arXiv subjects

Yi-Chieh Lee

Publications and source records attributed to Yi-Chieh Lee.

41 records · Page 3Linked to original sources

arXiv2025

From Interaction to Attitude: Exploring the Impact of Human-AI Cooperation on Mental Illness Stigma

AI conversational agents have demonstrated efficacy in social contact interventions for stigma reduction at a low cost. However, the underlying mechanisms of how interaction designs contribute to these effects remain unclear. This study investigates how participating in three human-chatbot interactions affects attitudes toward mental illness. We developed three chatbots capable of engaging in either one-way information dissemination from chatbot to a human or two-way cooperation where the chatbot and a human exchange thoughts and work together on a cooperation task. We then conducted a two-week mixed-methods study to investigate variations over time and across different group memberships. The results indicate that human-AI cooperation can effectively reduce stigma toward individuals with mental illness by fostering relationships between humans and AI through social contact. Additionally, compared to a one-way chatbot, interacting with a cooperative chatbot led participants to perceive it as more competent and likable, promoting greater empathy during the conversation. However, despite the success in reducing stigma, inconsistencies between the chatbot's role and the mental health context raised concerns. We discuss the implications of our findings for human-chatbot interaction designs aimed at changing human attitudes.

cs.HC↗

arXiv2024

Wild Narratives: Exploring the Effects of Animal Chatbots on Empathy and Positive Attitudes toward Animals

Rises in the number of animal abuse cases are reported around the world. While chatbots have been effective in influencing their users' perceptions and behaviors, little if any research has hitherto explored the design of chatbots that embody animal identities for the purpose of eliciting empathy toward animals. We therefore conducted a mixed-methods experiment to investigate how specific design cues in such chatbots can shape their users' perceptions of both the chatbots' identities and the type of animal they represent. Our findings indicate that such chatbots can significantly increase empathy, improve attitudes, and promote prosocial behavioral intentions toward animals, particularly when they incorporate emotional verbal expressions and authentic details of such animals' lives. These results expand our understanding of chatbots with non-human identities and highlight their potential for use in conservation initiatives, suggesting a promising avenue whereby technology could foster a more informed and empathetic society.

cs.HC↗

arXiv2024

Mitigating Ageism through Virtual Reality: Intergenerational Collaborative Escape Room Design

As virtual reality (VR) becomes more popular for intergenerational collaboration, there is still a significant gap in research regarding understanding the potential for reducing ageism. Our study aims to address this gap by analyzing ageism levels before and after VR escape room collaborative experiences. We recruited 28 participants to collaborate with an older player in a challenging VR escape room game. To ensure consistent and reliable performance data of older players, our experimenters simulated older participants following specific guidelines. After completing the game, we found a significant reduction in ageism among younger participants. Furthermore, we introduce a new game mechanism that encourages intergenerational collaboration. Our research highlights the potential of VR collaborative games as a practical tool for mitigating ageism. It provides valuable insights for designing immersive VR experiences that foster enhanced intergenerational collaboration.

cs.HC↗

arXiv2024

Understanding Public Perceptions of AI Conversational Agents: A Cross-Cultural Analysis

Conversational Agents (CAs) have increasingly been integrated into everyday life, sparking significant discussions on social media. While previous research has examined public perceptions of AI in general, there is a notable lack in research focused on CAs, with fewer investigations into cultural variations in CA perceptions. To address this gap, this study used computational methods to analyze about one million social media discussions surrounding CAs and compared people's discourses and perceptions of CAs in the US and China. We find Chinese participants tended to view CAs hedonically, perceived voice-based and physically embodied CAs as warmer and more competent, and generally expressed positive emotions. In contrast, US participants saw CAs more functionally, with an ambivalent attitude. Warm perception was a key driver of positive emotions toward CAs in both countries. We discussed practical implications for designing contextually sensitive and user-centric CAs to resonate with various users' preferences and needs.

cs.HC↗

arXiv2023

Comparing How a Chatbot References User Utterances from Previous Chatting Sessions: An Investigation of Users' Privacy Concerns and Perceptions

Chatbots are capable of remembering and referencing previous conversations, but does this enhance user engagement or infringe on privacy? To explore this trade-off, we investigated the format of how a chatbot references previous conversations with a user and its effects on a user's perceptions and privacy concerns. In a three-week longitudinal between-subjects study, 169 participants talked about their dental flossing habits to a chatbot that either, (1-None): did not explicitly reference previous user utterances, (2-Verbatim): referenced previous utterances verbatim, or (3-Paraphrase): used paraphrases to reference previous utterances. Participants perceived Verbatim and Paraphrase chatbots as more intelligent and engaging. However, the Verbatim chatbot also raised privacy concerns with participants. To gain insights as to why people prefer certain conditions or had privacy concerns, we conducted semi-structured interviews with 15 participants. We discuss implications from our findings that can help designers choose an appropriate format to reference previous user utterances and inform in the design of longitudinal dialogue scripting.

cs.HC↗