Currently, high-tech assistive technologies (ATs), particularly Socially Assistive Robots (SARs), virtual reality (VR) and conversational AI (ConvAI), are considered very useful in supporting professionals in Speech and Language Therapy (SLT) for children with communication disorders. However, despite a positive public perception, therapists face difficulties when integrating these technologies into practice due to technical challenges and a lack of user-friendly interfaces. To address this gap, a design-based research approach has been employed to streamline the integration of SARs, VR and ConvAI in SLT, and a new software platform called “ATLog” has been developed for designing interactive and playful learning scenarios with ATs. ATLog’s main features include visual-based programming with graphical interface, enabling therapists to intuitively create personalized interactive scenarios without advanced programming skills. The platform follows a subprocess-oriented design, breaking down SAR skills and VR scenarios into microskills represented by pre-programmed graphical blocks, tailored to specific treatment domains, therapy goals, and language skill levels. The ATLog platform was evaluated by 27 SLT experts using the Technology Acceptance Model (TAM) and System Usability Scale (SUS) questionnaires, extended with additional questions specifically focused on ATLog structure and functionalities. According to the SUS results, most of the experts (74%) evaluated ATLog with grades over 70, indicating high acceptance of its usability. Over half (52%) of the experts rated the additional questions focused on ATLog’s structure and functionalities in the A range (90–100), while 26% rated them in the B range (80–89), showing strong acceptance of the platform for creating and running personalized interactive scenarios with ATs. According to the TAM results, experts gave high grades for both perceived usefulness (44% in the A range) and perceived ease of use (63% in the A range).

Virtual reality (VR) games are becoming increasingly popular for entertainment. When integrated into speech and language therapy (SLT) practice, VR can be an innovative tool that supports therapeutic interventions by offering new opportunities for immersive interaction and greater motivation for individuals with language disorders to participate in therapy. In this study, we aim to introduce a methodology for designing interactive VR serious-game scenarios for speech and language therapy, together with an integrated multidisciplinary user-feedback assessment framework. The research methodology is based on design research principles and an innovative UX framework to guide the design, evaluation, and improvement process. The feasibility and usability of the methodology and framework are demonstrated through preliminary interactive sessions involving 10 children and adolescents with language disorders (5–13 years) and 10 students undergoing speech and language therapy. Their role was to evaluate the serious VR games developed in accordance with specific speech and language therapy goals. The assessment framework consists of a four-level VR serious game hub statistics module, phonetic and linguistic analysis of children’s and adolescents’ utterances from the serious game sessions, and usability and design questionnaires. As a result, six VR serious games with integrated large language model (LLM)-based conversational AI were designed for speech and language therapy. Based on the outcomes, a safety protocol for conducting VR-based therapeutic activities was proposed. We used descriptive statistics and Friedman and Wilcoxon post hoc tests with Bonferroni-adjusted p -values for pairwise comparisons of questionnaires and speech and language assessment data. While preliminary, the results of the phonetic and linguistic analyses conducted using Praat indicate that the VR serious games effectively capture and maintain children’s attention, elicit a high level of emotional expressiveness, and motivate them to communicate. The feedback collected through the participant questionnaires showed that the VR serious games provided a highly positive user experience. No symptoms of cybersickness or dizziness were observed during the game sessions. Younger children interacted with the VR environment using controllers, while older individuals preferred interaction with hand gestures. The proposed solution can be personalized to individual therapeutic needs, enabling children with language disorders to be engaged in therapy, while the conversational AI integrated into VR stimulates not only expressive development but also receptive language development. To confirm the promising preliminary results, further evaluations with a larger group of children and longitudinal studies are required.

Smart campus development is increasingly associated with the combined use of IoT technologies, artificial intelligence, cloud infrastructures, and large-scale data analytics in higher education. Despite this progress, many existing data models are not well-suited to the educational domain, particularly when interoperability and real-time analytical capabilities are required. To address this limitation, the study proposes a Smart Campus Education Data Model (SCEDM), which can be integrated into any FIWARE-based platform. The model is organized as a layered architecture that includes data acquisition, processing, and storage; analytics and decision support; application presentation; and security. The proposed model is not presented only at a conceptual level; it is also validated in a containerized FIWARE environment built around the Orion-ld Context Broker and NGSI-ld specifications. The SCEDM model is validated in a system that supports real-time state management across multiple campus domains. The model’s practical operation is validated across several experimental scenarios, including a simulation of a lecture process, classroom occupancy monitoring, and automated notifications to external platforms. In addition, the study compares five international case studies from different contexts. The comparison shows that, despite differences across local settings, similar benefits can be observed in campus operations and learning conditions. The study also recognizes several continuing challenges in the development of smart campuses, including interoperability, long-term scalability, data governance, privacy protection, stakeholder engagement, and financial sustainability. In response to these issues, the authors propose practical design guidelines alongside strategic recommendations for adoption at the institutional level.