In November 2001, the Greek Society for the Protection of Autistic People (GSPAP) established the first residence for people with autism in Greece, following the guidelines of structured teaching and the TEACCH method with all 12 of the residents. Using interview questionnaires and systematic naturalistic observations, this case study explored the effectiveness of the training programme in the residence for the 12 adolescents and adults with autism, who had never received any other intervention or training.The instruments used for the evaluation were the Childhood Autism Rating Scale, the Vineland Adaptive Behavior Scales and structured observations. The categories evaluated were personal independence, social abilities and functional communication. After a period of 6 months the results showed significant progress in these three areas of functioning for all of the residents.The implications of the results in particular for further research and service development in Greece are discussed.

Occupational therapy (OT) is vital in improving functional outcomes and aiding recovery for individuals with long-term disabilities, particularly those resulting from neurological diseases. Traditional assessment methods often rely on clinical judgment and individualized evaluations, which may overlook broader, data-driven insights. The integration of artificial intelligence (AI) presents a transformative opportunity to enhance assessment precision and personalize therapeutic interventions. Additionally, advancements in human–computer interaction (HCI) enable more intuitive and adaptive AI-driven assessment tools, improving user engagement and accessibility in OT. This scoping review investigates current applications of AI in OT, particularly regarding the evaluation of functional outcomes and support for clinical decision-making. The literature search was conducted using the PubMed and Scopus databases. Studies were included if they focused on AI applications in evaluating functional outcomes within OT assessment tools. Out of an initial pool of 85 articles, 13 met the inclusion criteria, highlighting diverse AI methodologies such as support vector machines, deep neural networks, and natural language processing. These were primarily applied in domains including motor recovery, pediatric developmental assessments, and cognitive engagement evaluations. Findings suggest that AI can significantly improve evaluation processes by systematically integrating diverse data sources (e.g., sensor measurements, clinical histories, and behavioral analytics), generating precise predictive insights that facilitate tailored therapeutic interventions and comprehensive assessments of both pre- and post-treatment strategies. This scoping review also identifies existing gaps and proposes future research directions to optimize AI-driven assessment tools in OT.

Occupational therapy (OT) is a client-centered health profession focused on enhancing individuals’ ability to perform meaningful activities and daily tasks, particularly for those recovering from injury, illness, or disability. As a core component of rehabilitation, it promotes independence, well-being, and quality of life through personalized, goal-oriented interventions. Identifying and measuring the role of artificial intelligence (AI) in the human–computer interaction (HCI) within OT is critical for improving therapeutic outcomes and patient engagement. Despite AI’s growing significance, the integration of AI-driven HCI in OT remains relatively underexplored in the existing literature. This scoping review identifies and maps current research on the topic, highlighting applications and proposing directions for future work. A structured literature search was conducted using the Scopus and PubMed databases. Articles were included if their primary focus was on the intersection of AI, HCI, and OT. Out of 55 retrieved articles, 26 met the inclusion criteria. This work highlights three key findings: (i) machine learning, robotics, and virtual reality are emerging as prominent AI-driven HCI techniques in OT; (ii) the integration of AI-enhanced HCI offers significant opportunities for developing personalized therapeutic interventions; (iii) further research is essential to evaluate the long-term efficacy, ethical implications, and patient outcomes associated with AI-driven HCI in OT. These insights aim to guide future research efforts and clinical applications within this evolving interdisciplinary field. In conclusion, AI-driven HCI holds considerable promise for advancing OT practice, yet further research is needed to fully realize its clinical potential.

Individuals with High-Functioning Autism present impairments in communication, social interaction, and motor development. A low level of motor skills, namely difficulties in gross and fine mobility, and in motor control, discourage individuals with High-Functioning Autism from being involved in physical activities, resulting in fewer opportunities for social interaction. There is not much evidence available about the effects of regular swimming exercise and/or aquatic therapy on health promotion in adults with High-Functioning Autism. An adult male (22 yrs) diagnosed with High-Functioning Autism participated in a combined 6-month swimming and aquatic therapy program (two sessions/week, 60 min each). The pre- and post-intervention assessments consisted of physical fitness, balance, functional ability, and psychomotor tests. The post-intervention assessments showed improvements in the standing long jump (+100%), hand grip force (+71.7%), bend arm hang test (+123.1%), and the physiological parameters in the 6 min walk test (+10.2%). On the opposite, decrements in the sit-up (−12%) and sit-and-reach test (−6.3%) were observed. It was noted that the participant frequently lost interest and focus quickly, resulting in the abandonment of the exercise. Conclusively, there is a great need for further research on this topic examining a larger adult population.