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Evaluating the Efficacy of Interactive Language Therapy Based on LLM for High-Functioning Autistic Adolescent Psychological Counseling

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arxiv 2311.09243 v1 pith:5FIHBDX3 submitted 2023-11-12 cs.HC cs.AI

classification cs.HCcs.AI
keywords languagellmstherapeuticautisticcounselingpsychologicaltherapyability
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 6 citations worldwide. Full citation record

  1. Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning

    cs.CL 2025-08 conditional novelty 6.0 of 10

    A 7B Chinese psychological LLM trained with curated reasoning questions and empathetic dialogues via SFT plus GRPO approaches DeepSeek-R1's exam accuracy at a fraction of the size.

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