This study investigates the effectiveness of designing a gamified Virtual Reality (VR) intervention, grounded in Kolb’s Experiential Learning Theory, in enhancing social reciprocity, affective expression, and daily living skills among Thai children with Autism Spectrum Disorder (ASD). Utilizing the Meta Quest 2 headset, the intervention simulated five real-world contexts—home routines, classroom behavior, street crossing, supermarket shopping, and fast-food interactions. A quasi-experimental pretest-posttest control group design was employed with 33 children aged 6–12, complemented by thematic analysis of caregiver and therapist interviews. Results from the Social Responsiveness Scale, Second Edition (SRS-2), indicated significant improvements in social communication (p = 0.001) and social motivation (p = 0.045), while changes in social awareness (p = 0.233) and repetitive behaviors (p = 0.169) were not statistically significant. However, an ANCOVA analysis controlling for pre-test scores found that the difference in post-test outcomes between the intervention and control groups did not reach statistical significance (p = 0.073), suggesting that observed improvements may be influenced by baseline variability. Qualitative feedback highlighted the program’s engagement value, cultural relevance, and usability, alongside suggestions for increased adaptivity and contextual nuance. These results underscore the potential of VR-based learning while highlighting the need for further research with larger samples and more adaptive systems. These findings offer practical insights for educators, researchers, teachers, and program developers by demonstrating how culturally grounded, gamified VR interventions when guided by experiential learning principles can effectively enhance engagement and support targeted social skill development in children with ASD.

This study examines the effects of gamified and non-gamified digital board games on students’ digital literacy and engagement. A total of 98 undergraduate students (n = 98) were randomly assigned to one of two conditions: gamified or non-gamified. The digital board game, designed to simulate real-world digital literacy scenarios, was implemented in a classroom setting. Students’ digital literacy performance was assessed through pre- and post-tests, and their engagement was measured using the Game Engagement Questionnaire. The results revealed that students in the gamified condition significantly outperformed those in the non-gamified condition in digital literacy post-test scores (p = 0.039). Additionally, the gamified group showed significantly higher engagement scores in flow (p = 0.039), enjoyment (p = 0.033), immersion (p = 0.042), and social interaction (p = 0.030). These findings highlight the effectiveness of gamified learning environments in enhancing digital literacy skills and multidimensional engagement.

This study explores the design of Metaverse technologies for preserving and teaching Lanna Dance, a traditional cultural heritage of Northern Thailand. It addresses the challenges of sustaining intangible cultural heritage by developing an immersive learning system that integrates motion capture, generative AI, and gamified virtual environments. Grounded in Situated Learning Theory and adaptive learning, the platform features four interactive zones, the Motion Showcase, Knowledge Exhibition, Video and AI Interaction, and Interactive Game Zone, offering learners multifaceted, context-rich experiences. Using a quasi-experimental design with 36 participants, the study evaluates learning outcomes, motivation, and user satisfaction. Results show significant improvements in knowledge acquisition and intrinsic motivation, along with high usability scores, indicating the effectiveness of immersive digital environments in enhancing cultural appreciation and skill development. The findings offer practical insights into Metaverse design for immersive cultural education, supporting educators, cultural institutions, and policymakers in developing scalable and engaging solutions for preserving intangible heritage through emerging technologies.

Manual data entry in dialysis centers is time-consuming, error-prone, and increases the administrative burden on healthcare professionals. Traditional optical character recognition (OCR) systems partially automate this process but lack the ability to handle complex data anomalies and ensure reliable clinical documentation. This study presents the design and evaluation of an AI-enhanced OCR system that integrates advanced image processing, rule-based validation, and large language model-driven anomaly detection to improve data accuracy, workflow efficiency, and user experience. A total of 65 laboratory reports, each containing approximately 35 fields, were processed and compared under two configurations: a basic OCR system and the AI-enhanced OCR system. System performance was evaluated using three key metrics: error detection accuracy across three error categories (Missing Values, Out-of-Range, and Typo/Free-text), workflow efficiency measured by average processing time per record and total completion time, and user acceptance measured using the System Usability Scale (SUS). The AI-enhanced OCR system outperformed the basic OCR system in all metrics, particularly in detecting and correcting Out-of-Range errors, such as decimal placement issues, achieving near-perfect precision and recall. It reduced the average processing time per record by almost 50% (85.2 to 42.1 s) and improved usability, scoring 81.0 (Excellent) compared to 75.0 (Good). These results demonstrate the potential of AI-driven OCR to reduce clerical workload, improve healthcare data quality, and streamline clinical workflows, while maintaining a human-in-the-loop verification process to ensure patient safety and data integrity.

This study introduces a Generative AI-Enhanced Robotic Data Automation (AI-ERDA) framework designed to improve accuracy, efficiency, and adaptability in healthcare data workflows. Conducted over a two-month, real-world experiment across three government health platforms—one web-based (NHSO) and two PC-based systems (CHi and TRT)—the study compared the performance of AI-ERDA against a conventional RDA system in terms of usability, automation accuracy, and resilience to user interface (UI) changes. Results demonstrated notable improvements in both usability and reliability. The AI-ERDA achieved a mean System Usability Scale (SUS) score of 80, compared with 68 for the traditional RDA, while Field Exact Match Accuracy increased by 1.8 percent in the web system and by 0.2 to 0.3 percent in the PC systems. During actual UI modifications, the AI-ERDA maintained near-perfect accuracy, with rapid self-correction within one day, whereas the baseline RDA required several days of manual reconfiguration and assistance from the development team to resolve issues. These findings indicate that generative and adaptive automation can effectively reduce manual workload, minimize downtime, and maintain high data integrity across heterogeneous systems. By integrating adaptive learning, semantic validation, and human-in-the-loop oversight, the AI-ERDA framework advances sustainable digital transformation and reinforces transparency, trust, and accountability in healthcare data management.

Virtual museums delivered through immersive virtual reality (VR) function as information environments where users access interpretive content while navigating spatially. With the integration of generative artificial intelligence (AI), conversational assistants can dynamically mediate information interaction; however, evidence remains limited regarding how different AI interface representations affect user experience. This study compares three generative AI interface modalities in a VR virtual museum: voice only, voice with synchronized text, and voice with an embodied AI avatar. A controlled experiment with 75 participants examined their effects on user engagement, perceived information quality, and subjective cognitive workload while holding informational content constant. The results indicate that the voice-and-text modality produced the highest perceived information quality, whereas the embodied AI avatar modality yielded the highest user engagement. No significant differences were observed in cognitive workload across modalities. These findings suggest that AI interface modalities play complementary roles in VR-based information interaction and provide design guidance for selecting appropriate AI representations in immersive information systems.