_edited.jpg)
Contact
Eric Junhan Kim
PhD Candidate
(Expected Graduation: May 2024)
School of Information
University of Michigan
I’m Eric Kim, a Mixed-method researcher passionate about designing health products and services using AI
I'm passionate about digging into people's behaviors, ideas, and minds to derive meaningful insights that I can apply to technology design. I use my expertise in HCI and UX, user-centered design methodologies, and quantitative data analytics to design and develop technology for underrepresented groups. I am especially interested in using artificial intelligence technology, mainly conversational agents, to support the health and well-being of marginalized groups.
Publications
Peer-reviewed Conference Proceedings
Designing Chatbots with Black Americans with Chronic Conditions: Overcoming Challenges against COVID-19
Junhan Kim, Jana Muhic, Lionel P. Robert, Sun Young Park. CHI 2022. [pdf]
Chatbots have been deployed in healthcare in various ways such as providing educational information, and monitoring and triaging symptoms. However, they can be ineffective when they are designed without a careful consideration of the cultural context of the users, especially for marginalized groups. Through interviews and design studies, we attempt to understand how chatbots can be better designed for Black American communities within the context of COVID-19.
Can a Machine Tend to Teenagers' Emotional Needs? A Study with Conversational Agents
Junhan Kim, Yoojung Kim, Byungjoon Kim, Sookyung Yun, Minjoon Kim, Joongseek Lee.
CHI 2018 Late-Breaking Work. [pdf]
As teen stress and its negative consequences are on the rise, several studies have attempted to tend to their emotional needs through conversational agents (CAs). However, these attempts have focused on increasing human-like traits of agents, thereby overlooking the possible advantage of machine inherits, such as lack of emotion or the ability to perform calculations. We shed light on the machine inherits of CAs to help satisfy the emotional needs of teenagers through a workshop with 20 teenagers, followed by in-depth interviews.
Event-Contingent Experience Sampling Methodology Using Automated Chatbots : In Relation to a Study on TV Watching Behavior
Junhan Kim, Minjoon Kim, Jinyoung Kim, Joongseek Lee. HCIK 2017.
Although various methods for event sampling methodology (ESM) have been developed through technology, conducting ESM through chatbots has been rarely explored. We demonstrated how to conduct ESM through chatbots and its results through a study on households' TV watching behavior.
Journal Papers
Bridging the health disparity of African Americans through conversational agents
Junhan Kim, Sun Young Park, Lionel P. Robert. DGOV 2021. [pdf]
African Americans have faced health disparities in terms of access to health care and treatment of illnesses. The novel coronavirus disease 2019 pandemic exacerbates those disparities and disproportionately affect the African American population in terms of infection and mortality. Through a review on past literature, we explore how conversational agents (CAs) are a technological intervention with the potential to narrow the disparities.
Workshop Papers
Trustworthy Conversational Agent Design for African Americans with Chronic Conditions during COVID-19
Junhan Kim, Jana Muhic, Lionel P. Robert, Sun Young Park. CHI 2021. [pdf]
This paper discusses preliminary findings on how to design chatbots that can increase African Americans’ trust in health information, particularly those who have experienced chronic conditions during the COVID-19 pandemic.
Leveraging challenges of an algorithm-based symptom checker on user trust through explainable AI
Youjin Hwang, Taewan Kim, Junhan Kim, Joonhwan Lee, Hwajung Hong. CHI 2021. [pdf]
An algorithm-based symptom checker is a service that predicts and informs the expected disease name based on the symptoms entered by users and informs the user of actions to be taken afterward. Few studies have been done on the perception and algorithm experience with the symptom checker. We share our results from an empirical study defining challenges that prevent user trust toward algorithm-based symptom checkers.
A Review on Acceptance of Conversational Agents in Health
Junhan Kim, Sun Young Park, Lionel P. Robert. CHI 2020. [pdf]
We present a preliminary analysis of papers from the past review studies on conversational agents (CAs) in healthcare in terms of acceptance. We show a trend of user acceptance in CAs in healthcare by synthesizing the outcomes of different works on five main factors (i.e. satisfaction, ease of use, usefulness, the number of interactions, and the duration of interaction) of technological acceptance on CAs.
Conversational agents for health and wellbeing: Review and future agendas
Junhan Kim, Sun Young Park, Lionel P. Robert. CHI 2019. [pdf]
We present a literature review of 57 papers that have examined the role and impact of conversational agents (CAs) in the health domain. We note that three key themes repeatedly arose during the review: therapeutic alliance, trust, and human intervention. We also point out several areas that have been largely overlooked, such as specific patient characteristics that influence the effects of CA usage, the results of differing CA designs, and specific human-CA relationships.