As GenAI technologies become more pervasive in higher education (HE), scholars call for guidance on AI governance. To meet this need, a Delphi technique and collective writing was used in gathering expert perspectives from across 22 countries/locations and six continents. This resulted in the development of a HE GenAI policy/guidelines framework with eight core areas: (1) academic integrity, (2) ethical use and responsible use, (3) privacy and protection, (4) equitable access, (5) GenAI literacy, (6) integration strategy, (7) human oversight and accountability, and (8) institutional support and infrastructure. In addition, a six-part framework was developed to ensure that policies remain current and relevant: (1) creating a dedicated GenAI Committee, (2) conducting regularly scheduled policy reviews, (3) providing ongoing professional development and support, (4) communicating with all stakeholders, (5) evaluating the effectiveness and impact of GenAI, and 6) monitoring external developments. By providing a robust, eight-part framework for policy and guidelines, alongside a six-part mechanism for continued review, this study offers faculty, students, administrators, educational leaders, policymakers, and funders a responsible, adaptable, and consensus-driven blueprint for navigating the integration of GenAI in HE, ensuring that technological innovation serves pedagogical excellence.

The integration of artificial intelligence (AI) tools into educational settings is reshaping how students learn, create, and engage with digital technologies, particularly in fields such as technology and design education where virtual simulations and AI-assisted workflows are increasingly prevalent. Despite this momentum, student adoption of AI tools remains inconsistent, especially in developing regions where technological readiness and trust in emerging technologies vary widely. This study investigates factors influencing AI tool acceptance among 493 industrial technology students from a public university in Central Visayas, Philippines. Using structural equation modeling (SEM), the research examined the relationships among perceived usefulness, perceived ease of use, perceived risk, and behavioral intention to adopt AI-driven virtual simulation applications. Findings confirm that perceived usefulness and ease of use remain strong drivers of adoption intention, aligning with the Technology Acceptance Model (TAM). However, perceived risk demonstrated a significant negative influence on both perceptions and intentions, highlighting the impact of concerns related to data privacy, algorithmic transparency, and ethical implications of AI in design-oriented learning environments. Integrating perceived risk into TAM expands the explanatory power of the model in technical drafting and design education, offering a more comprehensive picture of how students evaluate and adopt AI-driven virtual simulation apps. This enriched perspective highlights the necessity for institutional strategies that build AI literacy, reinforce data governance mechanisms, and foster responsible engagement with emerging technologies. The results provide valuable insights for educators, curriculum designers, and policymakers working to advance AI-supported learning, particularly within resource-constrained and rapidly developing educational contexts.

As artificial intelligence (AI) systems assume greater responsibility in educational assessment, questions surrounding fairness, transparency, and trust have become central to their ethical and pedagogical legitimacy. Yet, little empirical work has examined how specific design features shape students’ trust in AI-driven assessment, particularly in contexts where algorithmic decisions carry meaningful academic consequences. This study examines how transparency, ethical framing, and user agency influence students’ trust in an AI-based assessment platform. Using a 2 × 2 × 2 between-subjects experimental design with 240 undergraduate participants, the study isolates the main and interaction effects of these variables on trust, perceived fairness, perceived control, and adoption intention. Findings indicate that transparency is the most influential predictor of trust, while user agency functions as a compensatory mechanism in low-transparency conditions. Ethical framing, although theoretically salient, showed limited impact once users interacted with the system directly and shifted their attention toward the more concrete procedural cues embedded in the interface. A significant interaction between transparency and agency underscores the importance of aligning epistemic clarity with procedural control to foster behavioral commitment. These results support a multidimensional model of trust that incorporates emotional security, procedural justice, and behavioral intent. Overall, the study underscores that trust in AI assessment is not a byproduct of system accuracy alone but a reflection of students’ perceived legitimacy of the evaluative process.

The rapid emergence of generative artificial intelligence (GenAI) has introduced both opportunities and challenges for education systems worldwide. Educational stakeholders are grappling with fundamental questions of how to guide students on whether and when, and in what ways, they should use GenAI. In this study, a framework was developed to guide K–12 policies and guidelines on the use of GenAI. Using the Delphi technique and collective writing, expert perspectives were gathered from participants across 20 countries and six continents. The analysis identified eight key topic areas for K–12 GenAI policy and guideline development: (1) data privacy and security, (2) ethical and responsible use, (3) equitable access, (4) academic integrity, (5) human oversight, (6) GenAI literacy, (7) curriculum integration, and (8) governance and review. A complementary six-part framework was also constructed to support policy relevance and currency through multi-stakeholder governance, continuous review, ongoing training, awareness of external developments, outcome monitoring, and transparent communication. Together, these frameworks advance the scholarly and practical understanding of how GenAI policies can be designed and maintained in schools.

The metaverse and artificial intelligence (AI) are increasingly intersecting in educational contexts, yet limited empirical research has examined how generative AI avatars function within socially interactive virtual environments. This study investigates the deployment of generative AI avatars within a socio-educational metaverse environment. Using a developmental evaluation approach, data were collected through interviews with seven institutional stakeholders, teacher-generated reflections, internal documentation, embedded user feedback captured through in-platform reporting tools, and longitudinal field memos across an iterative deployment cycle. Findings indicate that the transition from scripted NPCs to generative AI avatars recalibrated users’ attribution of agency, intensified dialogic unpredictability, and elevated social realism beyond visual fidelity. Voice-mediated interaction emerged as a threshold mechanism for co-presence, while algorithmic improvisation exposed tensions between pedagogical intent and stochastic response generation. The deployment further revealed affective frictions, expectation misalignments, and the mediating role of AI literacy in shaping trust, participation, and interpretive coherence. Overall, the study advances a sociotechnical understanding of AI avatars as co-constructors of meaning and interaction, offering implications for the design, implementation, and governance of future AI-enhanced metaverse learning environments.