arXiv subjects

Pengcheng An

Publications and source records attributed to Pengcheng An.

At least 19 recordsLinked to original sources

arXiv2026

Mirroring the Past: Exploring How Ancestral Digital Self Influences History Learning

Learners often perceive history as distant from themselves, which limits immersion and empathy in history learning. To bridge this gap, we introduce the "Ancestral Digital Self," an AI-generated pedagogical agent presented in prerecorded videos that mirrors the learner's facial features and vocal timbre, representing a historically situated version of the self. We developed a reproducible workflow for creating AI-generated historical learning videos and conducted a within-subjects study (N=36) comparing a Digital Self agent with a non-self pedagogical agent. The Digital Self agent enhanced experiential measures, including narrative transportation, perceived relatedness, self-other inclusion, and agent perception. However, it did not improve immediate learning outcomes: quiz scores were lower in the Digital Self condition, and Remember/Know judgments showed no reliable differences. Interviews further suggested that self-similarity increased familiarity and motivation, while novelty and uncanniness could draw attention away from historical content. These findings offer design implications for future educational environments supported by pedagogical agents.

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arXiv2026

TaleBot: A Tangible AI Companion to Support Children in Co-creative Storytelling for Resilience Cultivation

Resilience is a key factor affecting children's mental wellbeing and future development. Yet, limited HCI research has explored how to help children build resilience through adversarial experiences. Informed by a formative study with elementary school teachers and professional psychologists, we design TaleBot, an AI-empowered system that supports children to co-create stories about overcoming everyday adversities tailored to their personal situations. We evaluated the system with 12 elementary children in school counseling rooms under teacher guidance and conducted reflective interviews with parents upon the Child-AI co-created stories. The findings show that TaleBot encourages children in self-expression of feelings and thoughts, creating opportunities for teachers to provide personalized support and for parents to better understand the profound impact of family communication on children's mental wellbeing. We conclude with design implications for using generative AI to support children's mental health education and interventions across school and family contexts.

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arXiv2026

FuturePrism: Supporting Adolescence in Collaborative Storytelling to Cope with Future Uncertainty

FuturePrism is a GenAI-empowered collaborative storytelling system designed to scaffold adolescents to navigate future life challenges. Adolescents often suffer from anxiety related to future uncertainty for lacking the executive function to develop concrete pathways. Operationalizing Snyder's Hope Theory, the system utilizes a triadic role-play mechanics to externalize cognitive processes through four narrative chapters: The Goal, The Opportunity, The Challenge, and The Agency. An evaluation workshop with 20 adolescents demonstrated that FuturePrism significantly enhances momentary hope levels, particularly in the Agency dimension. Participants reported high levels of narrative immersion and positive feedback towards system usability. Participants also confirmed that the AI-scaffolded collaborative storytelling empowered them to develop positive attitudes towards future challenges.

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arXiv2026

ASafePlace: User-Led Personalization of VR Relaxation via an Art Therapy Activity

To overcome the lack of deep personalization in standard biofeedback methods, we introduce ASafePlace, a system utilizing an AI-powered, art-therapy-inspired exercise called The Safe Place, to create a personalized VR biofeedback experience. In our system, users sketch a personal sanctuary from memory, which is then transformed into a customized 360 virtual environment with personalized audio guidance for relaxation training. A study with 52 participants showed this approach effectively reduced anxiety and increased user presence, while the integration of art-therapy-inspired activity and biofeedback produced strong improvements in physiological relaxation, measured by heart rate variability and respiration rate. Qualitative results showed how participants' sense of familiarity and presence was enhanced by the symbolic elements and natural sanctuaries created from their autobiographical memories. Our findings demonstrate that art-therapy-inspired activity is a powerful tool for creating highly effective and individualized relaxation experiences, naturally connecting the virtual environment to a user's core memories and emotions.

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arXiv2026

When Generative AI Is Intimate, Sexy, and Violent: Examining Not-Safe-For-Work (NSFW) Chatbots on FlowGPT

User-created chatbots powered by generative AI offer new ways to share and interact with Not-Safe-For-Work (NSFW) content. However, little is known about the characteristics of these GenAI-based chatbots and their user interactions. Drawing on the functional theory of NSFW on social media, this study analyzes 376 NSFW chatbots and 307 public conversation sessions on FlowGPT. Findings identify four chatbot types: roleplay characters, story generators, image generators, and do-anything-now bots. AI Characters portraying fantasy personas and enabling hangout-style interactions are most common, often using explicit avatar images to invite engagement. Sexual, violent, and insulting content appears in both user prompts and chatbot outputs, with some chatbots generating explicit material even when users do not create erotic prompts. In sum, the NSFW experience on FlowGPT can be understood as a combination of virtual intimacy, sexual delusion, violent thought expression, and unsafe content acquisition. We conclude with implications for chatbot design, creator support, user safety, and content moderation.

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arXiv2026

ProjecTA: A Semi-Humanoid Robotic Teaching Assistant with In-Situ Projection for Guided Tours

Robotic teaching assistants (TAs) often use body-mounted screens to deliver content. In nomadic, walk-and-talk learning, such as tours in makerspaces, these screens can distract learners from real-world objects, increasing extraneous cognitive load. HCI research lacks empirical comparisons of potential alternatives, such as robots with in-situ projection versus screen-based counterparts; little knowledge has been derived for designing such alternatives. We introduce ProjecTA, a semi-humanoid, gesture-capable TA that guides learners while projecting near-object overlays coordinated with speech and gestures. In a mixed-method study (N=24) in a university makerspace, ProjecTA significantly reduced extraneous load and outperformed its screen-based counterpart in perceived usability, usefulness of visual display, and cross-modal complementarity. Qualitative analyses revealed how ProjecTA's coordinated projections, gestures, and speech anchored explanations in place and time, enhancing understanding in ways a screen could not. We derive key design implications for future robotic TAs leveraging spatial projection to support mobile learning in physical environments.

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arXiv2025

"Can I Decorate My Teeth With Diamonds?": Exploring Multi-Stakeholder Perspectives on Using VR to Reduce Children's Dental Anxiety

Dental anxiety is prevalent among children, often leading to missed treatment and potential negative effects on their mental well-being. While several interventions (e.g., pharmacological and psychotherapeutic techniques) have been introduced for anxiety alleviation, the recently emerged virtual reality (VR) technology, with its immersive and playful nature, opened new opportunities for complementing and enhancing the therapeutic effects of existing interventions. In this light, we conducted a series of co-design workshops with 13 children aged 10-12 to explore how they envisioned using VR to address their fear and stress associated with dental visits, followed by interviews with parents (n = 13) and two dentists. Our findings revealed that children expected VR to provide immediate relief, social support, and a sense of control during dental treatment, parents sought educational opportunities for their children to learn about oral health, and dentists prioritized treatment efficiency and safety issues. Drawing from the findings, we discuss the considerations of multi-stakeholders for developing VR-assisted anxiety management applications for children within and beyond dental settings.

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arXiv2025

Novobo: Supporting Teachers' Peer Learning of Instructional Gestures by Teaching a Mentee AI-Agent Together

Instructional gestures are essential for teaching, as they enhance communication and support student comprehension. However, existing training methods for developing these embodied skills can be time-consuming, isolating, or overly prescriptive. Research suggests that developing these tacit, experiential skills requires teachers' peer learning, where they learn from each other and build shared knowledge. This paper introduces Novobo, an apprentice AI-agent stimulating teachers' peer learning of instructional gestures through verbal and bodily inputs. Positioning the AI as a mentee employs the learning-by-teaching paradigm, aiming to promote deliberate reflection and active learning. Novobo encourages teachers to evaluate its generated gestures and invite them to provide demonstrations. An evaluation with 30 teachers in 10 collaborative sessions showed Novobo prompted teachers to share tacit knowledge through conversation and movement. This process helped teachers externalize, exchange, and internalize their embodied knowledge, promoting collaborative learning and building a shared understanding of instructional gestures within the local teaching community. This work advances understanding of how teachable AI agents can enhance collaborative learning in teacher professional development, offering valuable design insights for leveraging AI to promote the sharing and construction of embodied and practical knowledge.

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arXiv2025

ClassComet: Exploring and Designing AI-generated Danmaku in Educational Videos to Enhance Online Learning

Danmaku, users' live comments synchronized with, and overlaying on videos, has recently shown potential in promoting online video-based learning. However, user-generated danmaku can be scarce-especially in newer or less viewed videos and its quality is unpredictable, limiting its educational impact. This paper explores how large multimodal models (LMM) can be leveraged to automatically generate effective, high-quality danmaku. We first conducted a formative study to identify the desirable characteristics of content- and emotion-related danmaku in educational videos. Based on the obtained insights, we developed ClassComet, an educational video platform with novel LMM-driven techniques for generating relevant types of danmaku to enhance video-based learning. Through user studies, we examined the quality of generated danmaku and their influence on learning experiences. The results indicate that our generated danmaku is comparable to human-created ones, and videos with both content- and emotion-related danmaku showed significant improvement in viewers' engagement and learning outcome.

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arXiv2025

Eggly: Designing Mobile Augmented Reality Neurofeedback Training Games for Children with Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects how children communicate and relate to other people and the world around them. Emerging studies have shown that neurofeedback training (NFT) games are an effective and playful intervention to enhance social and attentional capabilities for autistic children. However, NFT is primarily available in a clinical setting that is hard to scale. Also, the intervention demands deliberately-designed gamified feedback with fun and enjoyment, where little knowledge has been acquired in the HCI community. Through a ten-month iterative design process with four domain experts, we developed Eggly, a mobile NFT game based on a consumer-grade EEG headband and a tablet. Eggly uses novel augmented reality (AR) techniques to offer engagement and personalization, enhancing their training experience. We conducted two field studies (a single-session study and a three-week multi-session study) with a total of five autistic children to assess Eggly in practice at a special education center. Both quantitative and qualitative results indicate the effectiveness of the approach as well as contribute to the design knowledge of creating mobile AR NFT games.

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arXiv2025

TherAIssist: Assisting Art Therapy Homework and Client-Practitioner Collaboration through Human-AI Interaction

Art therapy homework is essential for fostering clients' reflection on daily experiences between sessions. However, current practices present challenges: clients often lack guidance for completing tasks that combine art-making and verbal expression, while therapists find it difficult to track and tailor homework. How HCI systems might support art therapy homework remains underexplored. To address this, we present TherAIssist, comprising a client-facing application leveraging human-AI co-creative art-making and conversational agents to facilitate homework, and a therapist-facing application enabling customization of homework agents and AI-compiled homework history. A 30-day field study with 24 clients and 5 therapists showed how TherAIssist supported clients' homework and reflection in their everyday settings. Results also revealed how therapists infused their practice principles and personal touch into the agents to offer tailored homework, and how AI-compiled homework history became a meaningful resource for in-session interactions. Implications for designing human-AI systems to facilitate asynchronous client-practitioner collaboration are discussed.

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arXiv2025

BIG-AOME: Designing Bodily Interaction Gamification towards Anti-sedentary Online Meeting Environments

Online meetings have become an integral part of daily life, but prolonged screen time poses significant health risks. While various interventions address sedentary lifestyles, few focus on mitigating sedentary behavior during online meetings. Design opportunities in this context remain underexplored. This study investigates the design of gamified bodily interactions as anti-sedentary measures during online meetings using a research through design approach. In collaboration with 11 users, we co-designed and iterated three prototypes, resulting in the BIG-AOME (Bodily Interaction Gamification towards Anti-sedentary Online Meeting Environments) framework. User studies with 15 participants across three groups evaluated these prototypes through semi-structured interviews analyzed using Hsieh's qualitative content analysis. Findings show that gamified bodily interactions encourage physical movement while reducing awkwardness during online meetings. Participants valued the social engagement fostered by cooperative and competitive elements in these games, enhancing social dynamics while encouraging physical movement. Such games can also serve as online icebreakers or playful decision-making tools. This study offers a comprehensive analysis of design dimensions within the BIG-AOME framework, including body engagement, attention, bodily interplay, timeliness, and virtual/physical environments, highlighting the potential of anti-sedentary bodily interactions to mitigate sedentary behavior and enhance social connections in online meetings.

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arXiv2025

Walk in Their Shoes to Navigate Your Own Path: Learning About Procrastination Through A Serious Game

Procrastination, the voluntary delay of tasks despite potential negative consequences, has prompted numerous time and task management interventions in the HCI community. While these interventions have shown promise in addressing specific behaviors, psychological theories suggest that learning about procrastination itself may help individuals develop their own coping strategies and build mental resilience. However, little research has explored how to support this learning process through HCI approaches. We present ProcrastiMate, a text adventure game where players learn about procrastination's causes and experiment with coping strategies by guiding in-game characters in managing relatable scenarios. Our field study with 27 participants revealed that ProcrastiMate facilitated learning and self-reflection while maintaining psychological distance, motivating players to integrate newly acquired knowledge in daily life. This paper contributes empirical insights on leveraging serious games to facilitate learning about procrastination and offers design implications for addressing psychological challenges through HCI approaches.

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arXiv2025

Breaking Barriers or Building Dependency? Exploring Team-LLM Collaboration in AI-infused Classroom Debate

Classroom debates are a unique form of collaborative learning characterized by fast-paced, high-intensity interactions that foster critical thinking and teamwork. Despite the recognized importance of debates, the role of AI tools, particularly LLM-based systems, in supporting this dynamic learning environment has been under-explored in HCI. This study addresses this opportunity by investigating the integration of LLM-based AI into real-time classroom debates. Over four weeks, 22 students in a Design History course participated in three rounds of debates with support from ChatGPT. The findings reveal how learners prompted the AI to offer insights, collaboratively processed its outputs, and divided labor in team-AI interactions. The study also surfaces key advantages of AI usage, reducing social anxiety, breaking communication barriers, and providing scaffolding for novices, alongside risks, such as information overload and cognitive dependency, which could limit learners' autonomy. We thereby discuss a set of nuanced implications for future HCI exploration.

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arXiv2024

GenPod: Constructive News Framing in AI-Generated Podcasts More Effectively Reduces Negative Emotions Than Non-Constructive Framing

AI-generated media products are increasingly prevalent in the news industry, yet their impacts on audience perception remain underexplored. Traditional media often employs negative framing to capture attention and capitalize on news consumption, and without oversight, AI-generated news could reinforce this trend. This study examines how different framing styles-constructive versus non-constructive-affect audience responses in AI-generated podcasts. We developed a pipeline using generative AI and text-to-speech (TTS) technology to create both constructive and non-constructive news podcasts from the same set of news resources. Through empirical research (N=65), we found that constructive podcasts significantly reduced audience's negative emotions compared to non-constructive podcasts. Additionally, in certain news contexts, constructive framing might further enhance audience self-efficacy. Our findings show that simply altering the framing of AI generated content can significantly impact audience responses, and we offer insights on leveraging these effects for positive outcomes while minimizing ethical risks.

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arXiv2024

Anger Speaks Louder? Exploring the Effects of AI Nonverbal Emotional Cues on Human Decision Certainty in Moral Dilemmas

Exploring moral dilemmas allows individuals to navigate moral complexity, where a reversal in decision certainty, shifting toward the opposite of one's initial choice, could reflect open-mindedness and less rigidity. This study probes how nonverbal emotional cues from conversational agents could influence decision certainty in moral dilemmas. While existing research heavily focused on verbal aspects of human-agent interaction, we investigated the impact of agents expressing anger and sadness towards the moral situations through animated chat balloons. We compared these with a baseline where agents offered same responses without nonverbal cues. Results show that agents displaying anger significantly caused reversal shifts in decision certainty. The interaction between participant gender and agents' nonverbal emotional cues significantly affects participants' perception of AI's influence. These findings reveal that even subtly altering agents' nonverbal cues may impact human moral decisions, presenting both opportunities to leverage these effects for positive outcomes and ethical risks for future human-AI systems.

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arXiv2024

MultiSurf-GPT: Facilitating Context-Aware Reasoning with Large-Scale Language Models for Multimodal Surface Sensing

Surface sensing is widely employed in health diagnostics, manufacturing and safety monitoring. Advances in mobile sensing affords this potential for context awareness in mobile computing, typically with a single sensing modality. Emerging multimodal large-scale language models offer new opportunities. We propose MultiSurf-GPT, which utilizes the advanced capabilities of GPT-4o to process and interpret diverse modalities (radar, microscope and multispectral data) uniformly based on prompting strategies (zero-shot and few-shot prompting). We preliminarily validated our framework by using MultiSurf-GPT to identify low-level information, and to infer high-level context-aware analytics, demonstrating the capability of augmenting context-aware insights. This framework shows promise as a tool to expedite the development of more complex context-aware applications in the future, providing a faster, more cost-effective, and integrated solution.

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arXiv2024

AniBalloons: Animated Chat Balloons as Affective Augmentation for Social Messaging and Chatbot Interaction

Despite being prominent and ubiquitous, message-based interaction is limited in nonverbally conveying emotions. Besides emoticons or stickers, messaging users continue seeking richer options for affective communication. Recent research explored using chat balloons' shape and color to communicate emotional states. However, little work explored whether and how chat-balloon animations could be designed to convey emotions. We present the design of AniBalloons, 30 chat-balloon animations conveying Joy, Anger, Sadness, Surprise, Fear, and Calmness. Using AniBalloons as a research means, we conducted three studies to assess the animations' affect recognizability and emotional properties (N = 40), and probe how animated chat balloons would influence communication experience in typical scenarios including instant messaging (N = 72) and chatbot service (N = 70). Our exploration contributes a set of chat-balloon animations to complement non-nonverbal affective communication for a range of message-based interfaces, and empirical insights into how animated chat balloons might mediate particular conversation experiences (e.g., perceived interpersonal closeness, or chatbot personality).

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