Evaluating the Efficacy of Interactive Language Therapy Based on LLM for High-Functioning Autistic Adolescent Psychological Counseling
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Abstract (arXiv)
This study investigates the efficacy of Large Language Models (LLMs) in interactive language therapy for high-functioning autistic adolescents. With the rapid advancement of artificial intelligence, particularly in natural language processing, LLMs present a novel opportunity to augment traditional psychological counseling methods. This research primarily focuses on evaluating the LLM's ability to engage in empathetic, adaptable, and contextually appropriate interactions within a therapeutic setting. A comprehensive evaluation was conducted by a panel of clinical psychologists and psychiatrists using a specially developed scorecard. The assessment covered various aspects of the LLM's performance, including empathy, communication skills, adaptability, engagement, and the ability to establish a therapeutic alliance. The study avoided direct testing with patients, prioritizing privacy and ethical considerations, and instead relied on simulated scenarios to gauge the LLM's effectiveness. The results indicate that LLMs hold significant promise as supportive tools in therapy, demonstrating strengths in empathetic engagement and adaptability in conversation. However, challenges in achieving the depth of personalization and emotional understanding characteristic of human therapists were noted. The study also highlights the importance of ethical considerations in the application of AI in therapeutic contexts. This research provides valuable insights into the potential and limitations of using LLMs in psychological counseling for autistic adolescents. It lays the groundwork for future explorations into AI's role in mental health care, emphasizing the need for ongoing development to enhance the capabilities of these models in therapeutic settings.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 0 of its 27 references that are in the map. First posted on arXiv 12 Nov 2023.
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Citing papers per year: 2024: 5; 2025: 4.
9 papers.
- 2024Large Language Models in Mental Health Care: a Scoping Reviewcited by 34
- 2024The Colorful Future of LLMs: Evaluating and Improving LLMs as Emotional Supporters for Queer Youthcited by 20
- 2025Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoningcited by 20
- 2025Personalized Safety in LLMs: A Benchmark and A Planning-Based Agent Approachcited by 16
- 2024"I Like Sunnie More Than I Expected!": Exploring User Expectation and Perception of an Anthropomorphic LLM-based Conversational Agent for Well-Being Supportcited by 5
- 2024Copiloting Diagnosis of Autism in Real Clinical Scenarios via LLMscited by 3
- 2024STAMPsy: Towards SpatioTemporal-Aware Mixed-Type Dialogues for Psychological Counselingcited by 2
- 2025Beyond Keywords: Evaluating Large Language Model Classification of Nuanced Ableismcited by 1
- 2025MoPHES:Leveraging on-device LLMs as Agent for Mobile Psychological Health Evaluation and Supportcited by 1
- 2025MoPHES:Leveraging on-device LLMs as Agent for Mobile Psychological Health Evaluation and Support17 Oct 2025
- 2025Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning14 Aug 2025
- 2025Beyond Keywords: Evaluating Large Language Model Classification of Nuanced Ableism26 May 2025
- 2025Personalized Safety in LLMs: A Benchmark and A Planning-Based Agent Approach24 May 2025
- 2024STAMPsy: Towards SpatioTemporal-Aware Mixed-Type Dialogues for Psychological Counseling21 Dec 2024
- 2024Copiloting Diagnosis of Autism in Real Clinical Scenarios via LLMs8 Oct 2024
- 2024"I Like Sunnie More Than I Expected!": Exploring User Expectation and Perception of an Anthropomorphic LLM-based Conversational Agent for Well-Being Support22 May 2024
- 2024The Colorful Future of LLMs: Evaluating and Improving LLMs as Emotional Supporters for Queer Youth19 Feb 2024
- 2024Large Language Models in Mental Health Care: a Scoping Review1 Jan 2024