[논문]Users' Attachment Styles and ChatGPT Interaction: Revealing Insights into User Experiences

I-Tsen Hsieh; Chang-Hoon Oh

doi:10.9708/jksci.2024.29.03.021

[국내논문] Users' Attachment Styles and ChatGPT Interaction: Revealing Insights into User Experiences

韓國컴퓨터情報學會論文誌 = Journal of the Korea Society of Computer and Information, v.29 no.3, 2024년, pp.21 - 41

I-Tsen Hsieh (Graduate School of Information, Yonsei University) , Chang-Hoon Oh (Graduate School of Information, Yonsei University)

초록
AI-Helper

본 연구는 OpenAI가 개발한 고급 언어 모델인 ChatGPT (Chat Generative Pre-trained Transformer)와 사용자의 애착 유형 간의 관계를 탐구한다. 인공지능(AI)이 점차 일상생활에 통합되면서, 다양한 애착 유형을 가진 개인들이 AI 챗봇과 상호 작용하는 방식을 이해하는 것은 특정 사용자 요구를 충족하고 사용자와 가장 이상적인 방식으로 상호 작용하는 더 나은 사용자 경험을 구축하기 위해 중요하다. 심리학의 애착 이론을 기반으로 한 이 연구에서는 애착 유형이 ChatGPT와 상호 작용에 미치는 영향을 탐구하여 인간과 AI 간의 상호 작용에 대한 이해에서 중요한 공백을 메우고 있다. 예상과는 달리, 애착 유형은 ChatGPT 사용에 유의미한 영향을 미치지 않았다. 애착 유형에 관계없이 중요한 정보를 전달하는 ChatGPT를 완전히 신뢰하는 것을 주저했으며, AI 시스템의 신뢰 문제를 해결해야 할 필요성을 강조한다. 단, 본 연구는 사용자와 ChatGPT 간 독특한 상호 작용에 중점을 두어, 애착 유형이 이러한 상호 작용에 미치는 영향을 해명하여 AI 챗봇의 개인화된 사용자 경험을 개발하는 데에 도움이 되고자 한다. 또, 본 연구는 Perceived Partner Responsiveness Scale의 도입은 사용자가 ChatGPT의 역할에 대한 인식을 평가하는 유용한 도구로 기능하며, AI의 인격화에 대한 관점을 제시한다. 본 연구는 인간과 AI 간의 관계에 대한 넓은 토론에 기여하며, 사용자 중심의 미래를 위해 AI 시스템에 감정 지능을 통합하는 중요성을 강조한다.

Abstract ▼ AI-Helper

This study explores the relationship between users' attachment styles and their interactions with ChatGPT (Chat Generative Pre-trained Transformer), an advanced language model developed by OpenAI. As artificial intelligence (AI) becomes increasingly integrated into everyday life, it is essential to understand how individuals with different attachment styles engage with AI chatbots in order to build a better user experience that meets specific user needs and interacts with users in the most ideal way. Grounded in attachment theory from psychology, we are exploring the influence of attachment style on users' interaction with ChatGPT, bridging a significant gap in understanding human-AI interaction. Contrary to expectations, attachment styles did not have a significant impact on ChatGPT usage or reasons for engagement. Regardless of their attachment styles, hesitated to fully trust ChatGPT with critical information, emphasizing the need to address trust issues in AI systems. Additionally, this study uncovers complex patterns of attachment styles, demonstrating their influence on interaction patterns between users and ChatGPT. By focusing on the distinctive dynamics between users and ChatGPT, our aim is to uncover how attachment styles influence these interactions, guiding the development of AI chatbots for personalized user experiences. The introduction of the Perceived Partner Responsiveness Scale serves as a valuable tool to evaluate users' perceptions of ChatGPT's role, shedding light on the anthropomorphism of AI. This study contributes to the wider discussion on human-AI relationships, emphasizing the significance of incorporating emotional intelligence into AI systems for a user-centered future.

주제어

표/그림 (12)

표 Table 1. Research Questions
표 Table 2. Research Hypothesis
표 Table 3. Situational question
표 Table 4. Attachment style across participants
표 Table 5. Attachment style and ChatGPT usage frequency
표 Table 6. Attachment style and primary purposes for using ChatGPT
표 Table 7. Attachment style and participants' perception of ChatGPT
그림 Fig. 1. The Ranking of purpose of all participants
그림 Fig. 3. Avoidant attachment style's ranking of purpose
그림 Fig. 4. Anxious attachment style's ranking of purpose
그림 Fig. 2. Secure attachment style's ranking of purpose
그림 Fig. 5. Participants' description word cloud

AI 본문요약
AI-Helper

제안 방법

Later, logistic regression was used to analyze the relationship between the PPRS score and the role assigned to ChatGPT by participants. The omnibus tests revealed a significant difference in the hypothesis (p = .
This research paper systematically explores the relationship between attachment styles and users' interactions with ChatGPT. The following sections will discuss attachment theory and its relevance to human-AI interactions, present our methodology for exploring attachment styles and ChatGPT usage patterns, delve into the findings examining the impact of attachment styles on interaction purposes and perceptions, and conclude with implications for designing user-centered AI systems and suggestions for future research.
All information collected in the survey was kept strictly confidential. The survey was divided into four main sections: Demographics, Attachment Style Test, Experience with ChatGPT, and Situational Questions.
Most participants, regardless of their attachment styles, provided clear prompts to ChatGPT, outlining specific research questions. These queries went beyond the instructions provided in the scenario guide and included inquiries about the rate of glacier melting, impacts on polar creatures, and the effects of climate change on specific places.

데이터처리

A One-way ANOVA was conducted to determine whether a PPRS score given from participants would be impacted by their attachment style. The result shows that there were no significant differences between groups, as revealed by the ANOVA, F(2, 319) = 1.

성능/효과

Participants were recruited through an online anonymous survey in English, Korean, and Chinese. A total of 322 valid responses were collected, with 105 males, 216 females, and one individual who identified as others. Regarding language preference, 32 participants completed the survey in English, 158 in Korean, and 132 in Chinese.
Among the analyzed responses, the top ten most frequently occurring words include "search" (10.59%), "engine" (8.69%), "tool" (7.06%), "assistant" (3.80%), "friend" (3.62%), "information" (3.17%), "teacher" (2.35%), "help" (1.72%), and "use" (1.72%)
3%, perceive it as a machine. And in the anxious attachment style, there is a notable split, with 40% perceiving ChatGPT as human and 60% as a machine.
Factors like language usage, user backgrounds, and cultures have an impact. Broader factors and contextual nuances remain unexplored. Future research can consider cultural nuances and previous AI interactions.
Our research has revealed valuable insights into the interplay between attachment styles and user experiences with ChatGPT. Contrary to expectations, attachment styles did not significantly influence the frequency of ChatGPT usage or the primary reasons for engagement. Users, regardless of their attachment styles, showed a common hesitance to fully trust ChatGPT in critical situations, emphasizing the need to address trust issues in AI systems.
This suggests a significant association between participants' perceptions of ChatGPT's role and their PPRS scores. Furthermore, the logistic regression model was found to be significant, indicating its predictive power. The logistic regression found that the odds of mentioning ChatGPT as having a human-like role will increase by 0.
On the other hand, our logistic regression analysis found correlation between descriptions of ChatGPT's role and PPRS scores. Higher scores increased the odds of attributing human-like roles to ChatGPT, indicating users perceived it as more responsive and anthropomorphized its role.
Some said that in an emergency situation, they are not really accustomed to relying solely on ChatGPT in all processes. However, all participants indicated that they expect ChatGPT to perform well in translation.
7% see it more as a machine. In the avoidant attachment style group, 32.7% view ChatGPT as human, while the majority, 67.3%, perceive it as a machine. And in the anxious attachment style, there is a notable split, with 40% perceiving ChatGPT as human and 60% as a machine.
In the demographics section, participants provided information including, gender, age, usage of ChatGPT, primary purpose for using ChatGPT, and their perception of ChatGPT. Next, they took the Adult Attachment Questionnaire (AAQ) to determine attachment style.
Then categorized ultimately into two groups: human-like and machine-like. In the secure attachment style group, 42.3% attribute human-like qualities to ChatGPT, while 57.7% see it more as a machine. In the avoidant attachment style group, 32.
Additionally, we found that they are the only group that says "thank" at the end of their prompt. It can be concluded that they also express gratitude, thereby contributing to positive interaction with ChatGPT.
It was also found that this group showed the highest ratio of using positive phrases, such as "good," "great," and "best," to describe ChatGPT as a "good assistant," "great AI chatbot," and "intangible but best secretary for any computer-related work" (S63).
Out of the participants, 62 reported using ChatGPT daily, 152 reported using it on a weekly basis, 76 used it monthly, and 32 indicated rare usage. Of note, all participants identified themselves as ChatGPT users and provided responses based on this self-identification, which ensures the collection of relevant data and alignment with the study objectives.
The age distribution was as follows: 144 individuals were aged 18-25, 98 were aged 26-35, 52 were aged 36-45, 24 were aged 46-55, and 4 were aged 55 and above. Out of the participants, 62 reported using ChatGPT daily, 152 reported using it on a weekly basis, 76 used it monthly, and 32 indicated rare usage. Of note, all participants identified themselves as ChatGPT users and provided responses based on this self-identification, which ensures the collection of relevant data and alignment with the study objectives.
This suggests a direction for future research. Scales such as PPRS could be expanded and adjusted to comprehensively measure various aspects of user satisfaction, engagement, and emotional connection with AI chatbots.
The analysis of users' perceptions revealed a consistent dual-role perception of ChatGPT as both a functional tool and a human-like figure across diverse attachment styles
Furthermore, the logistic regression model was found to be significant, indicating its predictive power. The logistic regression found that the odds of mentioning ChatGPT as having a human-like role will increase by 0.61% for each additional 1 point in PPRS score.
The other Chi-square test of independence was performed to determine whether participants' perception of ChatGPT differed based on their attachment style
The perception of ChatGPT's role was collected by allowing participants to describe how they view ChatGPT’s identity freely
The positive correlation between PPRS scores and users' perceptions suggests that perceived responsiveness enhances the anthropomorphism of AI, contributing to a positive user experience.
A One-way ANOVA was conducted to determine whether a PPRS score given from participants would be impacted by their attachment style. The result shows that there were no significant differences between groups, as revealed by the ANOVA, F(2, 319) = 1.53, p = .217. This suggests that attachment style may not have a significant influence on the participants' PPRS score toward ChatGPT.
Unlike secure attachment style, those with an avoidant attachment style displayed a more reserved and task-oriented interaction with ChatGPT. They tended to briefly state their emotional state, seeking solutions or comfort without providing extensive elaboration.
Contrary to expectations, attachment styles did not significantly influence the frequency of ChatGPT usage or the primary reasons for engagement. Users, regardless of their attachment styles, showed a common hesitance to fully trust ChatGPT in critical situations, emphasizing the need to address trust issues in AI systems. The analysis of users' perceptions revealed a consistent dual-role perception of ChatGPT as both a functional tool and a human-like figure across diverse attachment styles.
Delving into the responses of different attachment style groups. We found that participants with an anxious attachment style tend to use more than two identity descriptions in ChatGPT, along with additional explanations. Participants with an avoidant attachment style tend to use phrases such as "I think" and "to me" frequently in their responses.
While attachment styles play a crucial role in shaping human relationships, our findings suggest that ChatGPT's perceived responsiveness is more likely to depend on general expectations and experiences, rather than just individual attachment styles.

후속연구

Another contribution is the discovery of the potential use of the PPRS in human-AI interaction, which has not yet been widely used in this context.
PPRS can be considered a valuable tool for enhancing users' overall chatbot experience. As the demand for emotionally intelligent and user-centric AI systems continues to grow, further development and refinement of scales like PPRS could pave the way for standardized assessments of user experience in human-AI interaction. This suggests a direction for future research.
Understanding the unique needs of users with different attachment styles enables the creation of adaptive interfaces customized to individual preferences. Future research could explore the impact of culture on attachment styles and AI interactions. This could improve user experience in human-AI interaction.
Sample size, particularly the lack of participants with anxious attachment styles, limit statistical power and may contribute to the absence of statistical significance in some analyses. Future research should aim for a balanced representation of attachment styles to improve reliability and validity of findings. This study includes participants who speak different languages, impacting text analysis outcomes.
Addressing these limitations strengthens the methodology for a comprehensive understanding. Future research should prioritize user popularity, explore influencing factors, and consider evolving human-AI relationships.

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표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

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