## 1 Introduction
Literature reviews on robotics in ADHD and autism care highlight promising technical results, e.g., improved engagement and attention, but also emphasize that current robot-assisted interventions require better scalability, more natural human-machine interaction, and improved data processing to be effective in real-world clinical settings [10]. These examples illustrate how robotic HCI systems, from intelligent prostheses to socially assistive robots, can be integrated into OT practice to augment traditional rehabilitation techniques. Beyond robotics, other AI-driven interfaces are advancing rehabilitation and OT outcomes. Wearable sensor systems, for example, can continuously monitor benefits and automatically recognize personal needs, providing objective data to therapists and caregivers [11]. Meanwhile, immersive technologies such as virtual reality (VR) combined with human-computer interfaces open new frontiers in physical therapy. VR provides an engaging, simulated environment for people to practice tasks, while interfaces translate user behavior into control commands, thus enabling hands-free interaction with smart home devices or assistive tools. Integrating VR with HCI has been shown to improve the accuracy and speed of neural command issuance by providing real-time feedback and adaptive training scenarios [12]. This synergy leads to more intuitive control of devices and greater user independence and efficiency in managing Internet-of-things (IoT) tasks, especially for individuals with severe mobility limitations. Similar VR and brain-computer interface (BCI) systems are explored for neurorehabilitation and cognitive training, with researchers highlighting the value of adaptive signal processing and multimodal feedback (e.g., combining brain signals with eye-tracking or motion data) to enhance user engagement and therapeutic effectiveness [13, 14]. Incorporating advanced AI techniques into these virtual interfaces is poised to further personalize rehabilitation as ML algorithms adjust to each user's behavioral patterns and progress. In recent years, AI has gained significant traction across various medical fields, demonstrating its potential to enhance diagnostics [15], prognostics [16], treatment planning [17], surgery [18], and patient monitoring [19]. Numerous reviews have mapped these broad medical applications, highlighting how ML, DL, and natural language processing transform healthcare delivery. Such applications of AI in healthcare range from medical imaging [20, 21] and precision medicine [22, 23] to predictive analytics [24], big data [25], and robotic surgery [26], thus offering enhanced accuracy and efficiency in clinical decision-making [27]. However, while the integration of AI into traditional medical disciplines has been extensively studied, its role in supporting patient-centered interventions such as OT, particularly through HCI technologies, remains underexplored. This scoping review aims to bridge this gap by uniquely focusing on the intersection of AI, HCI, and OT, identifying and mapping the current research, highlighting applications, and proposing directions for future work. In this context, the current scoping review was conducted to (i) explore the application of AI-driven HCI technologies within OT, (ii) identify emerging trends and techniques, such as ML, robotics, and VR, and (iii) highlight existing research gaps to inform future scientific approaches. Unlike previous literature that broadly addresses AI applications in healthcare, this review specifically focuses on the integration of AI and HCI in the notable field of OT and how these technologies enhance therapeutic interventions and patient engagement with different domains of human occupations. By providing an in-depth analysis of the methodologies, therapeutic contexts, and results of selected studies, this scoping review aims to illuminate the current state of AI-driven HCI in OT and guide future research efforts in this evolving field.