Abstract Artificial Intelligence (AI) technologies have been progressing constantly and being more visible in different aspects of our lives. One recent phenomenon is ChatGPT, a chatbot with a conversational artificial intelligence interface that was developed by OpenAI. As one of the most advanced artificial intelligence applications, ChatGPT has drawn much public attention across the globe. In this regard, this study examines ChatGPT in education, among early adopters, through a qualitative instrumental case study. Conducted in three stages, the first stage of the study reveals that the public discourse in social media is generally positive and there is enthusiasm regarding its use in educational settings. However, there are also voices who are approaching cautiously using ChatGPT in educational settings. The second stage of the study examines the case of ChatGPT through lenses of educational transformation, response quality, usefulness, personality and emotion, and ethics. In the third and final stage of the study, the investigation of user experiences through ten educational scenarios revealed various issues, including cheating, honesty and truthfulness of ChatGPT, privacy misleading, and manipulation. The findings of this study provide several research directions that should be considered to ensure a safe and responsible adoption of chatbots, specifically ChatGPT, in education. 

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What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education

Abstract This article introduces the special issue of Human Communication Research (HCR) exploring the role of artificial intelligence (AI) in communication. It situates the six articles in this issue along conceptual dimensions drawn from human–computer interaction (HCI) and computer-mediated communication (CMC) research. It also underscores the importance of typologizing AI’s involvement in human communication by building on traditional distinctions such as mass versus interpersonal communication. In doing so, it describes how AI can fulfill analogous roles as either communicator or mediator in both contexts. This article also discusses the six articles and draws out common themes, most notably the tension between human agency and machine agency caused by the arrival of AI. It concludes by expressing optimism for the discovery of new concepts, frameworks and issues as we study both the positive and negative influences of AI on the process and effects of human communication. 

Rethinking Communication in the Era of Artificial Intelligence

The inclusion of chatbots is potentially disruptive in society, introducing opportunities, but also important implications that need to be addressed on different domains. The aim of this study is to examine chatbots in-depth, by mapping out their technological evolution, current usage, and potential applications, opportunities, and emerging problems within the health domain. The study examined three points of view. The first point of view traces the technological evolution of chatbots. The second point of view reports the fields of application of the chatbots, giving space to the expectations of use and the expected benefits from a cross-domain point of view, also affecting the health domain. The third and main point of view is that of the analysis of the state of use of chatbots in the health domain based on the scientific literature represented by systematic reviews. The overview identified the topics of greatest interest with the opportunities. The analysis revealed the need for initiatives that simultaneously evaluate multiple domains all together in a synergistic way. Concerted efforts to achieve this are recommended. It is also believed to monitor both the process of osmosis between other sectors and the health domain, as well as the chatbots that can create psychological and behavioural problems with an impact on the health domain.

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The Chatbots Are Invading Us: A Map Point on the Evolution, Applications, Opportunities, and Emerging Problems in the Health Domain

As commercial voice-based personal agents (VPAs) like Google Assistant increasingly penetrate people’s private social habitats, sometimes involving more than one user, these social situations are gaining importance for how people define the human-machine relationship (HMR). The paper contributes to the understanding of the situation’s impact on HMR on a theoretical and methodological level. First, Georg Simmel’s theory on the “Quantitative determination of the group” is applied to the HMR. A 2x1 between-subjects quasi-experiment (N = 100) contrasted the defined HMR in dyadic social situations (one human interacting with the Google Assistant) to the defined HMR in triadic social situations (two humans interacting with the Google Assistant). Second, the method of central tendency analysis was extended by the more robust and informative comparison of distributions and quantiles using the two-sample Kolmogorov–Smirnov test and the shift function. The results show that the triadic situation, compared to the dyadic one, led to a more confounded categorization of the VPA’s subjecthood in terms of self-similarity, while simultaneously strengthening a definition of the relationship that resembled those of a business relation through lowered intimacy and feedback, mainly grounded in a more realistic definition of the agent’s inability to understand affects. In contrast to Simmel’s inter-human theory the relationship’s dimension of reciprocity and commitment remained unaffected by the situation. The paper discusses how these effects and non-effects of the triad could be explained by drawing on Simmel as well as peculiarities of HMR and methodology. Finally, it offers preliminary hypotheses about the situation’s implications for the HMR and outlines avenues for future research. • If the interaction is good, the first human perceives a higher self-similarity with the artificial agent when the second human is present. • When a second human is present, the first human estimates the artificial agent’s affect understanding at a more realistic level than in one-on-one situations with the agent. • The definition of reciprocity and interdependence were not affected by the quantity of the situation’s constellation. • If the artificial agent performs poorly, the presence of the second human decreases the perception of mutual attention and message understanding between the first human and the agent. • If the first human has already established a relationship with the artificial agent, the second human may destabilize this relationship by increasing the feeling of distance between the first human and the agent. • Methodological contribution: the analysis of distributions and quantiles allows for more robust and meaningful results than the analysis of central tendencies. 

The third party will make a difference — A study on the impact of dyadic and triadic social situations on the relationship with a voice-based personal agent

In 2021, the San Francisco Chronicle released a feature article about a man who chose to resurrect his deceased fiancée by training a chatbot system built on OpenAI’s GPT language models on her old digital messages. He then had emotional conversations with this chatbot, which appeared to accurately mimic the deceased’s writing style. This case study raises questions about the communicative influences of thanabots: chatbots trained on data of the dead. While thanabots are clearly not living conversational partners, the rhetoric, everyday experiences, and emotions associated with these system have very real implications for living users. This paper applies a lifeworld perspective to consider the hermeneutics of thanabots. It shows that thanabots exist in a long lineage of efforts to communicate with the dead, but acknowledges that thanatechnologies must be more thoroughly studied for better understanding of what it means to die in a digital age.

https://doi.org/10.1177/01634437221147626

Chatting with the dead: The hermeneutics of thanabots


 Use of conversational artificial intelligence (AI), such as humanlike social chatbots, is increasing. While a growing number of people is expected to engage in intimate relationships with social chatbots, theories and knowledge of human–AI friendship remain limited. As friendships with AI may alter our understanding of friendship itself, this study aims to explore the meaning of human–AI friendship through a developed conceptual framework. We conducted 19 in-depth interviews with people who have a human–AI friendship with the social chatbot Replika to uncover how they understand and perceive this friendship and how it compares to human friendship. Our results indicate that while human–AI friendship may be understood in similar ways to human–human friendship, the artificial nature of the chatbot also alters the notion of friendship in multiple ways, such as allowing for a more personalized friendship tailored to the user’s needs.

My AI Friend: How Users of a Social Chatbot Understand Their Human–AI Friendship

Social chatbots have become more advanced, paving the way for human–chatbot relationships (HCRs). Although this phenomenon has already received some research attention, the results have been contradictory, and there is uncertainty regarding how to understand HCR formation. To provide the needed knowledge on this phenomenon, we conducted a qualitative longitudinal study. We interviewed 25 participants over a 12-week period to understand how their HCRs formed with the popular chatbot Replika. We found that the HCRs formed gradually and mostly in line with the assumptions of Social Penetration Theory. Our findings indicate the need to acknowledge substantial variation and nuance in the HCR formation process, plus variation in the onset of self-disclosure and in the subsequent relationship formation. The results show that important drivers pushing the relationship toward attachment and perceived closeness appear to be Replika's ability to participate in a variety of interactions, as well as to support more deep-felt human needs related to social contact and self-reflection. In contrast, unpredictable events and technical difficulties could hinder relationship formation and lead to termination. Finally, we discuss the appropriateness of using a theoretical framework developed for human–human relationships when investigating HCRs, and we suggest directions for future research. 

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A longitudinal study of human-chatbot relationships

Why People Use Chatgpt

Background Depression is common during adolescence. Early intervention can prevent it from developing into more progressive mental disorders. Combining information technology and clinical psychoeducation is a promising way to intervene at an earlier stage. However, data-driven research on the cognitive response to health information targeting adolescents with symptoms of depression is lacking. Objective This study aimed to fill this knowledge gap through a new understanding of adolescents’ cognitive response to health information about depression. This knowledge can help to develop population-specific information technology, such as chatbots, in addition to clinical therapeutic tools for use in general practice. Methods The data set consists of 1870 depression-related questions posted by adolescents on a public web-based information service. Most of the posts contain descriptions of events that lead to depression. On a sample of 100 posts, we conducted a qualitative thematic analysis based on cognitive behavioral theory investigating behavioral, emotional, and symptom responses to beliefs associated with depression. Results Results were organized into four themes. (1) Hopelessness, appearing as a set of negative beliefs about the future, possibly results from erroneous beliefs about the causal link between risk factors and the course of depression. We found beliefs about establishing a sturdy therapy alliance as a responsibility resting on the patient. (2) Therapy hesitancy seemed to be associated with negative beliefs about therapy prognosis and doubts about confidentiality. (3) Social shame appeared as a consequence of impaired daily function when the cause is not acknowledged. (4) Failing to attain social interaction appeared to be associated with a negative symptom response. In contrast, actively obtaining social support reduces symptoms and suicidal thoughts. Conclusions These results could be used to meet the clinical aims stated by earlier psychoeducation development, such as instilling hope through direct reattribution of beliefs about the future; challenging causal attributions, thereby lowering therapy hesitancy; reducing shame through the mechanisms of externalization by providing a tentative diagnosis despite the risk of stigmatizing; and providing initial symptom relief by giving advice on how to open up and reveal themselves to friends and family and balance the message of self-management to fit coping capabilities. An active counseling style advises the patient to approach the social environment, demonstrating an attitude toward self-action.

Analyzing User-Generated Web-Based Posts of Adolescents’ Emotional, Behavioral, and Symptom Responses to Beliefs About Depression: Qualitative Thematic Analysis

Marita Skjuve

Papers

My AI Friend: How Users of a Social Chatbot Understand Their Human–AI Friendship

A longitudinal study of human-chatbot relationships

Why People Use Chatgpt

Analyzing User-Generated Web-Based Posts of Adolescents’ Emotional, Behavioral, and Symptom Responses to Beliefs About Depression: Qualitative Thematic Analysis

The Depressed Teenage brain: Chatbot Psychoeducation