Jul 2026· Brazilian Journal of Science· 0 citations· 22 references
TL;DR
This protocol provides a reproducible foundation for developing clinically relevant conversational AI in occupational therapy and for future feasibility and effectiveness studies.
Abstract
Conversational artificial intelligence (AI) has shown potential to support knowledge translation, personalized education, and access to health-related information. However, applications in neurodevelopmental care remain largely focused on screening and assessment, while conversational systems tailored to occupational therapy are scarce. Moreover, general-purpose generative AI may produce inaccurate, insufficiently contextualized, or clinically inappropriate responses, highlighting the need for evidence-based, domain-specific systems with robust safety mechanisms. This study describes the protocol for the co-design, development, and preliminary evaluation of a conversational AI system designed to support occupational therapists and parents of children aged 5–12 years with neurodevelopmental disorders in Greece. The system is intended as an educational and decision-support resource and will not replace diagnosis, clinical judgment, or individualized intervention. A co-designed, multi-phase, mixed-methods proof-of-concept design will be adopted, informed by the Medical Research Council framework, Design Science Research, user-centered design principles, and the CeHRes Roadmap 2.0. Development will include evidence synthesis, stakeholder needs assessment, knowledge base construction, iterative prototype development, and expert, technical, safety, and user evaluation. The system will integrate a curated occupational therapy knowledge base, retrieval-augmented generation, role-specific prompting, source verification, and layered safety guardrails. Expected outputs include a stakeholder-informed Greek-language minimum viable product and a transparent framework linking evidence, user requirements, technical design, and evaluation criteria. Preliminary evaluation will assess factual accuracy, evidence concordance, occupational therapy relevance, clinical appropriateness, safety, usability, acceptability, and perceived usefulness. This protocol provides a reproducible foundation for developing clinically relevant conversational AI in occupational therapy and for future feasibility and effectiveness studies.
Generative artificial intelligence (GenAI) and multimodal interaction are attracting growing attention in mental health-related education and care. At the same time, pre-service childcare professionals are increasingly expected to observe and respond sensitively to infants’ and toddlers’ emotional signals, attachment needs, separation reactions, and interaction patterns. These contextual and ethically sensitive capacities require more than lectures or procedural training. Authentic practicum remains indispensable, but safeguarding, consent, privacy, placement availability, supervisory capacity, and the unpredictability of naturally occurring situations can limit safe rehearsal. Drawing on research on large language models, conversational agents, responsive caregiving, competency-based assessment, work-integrated learning, implementation science, and health AI governance, this conceptual analysis proposes five design domains for education–childcare partnerships: competency mapping, multimodal simulation, AI-supported reflective feedback, practicum readiness assessment, and partnership governance. Multimodal GenAI is positioned as human-supervised learning infrastructure that may extend established simulation through adaptive scenario variation, contingent interaction, multimodal cue integration, and scalable formative feedback. The proposed architecture includes a developmental-fidelity gate under which synthetic infant- or toddler-like materials would be reviewed against predefined anatomical, affective, temporal, cultural, and pedagogical criteria before use. The framework defines non-clinical mental health support competence through five observable dimensions and specifies human authority, data-minimization boundaries, and a staged research pathway beginning with a candidate Pre-Practicum Mental Health Support Competency Assessment (PP-MHSCA) blueprint. The proposed competency levels, readiness categories, critical-error rules, scoring procedures, and thresholds are candidate specifications requiring stakeholder review, pilot testing, standard setting, and empirical validation before implementation. Comparative studies with conventional simulation and lecture/case preparation, followed by prospective tests of practicum performance, can determine whether the approach adds educational value. The framework offers a cautious and governable bridge to practicum without displacing educators, mentors, or real-world experience. The framework is grounded in broadly applicable professional standards and governance principles, with China proposed as the initial context for implementation and empirical evaluation. Evidence generated through this process is expected to inform the framework’s subsequent adaptation, transfer, and application in other national and regional contexts.
Wenlan Xie, Chun-Ye Xiang, Fan Wu et al.· Frontiers in Psychiatry· 0 citations
Abstract Background Autism spectrum disorder is underdiagnosed in adults, with increasing demand on diagnostic services and prolonged waiting times. AI-powered tools may offer scalable solutions for early screening and triage. Objective This proof-of-concept study aimed to evaluate the feasibility, acceptability, and user experience of ASIST (Autism Screening With Intelligent Supportive Technology), an AI-powered conversational platform designed to support structured history taking and referral preparation for adults seeking to explore autistic traits. Methods A mixed methods feasibility study was conducted. Adults (N=12) interacted with a voice-based AI chatbot delivering validated screening tools (10-item Autism Spectrum Quotient and 2-Minute Autism Detection Scale). Quantitative acceptability and usability were assessed using items informed by the theoretical framework of acceptability alongside open-text qualitative feedback. Results Eleven patient and public involvement and engagement contributors informed the development and refinement of the study, and 12 adults completed the pilot evaluation. Participants generally reported positive perceptions of the chatbot, including low effort, favorable confidence, and perceived fairness. Open-text feedback highlighted the perceived value of ASIST as a history taking and referral support tool while also identifying areas for refinement, including pacing, speech clarity, and response format. Conclusions AI-powered conversational tools may offer scalable solutions for structured history taking, referral preparation, and early triage. Further large-scale validation, pathway integration, and equity-focused evaluation are required before wider implementation.
Shabnam Sadeghi Esfahlani, Louise Prothero, N. Abdulmohdi et al.· JMIR Formative Research· 0 citations
A LINE-deployed conversational AI system grounded in the WHO iSupport for Dementia framework, built around an Assessment-Before-Intervention dialogue mechanism, which offers practical implications for the design of AI-assisted care systems in dementia and other emotionally sensitive healthcare contexts.
FIT is a proposed framework, designed to reduce three specific risks that have been linked to conversational MH-AI, and the dimensions are intended as practical starting guardrails, not as an exhaustive list.
J. Steinberg, W. Thies, John R. Weisz· Journal of Consulting and Cl...· 0 citations
Based on the findings, AI seems highly promising for patient-specific ASD therapy via proactive, data-driven scaffolding, but more RCTs and crucial augmentation of representation gaps concerning adult and female ASD phenotype studies are required.
Julia Kuca, Magdalena Stencel, Błażej Pilarski et al.· Frontiers in Psychiatry· 0 citations
Artificial intelligence (AI) systems and large language models (LLMs) offer substantial potential to augment or even fundamentally change elements of psychological assessment and treatment. However, current AI technologies have yet to demonstrate the capacity to effect meaningful and sustained clinical change. This gap reflects both the limited integration of clinical science knowledge into language models and applications built using them, as well as the mismatch between the brief, minutes-long nature of most AI interactions and the months-long course of most evidence-based treatments. Here we introduce the Evidence-Based Psychotherapy with AI (EBP-AI) framework, which articulates a set of principles for developing effective clinical AI applications: a) psychodiagnostic assessment, b) longitudinal case conceptualization, c) appropriately dosed intervention planning, d) meaningful progress evaluation, e) rigorous validation with clinical populations, f) attention to real world implementation and use, g) clinically appropriate style, and h) understanding clinical psychology as a living science. We introduce a set of key technical questions for the development and evaluation of clinical LLMs and AIs aligned with these principles. Despite their potential, current clinical AIs fall short, in part due to issues with memory, sycophancy, and prioritizing short-term helpfulness over long-term clinical impact. Responsible and ethical design of effective, clinical-science-based AI systems will require understanding their limitations and strategically extending their capabilities.
Elizabeth C. Stade, Philip Held, H. A. Schwartz et al.· Journal of Psychopathology a...· 2 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
