Z., & Shahzad, K. (2023). Social chatbot: My friend in My distress. International Journal of Human–Computer Interaction, 111. https://doi.org/10.1080/10447318.2022.2150745.

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  • Chaturvedi, R., Verma, S., Das, R., & Dwivedi, Y. K. (2023). Social companionship with artificial intelligence: Recent trends and future avenues. Technological Forecasting and Social Change, 193, 122634. https://doi.org/10.1016/j.techfore.2023.122634.

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  • Pentina, I., Hancock, T., & Xie, T. (2023). Exploring relationship development with social chatbots: A mixed-method study of replika. Computers in Human Behavior, 140, 107600. https://doi.org/10.1016/j.chb.2022.107600.

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  • Petrina, N., Carter, M., & Stephenson, J. (2014). The nature of friendship in children with autism spectrum disorders: A systematic review. Research in Autism Spectrum Disorders, 8, 111126. https://doi.org/10.1016/j.rasd.2013.10.016.

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  • Westby, C. (2018). Why children with autism are more at risk for the negative effects of screen time. Word of Mouth, 29, 913. https://doi.org/10.1177/1048395018772844b.

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    • Export Citation
  • Ali, F., Zhang, Q., Tauni, M. Z., & Shahzad, K. (2023). Social chatbot: My friend in My distress. International Journal of Human–Computer Interaction, 111. https://doi.org/10.1080/10447318.2022.2150745.

    • Search Google Scholar
    • Export Citation
  • Chaturvedi, R., Verma, S., Das, R., & Dwivedi, Y. K. (2023). Social companionship with artificial intelligence: Recent trends and future avenues. Technological Forecasting and Social Change, 193, 122634. https://doi.org/10.1016/j.techfore.2023.122634.

    • Search Google Scholar
    • Export Citation
  • Pentina, I., Hancock, T., & Xie, T. (2023). Exploring relationship development with social chatbots: A mixed-method study of replika. Computers in Human Behavior, 140, 107600. https://doi.org/10.1016/j.chb.2022.107600.

    • Search Google Scholar
    • Export Citation
  • Petrina, N., Carter, M., & Stephenson, J. (2014). The nature of friendship in children with autism spectrum disorders: A systematic review. Research in Autism Spectrum Disorders, 8, 111126. https://doi.org/10.1016/j.rasd.2013.10.016.

    • Search Google Scholar
    • Export Citation
  • Westby, C. (2018). Why children with autism are more at risk for the negative effects of screen time. Word of Mouth, 29, 913. https://doi.org/10.1177/1048395018772844b.

    • Search Google Scholar
    • Export Citation
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Dr. Zsolt Demetrovics
Institute of Psychology, ELTE Eötvös Loránd University
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Phone: +36-1-461-2681
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2022  
Web of Science  
Total Cites
WoS
5713
Journal Impact Factor 7.8
Rank by Impact Factor

Psychiatry (SCIE) 18/155
Psychiatry (SSCI) 13/144

Impact Factor
without
Journal Self Cites
7.2
5 Year
Impact Factor
8.9
Journal Citation Indicator 1.42
Rank by Journal Citation Indicator

Psychiatry 35/264

Scimago  
Scimago
H-index
69
Scimago
Journal Rank
1.918
Scimago Quartile Score Clinical Psychology Q1
Medicine (miscellaneous) Q1
Psychiatry and Mental Health Q1
Scopus  
Scopus
Cite Score
11.1
Scopus
Cite Score Rank
Clinical Psychology 10/292 (96th PCTL)
Psychiatry and Mental Health 30/531 (94th PCTL)
Medicine (miscellaneous) 25/309 (92th PCTL)
Scopus
SNIP
1.966

 

 
2021  
Web of Science  
Total Cites
WoS
5223
Journal Impact Factor 7,772
Rank by Impact Factor Psychiatry SCIE 26/155
Psychiatry SSCI 19/142
Impact Factor
without
Journal Self Cites
7,130
5 Year
Impact Factor
9,026
Journal Citation Indicator 1,39
Rank by Journal Citation Indicator

Psychiatry 34/257

Scimago  
Scimago
H-index
56
Scimago
Journal Rank
1,951
Scimago Quartile Score Clinical Psychology (Q1)
Medicine (miscellaneous) (Q1)
Psychiatry and Mental Health (Q1)
Scopus  
Scopus
Cite Score
11,5
Scopus
CIte Score Rank
Clinical Psychology 5/292 (D1)
Psychiatry and Mental Health 20/529 (D1)
Medicine (miscellaneous) 17/276 (D1)
Scopus
SNIP
2,184

2020  
Total Cites 4024
WoS
Journal
Impact Factor
6,756
Rank by Psychiatry (SSCI) 12/143 (Q1)
Impact Factor Psychiatry 19/156 (Q1)
Impact Factor 6,052
without
Journal Self Cites
5 Year 8,735
Impact Factor
Journal  1,48
Citation Indicator  
Rank by Journal  Psychiatry 24/250 (Q1)
Citation Indicator   
Citable 86
Items
Total 74
Articles
Total 12
Reviews
Scimago 47
H-index
Scimago 2,265
Journal Rank
Scimago Clinical Psychology Q1
Quartile Score Psychiatry and Mental Health Q1
  Medicine (miscellaneous) Q1
Scopus 3593/367=9,8
Scite Score  
Scopus Clinical Psychology 7/283 (Q1)
Scite Score Rank Psychiatry and Mental Health 22/502 (Q1)
Scopus 2,026
SNIP  
Days from  38
submission  
to 1st decision  
Days from  37
acceptance  
to publication  
Acceptance 31%
Rate  

2019  
Total Cites
WoS
2 184
Impact Factor 5,143
Impact Factor
without
Journal Self Cites
4,346
5 Year
Impact Factor
5,758
Immediacy
Index
0,587
Citable
Items
75
Total
Articles
67
Total
Reviews
8
Cited
Half-Life
3,3
Citing
Half-Life
6,8
Eigenfactor
Score
0,00597
Article Influence
Score
1,447
% Articles
in
Citable Items
89,33
Normalized
Eigenfactor
0,7294
Average
IF
Percentile
87,923
Scimago
H-index
37
Scimago
Journal Rank
1,767
Scopus
Scite Score
2540/376=6,8
Scopus
Scite Score Rank
Cllinical Psychology 16/275 (Q1)
Medicine (miscellenous) 31/219 (Q1)
Psychiatry and Mental Health 47/506 (Q1)
Scopus
SNIP
1,441
Acceptance
Rate
32%

 

Journal of Behavioral Addictions
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Journal of Behavioral Addictions
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  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Joshua GRUBBS (University of New Mexico, Albuquerque, NM, USA)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
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  • Chih-Hung KO (Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Taiwan)
  • Shane KRAUS (University of Nevada, Las Vegas, NV, USA)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
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  • Katerina LUKAVSKA (Charles University, Prague, Czech Republic)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Gemma MESTRE-BACH (Universidad Internacional de la Rioja, La Rioja, Spain)
  • Astrid MÜLLER (Hannover Medical School, Germany)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Ståle PALLESEN (University of Bergen, Norway)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Michael SCHAUB (University of Zurich, Switzerland)
  • Marcantanio M. SPADA (London South Bank University, United Kingdom)
  • Daniel SPRITZER (Study Group on Technological Addictions, Brazil)
  • Dan J. STEIN (University of Cape Town, South Africa)
  • Sherry H. STEWART (Dalhousie University, Canada)
  • Attila SZABÓ (Eötvös Loránd University, Hungary)
  • Hermano TAVARES (Instituto de Psiquiatria do Hospital das Clínicas da Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Aviv M. WEINSTEIN (Ariel University, Israel)
  • Anise WU (University of Macau, Macao, China)

 

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