Conference Agenda

Abstract - This paper introduces the WHWW Inquiry Framework, a pedagogically grounded model designed to strengthen inquiry skills in learning environments increasingly shaped by Generative Artificial Intelligence (GenAI). Derived systematically from the Data–Information–Knowledge–Wisdom (DIKW) hierarchy, the framework operationalises cognitive progression through four structured stages of inquiry: What, How, Why, and What-if. Unlike the conventional prompt-engineering techniques that focus primarily on optimising AI output, the WHWW framework emphasises learner cognition, providing a theory-informed method for guiding deep questioning, critical evaluation, and in-depth inquiry-driven reasoning. To assess its effectiveness, a structured GenAI-supported workshop was conducted, during which participants applied the WHWW sequence to explore technical topics and interrogate AI-generated responses. Survey results indicate that the framework significantly enhanced learners’ ability to formulate deeper questions, strengthened their understanding of the DIKW progression, and improved their critical engagement with AI outputs. These findings demonstrate the value of WHWW as a scalable pedagogical scaffold for integrating GenAI into inquiry-based learning while maintaining cognitive rigour and supporting meaningful learning progression.

Background: The integration of immersive technologies such as virtual reality (VR) into nursing education has gained increasing attention due to their potential to enhance experiential learning, clinical skills acquisition, and student engagement. However, empirical evidence regarding their integration in nursing courses remains limited.

Objective: To describe and evaluate the implementation of a VR-based educational intervention in the Nursing Methodological Foundations and Procedures course, assessing perceived usefulness, impact on the teaching–learning process, and overall satisfaction.

Methods: A descriptive cross-sectional study was conducted. Undergraduate nursing students enrolled during the 2024/2025 and 2025/2026 academic years participated in a VR-based practical activity using immersive head-mounted displays.

Results: A total of 167 students participated. The overall satisfaction with the VR experience was high (mean score: 8.89/10). Students positively rated the usefulness of VR for understanding course content (M = 4.09/5), consolidating learning (M = 4.44), and increasing interest and enjoyment (M = 4.65/5 and 4.62/5, respectively). The perceived difficulty of using VR technology was low (M = 2.38/5), and 92.8% of participants recommended its continued use without changes. Qualitative feedback highlighted the value of extending VR to other nursing courses.

Conclusions: The integration of VR was associated with high student satisfaction, perceived usefulness, and ease of use. VR appears to function as an effective complementary tool for consolidating anatomical knowledge and supporting experiential learning in nursing education. Future research should explore longitudinal outcomes, skill transfer to clinical practice, and comparative designs.

Even though the rapid embrace of Artificial Intelligence (AI) is transforming the higher education landscape, it is also causing major systemic problems for the institutions found in the Global South. The existing models of AI discourse and implementation are predominantly Anglophone and China-based and fail to account for the realities of socioeconomic, infrastructural, and pedagogical resources in resource-limited settings. Consequently, AI applications in these fields tend to focus more on administrative than pedagogical transformation, and there is a risk of rising educational inequity and digital inequality. The proposed paper proposes a mixed-methods study to investigate the multifaceted effects of AI on the quality of learning and teaching in under-resourced environments. To conduct the study, the Technology Acceptance Model (TAM) will serve as the quantitative survey tool, and qualitative interviews with stakeholders will be conducted to evaluate institutional readiness for telehealth, identify structural obstacles, and discuss ethical considerations. The resulting empirical data are supposed to inform the planning of context-specific and generalizable policy frameworks and sustainable governance models. Ultimately, the aim of this research is to ensure equitable access to generative AI to reduce high student-teacher ratios, provide one-on-one learning, and even eliminate the global digital divide in education. This research will also employ a two-pronged approach. The current paper covers the literature review and the research methodology and design, which will later serve as a base for building and implementing Phase 2 of the research.