arXiv subjects

Dongwook Yoon

Publications and source records attributed to Dongwook Yoon.

14 recordsLinked to original sources

arXiv2026

Agentic Web Accessibility Auditing: A Criterion-Specific Framework for Translating WCAG Requirements into Assessments

Web accessibility auditing requires interpreting diverse requirements and examining interface behavior. Rule-based checks and noninteractive model assessments can miss barriers requiring contextual or interactive evidence. We present an agentic framework that assigns a vision-language agent to each accessibility requirement. Guided by tailored instructions, agents inspect webpages, operate controls, and record evidence supporting their findings. We implement the framework for 40 requirements from the Web Content Accessibility Guidelines (WCAG). To compare detection and cost, we construct a dataset of 250 page-criterion records derived from expert audits across 11 scholarly platforms. Agents recover 67 of 78 reported positive cases (86% recall), compared with 36% for axe-core, a rule-based checker, and 67% for an uncued, noninteractive vision-language model, at lower precision (56%). They recover nine of ten Keyboard and No Keyboard Trap cases missed by both baselines. Together, the framework, implementations, and dataset support automated accessibility auditing grounded in inspectable evidence.

cs.HC↗

arXiv2026

Amplification to Synthesis: A Comparative Analysis of Cognitive Operations Before and After Generative AI

Cognitive operations are a rising concern in the geopolitical sphere, a quiet yet rigorous fight for public perception and decision making. While such operations have been extensively studied in the context of bot-driven amplification, the emergence of generative AI introduces a new set of capabilities that may have fundamentally altered how these operations are designed and executed. The possible evolution of cognitive operation via generative AI puts nation states vulnerable without proper mitigation strategies. To address this, we compared behavioral and linguistic coordination patterns in X (formerly Twitter) datasets from the 2016 and 2024 U.S. presidential elections. Utilizing a combined corpus of over 133,000 posts, we applied post-type distribution, semantic clustering, temporal synchrony analysis, and Jaccard-based lexical overlap measures. Findings suggest that the 2024 corpus exhibits a distinct pattern from 2016. Original content rose from 59% to 93% with retweets virtually disappeared; lexical overlap collapsed from a mean Jaccard score of 0.99 to 0.27, with posts converging on the same subject matter expressed in markedly different words; and temporal coordination shifted from pervasive cross-semantic synchrony to narratively concentrated co-occurrence. Taken together, these patterns point toward an operational logic organized around active content generation and narrative-specific targeting - characteristics consistent with generative AI involvement. These findings offer an empirical baseline for future research investigating generative AI's role in the cognitive operation pipeline, and as a practical reference point for security practitioners developing detection frameworks calibrated to the post-generative AI threat environment.

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arXiv2026

Cloning the Self for Mental Well-Being: A Framework for Designing Safe and Therapeutic Self-Clone Chatbots

As digital tools increasingly mediate mental health care, self-clone chatbots can offer a uniquely novel approach to intra-personal exploration and self-derived support. Trained to replicate users' conversational patterns, self-clones allow users to talk to themselves through their digital replicas. Despite the promises, these systems may carry risks around identity confusion, negative reinforcement, and blurred user agency. Through interviews with 16 mental health professionals and 6 general users, we aim to uncover tensions and design opportunities in this emerging space to guide responsible self-clone design. Our analysis produces a design framework organized around three priorities: (1) defining goals and grounding the approach in existing therapeutic models, (2) design dimensions including the self-clone persona and user-clone relationship dynamics, and (3) considerations for minimizing potential emotional and ethical harms. This framework contributes an interdisciplinary foundation for designing self-clone chatbots as AI-mediated self-interaction tools that are emotionally and ethically attuned in mental health contexts.

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arXiv2026

Exploring Learners' Expectations and Engagement When Collaborating with Constructively Controversial Peer Agents

Peer agents can supplement real-time collaborative learning in asynchronous online courses. Constructive Controversy (CC) theory suggests that humans deepen their understanding of a topic by confronting and resolving controversies. This study explores whether CC's benefits apply to LLM-based peer agents, focusing on the impact of agents' disputatious behaviors and disclosure of agents' behavior designs on the learning process. In our mixed-method study (n=144), we compare LLMs that follow detailed CC guidelines (regulated) to those guided by broader goals (unregulated) and examine the effects of disclosing the agents' design to users (transparent vs. opaque). Findings show that learners' values influence their agent interaction: those valuing control appreciate unregulated agents' willingness to cease push-back upon request, while those valuing intellectual challenges favor regulated agents for stimulating creativity. Additionally, design transparency lowers learners' perception of agents' abilities. Our findings lay the foundation for designing effective collaborative peer agents in isolated educational settings.

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arXiv2026

The AI Genie Phenomenon and Three Types of AI Chatbot Addiction: Escapist Roleplays, Pseudosocial Companions, and Epistemic Rabbit Holes

Recent reports on generative AI chatbot use raise concerns about its addictive potential. An in-depth understanding is imperative to minimize risks, yet AI chatbot addiction remains poorly understood. This study examines how to characterize AI chatbot addiction--why users become addicted, the symptoms commonly reported, and the distinct types it comprises. We conducted a thematic analysis of Reddit entries (n=334) across 14 subreddits where users narrated their experiences with addictive AI chatbot use, followed by an exploratory data analysis. We found: (1) users' dependence tied to the "AI Genie" phenomenon--users can get exactly anything they want with minimal effort--and marked by symptoms that align with addiction literature, (2) three distinct addiction types: Escapist Roleplay, Pseudosocial Companion, and Epistemic Rabbit Hole, (3) sexual content involved in multiple cases, and (4) recovery strategies' perceived helpfulness differ between addiction types. Our work lays empirical groundwork to inform future strategies for prevention, diagnosis, and intervention.

cs.HC↗

arXiv2026

AI Twin: Enhancing ESL Speaking Practice through AI Self-Clones of a Better Me

Advances in AI have enabled ESL learners to practice speaking through conversational systems. However, most tools rely on explicit correction, which can interrupt the conversation and undermine confidence. Grounded in second language acquisition and motivational psychology, we present AI Twin, a system that rephrases learner utterances into more fluent English and delivers them in the learner's voice. Embodying a more confident and proficient version of the learner, AI Twin reinforces motivation through alignment with their aspirational Ideal L2 Self. Also, its use of implicit feedback through rephrasing preserves conversational flow and fosters an emotionally supportive environment. In a within-subject study with 20 adult ESL learners, we compared AI Twin with explicit correction and a non-personalized rephrasing agent. Results show that AI Twin elicited higher emotional engagement, with participants describing the experience as more motivating. These findings highlight the potential of self-representative AI for personalized, psychologically grounded support in ESL learning.

cs.HC↗

arXiv2026

PASTA: A Scalable Framework for Multi-Policy AI Compliance Evaluation

AI compliance is becoming increasingly critical as AI systems grow more powerful and pervasive. Yet the rapid expansion of AI policies creates substantial burdens for resource-constrained practitioners lacking policy expertise. Existing approaches typically address one policy at a time, making multi-policy compliance costly. We present PASTA, a scalable compliance tool integrating four innovations: (1) a comprehensive model-card format supporting descriptive inputs across development stages; (2) a policy normalization scheme; (3) an efficient LLM-powered pairwise evaluation engine with cost-saving strategies; and (4) an interface delivering interpretable evaluations via compliance heatmaps and actionable recommendations. Expert evaluation shows PASTA's judgments closely align with human experts ($\rho \geq .626$). The system evaluates five major policies in under two minutes at approximately \$3. A user study (N = 12) confirms practitioners found outputs easy-to-understand and actionable, introducing a novel framework for scalable automated AI governance.

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arXiv2025

AnchoredAI: Contextual Anchoring of AI Comments Improves Writer Agency and Ownership

Generative AI is increasingly integrated into writing support, yet current chat-based interfaces often obscure referential context and risk amplifying automation bias and overreliance. We introduce AnchoredAI, a novel system that anchors AI feedback directly to relevant text spans. AnchoredAI implements two key mechanisms: (1) an Anchoring Context Window (ACW) that maintains unique, context-rich references, and (2) an update-aware context retrieval method that preserves the intent of prior comments after document edits. In a controlled user study, we compared AnchoredAI to a chat-based LLM interface. Results show that AnchoredAI led to more targeted revisions while fostering a stronger agency metrics (e.g., control and ownership) among writers. These findings highlight how interface design shapes AI-assisted writing, suggesting that anchoring can mitigate overreliance and enable more precise, user-driven revision practices.

cs.HC↗

arXiv2025

Social Media Clones: Exploring the Impact of Social Delegation with AI Clones through a Design Workbook Study

Social media clones are AI-powered social delegates of ourselves created using our personal data. As our identities and online personas intertwine, these technologies have the potential to greatly enhance our social media experience. If mismanaged, however, these clones may also pose new risks to our social reputation and online relationships. To set the foundation for a productive and responsible integration, we set out to understand how social media clones will impact our online behavior and interactions. We conducted a series of semi-structured interviews introducing eight speculative clone concepts to 32 social media users through a design workbook. Applying existing work in AI-mediated communication in the context of social media, we found that although clones can offer convenience and comfort, they can also threaten the user's authenticity and increase skepticism within the online community. As a result, users tend to behave more like their clones to mitigate discrepancies and interaction breakdowns. These findings are discussed through the lens of past literature in identity and impression management to highlight challenges in the adoption of social media clones by the general public, and propose design considerations for their successful integration into social media platforms.

cs.HC↗

arXiv2025

Talking to an AI Mirror: Designing Self-Clone Chatbots for Enhanced Engagement in Digital Mental Health Support

Mental health conversational agents have the potential to deliver valuable therapeutic impact, but low user engagement remains a critical barrier hindering their efficacy. Existing therapeutic approaches have leveraged clients' internal dialogues (e.g., journaling, talking to an empty chair) to enhance engagement through accountable, self-sourced support. Inspired by these, we designed novel AI-driven self-clone chatbots that replicate users' support strategies and conversational patterns to improve therapeutic engagement through externalized meaningful self-conversation. Validated through a semi-controlled experiment (N=180), significantly higher emotional and cognitive engagement was demonstrated with self-clone chatbots than a chatbot with a generic counselor persona. Our findings highlight self-clone believability as a mediator and emphasize the balance required in maintaining convincing self-representation while creating positive interactions. This study contributes to AI-based mental health interventions by introducing and evaluating self-clones as a promising approach to increasing user engagement, while exploring implications for their application in mental health care.

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arXiv2025

WikiGap: Promoting Epistemic Equity by Surfacing Knowledge Gaps Between English Wikipedia and other Language Editions

With more than 11 times as many pageviews as the next largest edition, English Wikipedia dominates global knowledge access relative to other language editions. Readers are prone to assuming English Wikipedia as a superset of all language editions, leading many to prefer it even when their primary language is not English. Other language editions, however, comprise complementary facts rooted in their respective cultures and media environments, which are marginalized in English Wikipedia. While Wikipedia's user interface enables switching between language editions through its Interlanguage Link (ILL) system, it does not reveal to readers that other language editions contain valuable, complementary information. We present WikiGap, a system that surfaces complementary facts sourced from other Wikipedias within the English Wikipedia interface. Specifically, by combining a recent multilingual information-gap discovery method with a user-centered design, WikiGap enables access to complementary information from French, Russian, and Chinese Wikipedia. In a mixed-methods study (n=21), WikiGap significantly improved fact-finding accuracy, reduced task time, and received a 32-point higher usability score relative to Wikipedia's current ILL-based navigation system. Participants reported increased awareness of the availability of complementary information in non-English editions and reconsidered the completeness of English Wikipedia. WikiGap thus paves the way for improved epistemic equity across language editions.

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arXiv2025

Who Reaps All the Superchats? A Large-Scale Analysis of Income Inequality in Virtual YouTuber Livestreaming

The explosive growth of Virtual YouTubers (VTubers)-streamers who perform behind virtual anime avatars-has created a unique digital economy with profound implications for content creators, platforms, and viewers. Understanding the economic landscape of VTubers is crucial for designing equitable platforms, supporting content creator livelihoods, and fostering sustainable digital communities. To this end, we conducted a large-scale study of over 1 million hours of publicly available streaming records from 1,923 VTubers on YouTube, covering tens of millions of dollars in actual profits. Our analysis reveals stark inequality within the VTuber community and characterizes the sources of income for VTubers from multiple perspectives. Furthermore, we also found that the VTuber community is increasingly monopolized by two agencies, driving the financial disparity. This research illuminates the financial dynamics of VTuber communities, informing the design of equitable platforms and sustainable support systems for digital content creators.

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arXiv2022

Interaction Design for VR Applications: Understanding Needs for University Curricula

As virtual reality (VR) is emerging in the tech sector, developers and designers are under pressure to create immersive experiences for their products. However, the current curricula from top institutions focus primarily on technical considerations for building VR applications, missing out on concerns and usability problems specific to VR interaction design. To better understand current needs, we examined the status quo of existing university pedagogies by carrying out a content analysis of undergraduate and graduate courses about VR and related areas offered in the major citadels of learning and conducting interviews with 7 industry experts. Our analysis reveals that the current teaching practices underemphasize design thinking, prototyping, and evaluation skills, while focusing on technical implementation. We recommend VR curricula should emphasize design principles and guidelines, offer training in prototyping and ideation, prioritize practical design exercises while providing industry insights, and encourage students to solve VR design problems beyond the classroom.

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arXiv2021

StripBrush: A Constraint-Relaxed 3D Brush Reduces Physical Effort and Enhances the Quality of Spatial Drawing

Spatial drawing using ruled-surface brush strokes is a popular mode of content creation in immersive VR, yet little is known about the usability of existing spatial drawing interfaces or potential improvements. We address these questions in a three-phase study. (1) Our exploratory need-finding study (N=8) indicates that popular spatial brushes require users to perform large wrist motions, causing physical strain. We speculate that this is partly due to constraining users to align their 3D controllers with their intended stroke normal orientation. (2) We designed and implemented a new brush interface that significantly reduces the physical effort and wrist motion involved in VR drawing, with the additional benefit of increasing drawing accuracy. We achieve this by relaxing the normal alignment constraints, allowing users to control stroke rulings, and estimating normals from them instead. (3) Our comparative evaluation of StripBrush (N=17) against the traditional brush shows that StripBrush requires significantly less physical effort and allows users to more accurately depict their intended shapes while offering competitive ease-of-use and speed.

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