arXiv subjects

Keiko Katsuragawa

Publications and source records attributed to Keiko Katsuragawa.

4 recordsLinked to original sources

arXiv2026

EMooly: Supporting Autistic Children in Collaborative Social-Emotional Learning with Caregiver Participation through Interactive AI-infused and AR Activities

Children with autism spectrum disorder (ASD) have social-emotional deficits that lead to difficulties in recognizing emotions as well as understanding and responding to social interactions. This study presents EMooly, a tablet game that actively involves caregivers and leverages augmented reality (AR) and generative AI (GenAI) to enhance social-emotional learning for autistic children. Through a year of collaborative effort with five domain experts, we developed EMooly that engages children through personalized social stories, interactive and fun activities, and enhanced caregiver participation, focusing on emotion understanding and facial expression recognition. Compared with a baseline, a controlled study with 24 autistic children and their caregivers showed EMooly significantly improved children's emotion recognition skills and its novel features were preferred and appreciated. EMooly demonstrates the potential of AI and AR in enhancing social-emotional development for autistic children via prompt personalizing and engagement, and highlights the importance of caregiver involvement for optimal learning outcomes.

cs.HC↗

arXiv2026

"A Glimpse, Not a Gaze": Using Generative AI to Balance Privacy and Awareness in Inter-generational Caregiving

As older adults increasingly prefer to age in place, their adult children often assume the role of informal caregivers. This dynamic creates a distinct tension between the adult child's need for awareness and the older adult's fundamental right to privacy. Traditional monitoring technologies, such as raw video feeds, often compromise the older adult's autonomy. To address this challenge, this study explores the use of generative Artificial Intelligence (GenAI) to create abstract, privacy-preserving ``visual summaries'' of daily activities. We design a 10-day Experience Sampling Method (ESM) study with dyads consisting of older adults and their adult children. Through daily smartphone prompts, participants report their current context and evaluate pre-generated AI sketches, indicating their willingness to share or receive these images. Follow-up interviews will further investigate participants' boundary-setting behaviours. This research aims to quantify the privacy mismatch between generations and provide actionable design guidelines for applying visual abstraction in AI-mediated caregiving tools, ultimately supporting inter-generational connection while protecting user dignity.

cs.HC↗

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.

cs.HC↗

arXiv2024

Designing AI-Enabled Games to Support Social-Emotional Learning for Children with Autism Spectrum Disorders

Children with autism spectrum disorder (ASD) experience challenges in grasping social-emotional cues, which can result in difficulties in recognizing emotions and understanding and responding to social interactions. Social-emotional intervention is an effective method to improve emotional understanding and facial expression recognition among individuals with ASD. Existing work emphasizes the importance of personalizing interventions to meet individual needs and motivate engagement for optimal outcomes in daily settings. We design a social-emotional game for ASD children, which generates personalized stories by leveraging the current advancement of artificial intelligence. Via a co-design process with five domain experts, this work offers several design insights into developing future AI-enabled gamified systems for families with autistic children. We also propose a fine-tuned AI model and a dataset of social stories for different basic emotions.

cs.HC↗