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ChatGPT: perspectives from human–computer interaction and psychology

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Liu, Jiaxi·2024·Frontiers in Artificial Intelligence
DOI 10.3389/frai.2024.1418869PMC PMC11217544
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Abstract

The release of GPT-4 has garnered widespread attention across various fields, signaling the impending widespread adoption and application of Large Language Models (LLMs). However, previous research has predominantly focused on the technical principles of ChatGPT and its social impact, overlooking its effects on human–computer interaction and user psychology. This paper explores the multifaceted impacts of ChatGPT on human–computer interaction, psychology, and society through a literature review. The author investigates ChatGPT’s technical foundation, including its Transformer architecture and RLHF (Reinforcement Learning from Human Feedback) process, enabling it to generate human-like responses. In terms of human–computer interaction, the author studies the significant improvements GPT models bring to conversational interfaces. The analysis extends to psychological impacts, weighing the potential of ChatGPT to mimic human empathy and support learning against the risks of reduced interpersonal connections. In the commercial and social domains, the paper discusses the applications of ChatGPT in customer service and social services, highlighting the improvements in efficiency and challenges such as privacy issues. Finally, the author offers predictions and recommendations for ChatGPT’s future development directions and its impact on social relationships.

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  • Data security concerns stem from security risks relating to sensitive health data provided by users or by clinicians.

    “Data security concerns (mentioned by 13 articles) stem from security risks relating to sensitive health data provided by users or by clinicians36–39,44,47,50,51,55.”

    From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [38]

    The Psychological support section says that sharing sensitive information with AI for mental health therapy poses privacy and data-security risks. The paper also discusses doctors using patients’ medical data, but the passages do not connect those risks specifically to data provided by clinicians. Page numbers are unavailable in our copy. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Lack of genuine empathy in LLM-CBs may lead to exposure to harmful content and may be especially harmful for individuals with mental disorders.

    “Lack of genuine empathy may lead to exposure to harmful content38 and may be especially harmful for individuals with mental disorders46.”

    From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [38]

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References 86 · Cited by 99

Research works printed but not linked: 61

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Authors
Liu, J.
Affiliations
Nanyang Technological University (SG)
Corresponding author
Liu, Jiaxi
Venue
Frontiers in Artificial Intelligence
Publisher
Frontiers Media SA
Published
2024-06-18
Volume
7
Pages
1418869-1418869
ISSN
2624-8212 (ISSN-L)
DOI
10.3389/frai.2024.1418869
PMC
PMC11217544
OpenAlex
W4399788390
Cited by
99
References
86
Citations by year
2026: 36 · 2025: 59 · 2024: 4
FWCI
3.40
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Artificial Intelligence (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Nanyang Technological University
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
Keywords
ChatGPT, large language models, human-computer interaction, user psychology, conversational interfaces, transformer models, reinforcement learning from human feedback, customer service automation, social services, privacy, interpersonal relationships, future of AI

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