arXiv subjects

Jennifer G. Kim

Publications and source records attributed to Jennifer G. Kim.

5 recordsLinked to original sources

arXiv2026

From Daily Song to Daily Self: Supporting Reflective Songwriting of Deaf and Hard-of-Hearing Individuals through Generative Music AI

The rapid advancement of generative AI (GenAI) is expanding access to songwriting, offering a new medium of self-expression for Deaf and Hard-of-Hearing (DHH) individuals. However, emerging technologies that support DHH individuals in expressing themselves through music have largely been evaluated in single-session settings and often fall short in helping users unfamiliar with songwriting convey personal narratives or sustain engagement over time. This paper explores songwriting as an extended, music-based journaling practice that supports sustained emotional reflection over multiple sessions. We introduce SoulNote, a GenAI system enabling DHH to engage in iterative songwriting. Grounded in user-centered design, including a design workshop, a preliminary study, and a multi-session diary study, our findings show that ongoing songwriting with \textit{SoulNote} facilitated emotional growth across three dimensions: self-insight, emotion regulation, and \revised{everyday attitudes toward emotions and self-care}. Overall, this work demonstrates how GenAI can support marginalized communities by transforming creative expression into a daily practice of self-discovery and reflection.

cs.HC↗

arXiv2026

Designing a Generative AI-Assisted Music Psychotherapy Tool for Deaf and Hard-of-Hearing Individuals

Songwriting has long served as a powerful medium for expressing unconscious emotions and fostering self-awareness in psychotherapy. Due to the auditory-centric nature of traditional approaches, Deaf and Hard-of-Hearing (DHH) individuals have often been excluded from music's therapeutic benefits. In response, this study presents a music psychotherapy tool co-designed with therapists, integrating conversational agents (CAs) and music generative AI as symbolic and therapeutic media. Through a usage study with 23 DHH individuals, we found that collaborative song writing with the CA enabled them to experience emotional release, reinterpretation, and deeper self-understanding. In particular, the CA's strategies -- supportive empathy, example response options, and visual-based metaphors -- were found to facilitate musical dialogue effectively for DHH individuals. These findings contribute to inclusive AI design by showing the potential of human-AI collaboration to bridge therapeutic artistic practices.

cs.HC↗

arXiv2025

Collective Voice: Recovered-Peer Support Mediated by An LLM-Based Chatbot for Eating Disorder Recovery

Peer recovery narratives provide unique benefits beyond professional or lay mentoring by fostering hope and sustained recovery in eating disorder (ED) contexts. Yet, such support is limited by the scarcity of peer-involved programs and potential drawbacks on recovered peers, including relapse risk. To address this, we designed RecoveryTeller, a chatbot adopting a recovered-peer persona that portrays itself as someone recovered from an ED. We examined whether such a persona can reproduce the support affordances of peer recovery narratives. We compared RecoveryTeller with a lay-mentor persona chatbot offering similar guidance but without a recovery background. We conducted a 20-day cross-over deployment study with 26 ED participants, each using both chatbots for 10 days. RecoveryTeller elicited stronger emotional resonance than a lay-mentor chatbot, yet tensions between emotional and epistemic trust led participants to view the two personas as complementary rather than substitutes. We provide design implications for mental health chatbot persona design.

cs.HC↗

arXiv2025

Impact Ambivalence: How People with Eating Disorders Get Trapped in the Perpetual Cycle of Digital Food Content Engagement

Digital food content could impact viewers' dietary health, with individuals with eating disorders being particularly sensitive to it. However, a comprehensive understanding of why and how these individuals interact with such content is lacking. To fill this void, we conducted exploratory (N=23) and in-depth studies (N=22) with individuals with eating disorders to understand their motivations and practices of consuming digital food content. We reveal that participants engaged with digital food content for both disorder-driven and recovery-supporting motivations, leading to conflicting outcomes. This impact ambivalence, the coexistence of recovery-supporting benefits and disorder-exacerbating risks, sustained a cycle of quitting, prompted by awareness of harm, and returning, motivated by anticipated benefits. We interpret these dynamics within dual systems theory and highlight how recognizing such ambivalence can inform the design of interventions that foster healthier digital food content engagement and mitigate post-engagement harmful effects.

cs.HC↗

arXiv2013

Personalized Academic Research Paper Recommendation System

A huge number of academic papers are coming out from a lot of conferences and journals these days. In these circumstances, most researchers rely on key-based search or browsing through proceedings of top conferences and journals to find their related work. To ease this difficulty, we propose a Personalized Academic Research Paper Recommendation System, which recommends related articles, for each researcher, that may be interesting to her/him. In this paper, we first introduce our web crawler to retrieve research papers from the web. Then, we define similarity between two research papers based on the text similarity between them. Finally, we propose our recommender system developed using collaborative filtering methods. Our evaluation results demonstrate that our system recommends good quality research papers.

cs.IR↗