AACessTalk: Fostering Communication between Minimally Verbal Autistic Children and Parents with Contextual Guidance and Card Recommendation
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Abstract (arXiv)
As minimally verbal autistic (MVA) children communicate with parents through few words and nonverbal cues, parents often struggle to encourage their children to express subtle emotions and needs and to grasp their nuanced signals. We present AACessTalk, a tablet-based, AI-mediated communication system that facilitates meaningful exchanges between an MVA child and a parent. AACessTalk provides real-time guides to the parent to engage the child in conversation and, in turn, recommends contextual vocabulary cards to the child. Through a two-week deployment study with 11 MVA child-parent dyads, we examine how AACessTalk fosters everyday conversation practice and mutual engagement. Our findings show high engagement from all dyads, leading to increased frequency of conversation and turn-taking. AACessTalk also encouraged parents to explore their own interaction strategies and empowered the children to have more agency in communication. We discuss the implications of designing technologies for balanced communication dynamics in parent-MVA child interaction.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 9 of its 143 references that are in the map. First posted on arXiv 15 Sep 2024.
Builds on
9 of 143 references.
References · 9
- 2023Prompting Large Language Model for Machine Translation: A Case Studycited by 247
- 2023Augmenting Language Models with Long-Term Memorycited by 186
- 2020Attention is All You Need in Speech Separationcited by 184
- 2023Having Beer after Prayer? Measuring Cultural Bias in Large Language Modelscited by 153
- 2022Keep Me Updated! Memory Management in Long-term Conversationscited by 62
- 2023MindfulDiary: Harnessing Large Language Model to Support Psychiatric Patients' Journalingcited by 50
- 2023ChaCha: Leveraging Large Language Models to Prompt Children to Share Their Emotions about Personal Eventscited by 31
- 2022Captivate! Contextual Language Guidance for Parent-Child Interactioncited by 3
- 2023Using Large Language Models to Accelerate Communication for Users with Severe Motor Impairmentscited by 3
Built on by
Citing papers per year: 2025: 5; 2026: 2.
7 papers.
- 2025Towards Aligning Multimodal LLMs with Human Experts: A Focus on Parent-Child Interactioncited by 1
- 2025Human-AI Alignment of Multimodal Large Language Models with Speech-Language Pathologists in Parent-Child Interactionscited by 0
- 2025Autiverse: Eliciting Autistic Adolescents' Daily Narratives through AI-guided Multimodal Journalingcited by 0
- 2025AutiHero: Engaging Parents in Creating Personalized, Multi-path Social Narratives for Autistic Childrencited by 0
- 2025QoNext: Towards Next-generation QoE for Foundation Modelscited by 0
- 2026Exploring Implicit Perspectives on Autism in Large Language Models Through Multi-Agent Simulationscited by 0
- 2026Digital Harf: A Clinically Integrated Multimodal AI System for Pervasive Arabic Speech and Language Therapycited by 0
- 2026Digital Harf: A Clinically Integrated Multimodal AI System for Pervasive Arabic Speech and Language Therapy18 May 2026
- 2026Exploring Implicit Perspectives on Autism in Large Language Models Through Multi-Agent Simulations21 Jan 2026
- 2025Towards Aligning Multimodal LLMs with Human Experts: A Focus on Parent-Child Interaction6 Nov 2025
- 2025QoNext: Towards Next-generation QoE for Foundation Models26 Sep 2025
- 2025AutiHero: Engaging Parents in Creating Personalized, Multi-path Social Narratives for Autistic Children22 Sep 2025
- 2025Autiverse: Eliciting Autistic Adolescents' Daily Narratives through AI-guided Multimodal Journaling22 Sep 2025
- 2025Human-AI Alignment of Multimodal Large Language Models with Speech-Language Pathologists in Parent-Child Interactions6 Jun 2025