The metaverse, a rapidly evolving research area, is a virtual, interconnected universe that seamlessly integrates digital and physical realms, facilitating immersive experiences, social interaction, and economic activities across various fields, including computer science, business, and education. Despite its potential to transform current pedagogies and learning experiences through engaging, interactive, and fascinating educational techniques, research is limited in exploring its application, usability, and user experience, particularly for Thai undergraduate students in learning computer engineering courses. In response, we designed and developed an innovative metaverse-based learning system called the AIIS collaborative learning interface, tailored to European undergraduate students. While the original intent of AIIS was to cater to medical students, in this study, our objective was to assess its usability and applicability for computer science and engineering students, specifically focusing on the Asian demographic. After an initial pilot with European students in their local context, the research was extended to Thailand, where 21 Thai undergraduate students evaluated the platform’s usability. The observations from this study indicate that AIIS provides a dynamic, user-friendly learning environment. Nevertheless, the evaluation process unveiled some usability flaws that offer informative directions for future enhancements. Drawing from our observations, we formulated usability guidelines to guide designers and developers toward crafting more efficient metaverse systems, particularly those intended for educational use. Our results also underline the considerable promise that metaverse technologies hold for advancing higher education globally.

Human-AI collaboration is evolving from a tool-based perspective to a partnership model where AI systems complement and enhance human capabilities. Traditional approaches often limit AI to a supportive role, missing the potential for reciprocal relationships where both human and AI inputs contribute to shared goals. Although Human-Centered AI (HcAI) frameworks emphasize transparency, ethics, and user experience, they often lack mechanisms for genuine, dynamic collaboration. The "Human-AI Handshake Model" addresses this gap by introducing a bi-directional, adaptive framework with five key attributes: information exchange, mutual learning, validation, feedback, and mutual capability augmentation. These attributes foster balanced interaction, enabling AI to act as a responsive partner, evolving with users over time. Human enablers like user experience and trust, alongside AI enablers such as explainability and responsibility, facilitate this collaboration, while shared values of ethics and co-evolution ensure sustainable growth. Distinct from existing frameworks, this model is reflected in tools like GitHub Copilot and ChatGPT, which support bi-directional learning and transparency. Challenges remain, including maintaining ethical standards and ensuring effective user oversight. Future research will explore these challenges, aiming to create a truly collaborative human-AI partnership that leverages the strengths of both to achieve outcomes beyond what either could accomplish alone.