The Autism Open Clinical Model (A.-O.C.M.) as a Phenomenological Framework for Prompt Design in Parent Training for Autism: Integrating Embodied Cognition and Artificial Intelligence

TL;DR

This paper introduces a new clinical model for autism treatment that combines embodied cognition and AI to improve parent training and therapist-caregiver interactions.

Contribution

The novel contribution is a clinical framework integrating phenomenology, embodied cognition, and AI for adaptive autism interventions.

Findings

Relational patterns in therapist-caregiver dynamics reveal structural elements useful for personalizing interventions.

Bodily and emotional attunement quality influences readiness for change in autism interventions.

AI-supported prompts enhance intervention effectiveness through adaptive clinician styles.

Abstract

Background/Objectives: In the treatment of autism spectrum disorders, families express the need for dedicated clinical spaces to manage emotional overload and to develop effective relational skills. Parent training addresses this need by supporting the parent–child relationship and fostering the child’s development. This study proposes a clinical protocol designed for psychotherapists and behavior analysts, based on the Autism Open Clinical Model (A.-O.C.M.), which integrates the rigor of Applied Behavior Analysis (ABA) with a phenomenological and embodied perspective. The model acknowledges technology—particularly artificial intelligence—as an opportunity to structure adaptive and personalized intervention tools. Methods: A multi-level prompt design system was developed, grounded in the principles of the A.-O.C.M. and integrated with generative AI. The tool employs clinical questions, semantic constraints, and levels of analysis to support the clinician’s reasoning and phenomenologically informed observation of behavior. Results: Recurrent relational patterns emerged in therapist–caregiver dynamics, allowing the identification of structural elements of the intersubjective field that are useful for personalizing interventions. In particular, prompt analysis highlighted how the quality of bodily and emotional attunement influences readiness for change, suggesting that intervention effectiveness increases when the clinician can adapt their style according to emerging phenomenological resonances. Conclusions: The design of clinical prompts rooted in embodied cognition and supported by AI represents a new frontier for psychotherapy that is more attuned to subjectivity. The A.-O.C.M. stands as a theoretical–clinical framework that integrates phenomenology and intelligent systems.

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