The rapid integration of artificial intelligence (AI) tools into academic research and writing has introduced new challenges related to transparency, accountability, and reproducibility. While publishers increasingly permit AI use with disclosure, there is currently no standardized reporting framework guiding how such use should be documented. This study introduces PETMALU-AI (Principles for Ensuring Transparency in Machine-assisted Authorship, Logging, and Use of Artificial Intelligence), a reporting checklist designed to support more consistent disclosure and documentation of AI-assisted research practices. The development of PETMALU-AI followed a multi-phase design framework, including (1) a narrative synthesis of existing AI policies and reporting guidelines, (2) item generation grounded in methodological transparency principles, and (3) modified Delphi-based expert validation involving an interdisciplinary panel. The Delphi process produced a 33-item checklist organized across nine domains: disclosure, human accountability, system transparency, prompting and interaction processes, data provenance, validation, ethics, limitations, and reproducibility. All retained items achieved expert consensus, with item-level content validity indices ranging from 0.85 to 1.00 and final agreement rates ranging from 85% to 100%. Reliability analyses further supported the stability of expert judgments, with Fleiss’ kappa and the intraclass correlation coefficient indicating strong agreement and high consistency. The checklist emphasizes proportional reporting, human accountability, and transparent documentation of AI-assisted research practices. PETMALU-AI offers a framework for documenting AI involvement in ways that are visible, assessable, and accountable, providing a foundation for more transparent research reporting as scholarly writing becomes increasingly shaped by human–AI collaboration.

Technical and Vocational Education and Training (TVET) programs require reliable methods to teach and assess performance-based skills in authentic contexts. Immersive platforms such as the metaverse offer promising environments for simulating real-world tasks and providing scalable training opportunities. In these technology-rich settings, stealth assessment is recognized as a psychometrically sound and theoretically grounded approach. However, its application has been largely confined to video games and primarily in contexts emphasizing creativity and collaboration. This study broadens that scope by embedding stealth assessment within a metaverse environment to develop and evaluate technical competencies in PC building. Guided by evidence-centered design, a two-part investigation examined (1) the suitability of stealth assessment in a metaverse context and (2) the effectiveness of metaverse-based training in improving learning outcomes and facilitating skill transfer. Undergraduate students from computing and engineering programs engaged in structured PC-building scenarios within the metaverse, where their performance was tracked through automated event logs, validated against expert evaluations, and analyzed using inferential statistical methods. Results indicate that embedding stealth assessment in the metaverse enabled reliable and unobtrusive evaluation of learners’ technical competencies. Metaverse-based PC-building training yielded higher learning gains than traditional methods, and learners demonstrated the ability to transfer skills from virtual tasks to physical PC-building. Overall, these findings offer evidence-informed insights for educators, researchers, and policymakers on integrating immersive technologies and embedded assessments in technical education and vocational training.

Many educational institutes adopt immersive learning environments (ILEs) for their positive contribution to learning outcomes. However, challenges reflecting a wide range of serious ethical issues and concerns also evoke a degree of apprehension in its integration by educators and researchers. This study identifies the ethical issues and risks associated with integrating Metaverse technologies into educational settings. It also focuses on the voices of Metaverse experts with the idea of a safe and educationally immersive environment. A qualitative approach was used in which in-depth interviews of the experts on ethical issues were conducted. According to the experts’ perspectives, key ethical issues and risks are highlighted, such as data privacy, security, policies, hyperreality, loss of social skills, and negligence of physical health. The findings also emphasize the importance of the Metaverse-related considerations, including design, policies, universities’ roles, and safety concerns for equality, diversity, and inclusion (EDI). Based on the findings, we recommend enhancing the Metaverse design environments by integrating ethical frameworks and policies to ensure safe and inclusive environments for teachers and students.

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.