Using Artificial Intelligence to Improve Empathetic Statements in Autistic Adolescents and Adults: A Randomized Clinical Trial.
Challenges with social communication and social interaction are a defining characteristic of autism spectrum disorder (ASD). These challenges frequently interfere with making friendships, securing and maintaining employment, and can lead to co-occurring conditions. While face-to-face clinical interv...
| Publicado en: | Journal of Autism & Developmental Disorders Vol. 56; no. 7; pp. 2513 - 2530 |
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| Autores principales: | , , , , , , , , , |
| Formato: | pictorial research tables/charts randomized controlled trial Journal Article |
| Publicado: |
Springer Nature
Jul2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195184717&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195184717 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01623257 AUT jtl: Journal of Autism & Developmental Disorders issn: 01623257 maglogo: N pubinfo: dt: Jul2026 vid: 56 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 195184717 183018668 195184717 195184717 10.1007/s10803-025-06734-x 195184717 ppf: 2513 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Using Artificial Intelligence to Improve Empathetic Statements in Autistic Adolescents and Adults: A Randomized Clinical Trial. aug: au: Koegel, Lynn Kern Ponder, Elizabeth Bruzzese, Tommy Wang, Mason Semnani, Sina J. Chi, Nathan Koegel, Brittany L. Lin, Tzu Yuan Swarnakar, Ankush Lam, Monica S. affil: https://ror.org/00f54p054 Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA sug: subj: Artificial Intelligence Utilization Autism Spectrum Disorder Rehabilitation Autism Spectrum Disorder Rehabilitation Natural Language Processing Empathy Communication Communication Skills Training Autism Spectrum Disorder Diagnosis Treatment Outcomes Human Male Female Adolescence Adult Videoconferencing Sex Factors Race Factors Ethnic Groups Social Skills Interpersonal Relations Confidence Patient Satisfaction Machine Learning Randomized Controlled Trials Random Assignment Interrater Reliability United States Summated Rating Scaling Descriptive Statistics Mann-Whitney U Test Attention Deficit Hyperactivity Disorder Anxiety Depression Funding Source Adolescent: 13-18 years Adult: 19-44 years Male Female ab: Challenges with social communication and social interaction are a defining characteristic of autism spectrum disorder (ASD). These challenges frequently interfere with making friendships, securing and maintaining employment, and can lead to co-occurring conditions. While face-to-face clinical interventions with trained professionals can be helpful in improving social conversation, they can be costly and are unavailable to many, particularly given the high prevalence of ASD and lack of professional training. The purpose of this study was to assess whether an AI program using a Large Language Model (LLM) would improve verbal empathetic responses during social conversation. Autistic adolescents and adults, 11–35 years of age, who were able to engage in conversation but demonstrated challenges with empathetic responses participated in this study. A randomized clinical trial design was used to assess the effects of the AI program (Noora) compared to a waitlist control group. Noora asks participants to respond to leading statements and provides feedback on their answers. In this study, participants were asked to respond to 10 statements per day 5 days per week for 4 weeks for an expected total of 200 trials. Pre- and post-intervention conversation samples were collected to assess generalization during natural conversation. Additionally pre- and post-intervention questionnaires regarding each participant's comfort during social conversation and participants' satisfaction with the AI program were collected. The results of this study demonstrated that empathetic responses could be greatly improved by using an AI program for a short period of time. Participants in the experimental group showed statistically significant improvements in empathetic responses, which generalized to social conversation, compared to the waitlist control group. Some participants in the experimental group reported improved confidence in targeted areas and most reported high levels of satisfaction with the program. These findings suggest that AI using LLMs can be used to improve empathetic responses, thereby providing a time- and cost-efficient support program for improving social conversation in autistic adolescents and adults. pubtype: Academic Journal doctype: pictorial research tables/charts randomized controlled trial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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