This Work-in-Progress Research paper explores how to design and implement Collaborative Virtual Reality Learning Environments (CVRLE) taking Teacher-Student Dynamics (TSD) into account. CVRLEs engage students in a virtual representation of a learning space. However, they do not incorporate TSDs, which are key elements of in-person teaching. The current work explores how to recreate one specific TSD: the watchfulness teachers have over the students' location during a field trip. We propose and compare seamless approaches to recreate this TSD inside CVRLEs via virtual safety boundaries around the teacher. These boundaries keep students in proximity to their instructor as they explore the CVRLE. The current work explores five approaches: 1) an invisible wall (used as baseline), 2) sound-based feedback, 3) a virtual companion, 4) a translucent rope connected to the teacher and 5) a translucent dome mesh around the teacher. The approaches were validated by five middle school teachers, who provided feedback about them from an educator's point of view. The interviews highlighted the importance of subtle and simple approaches that provide a sense of freedom to the students and minimize cognitive load while maintaining the TSDs. Overall, the vision of this Work-in-Progress is to solidify CVRLEs into a plausible method to perform teaching and learning when co-presence between teacher and students cannot be guaranteed. The design guidelines from this project will inform the creation of future interactive CVRLEs.

The integration of educational video games (EVGs) in learning environments has garnered significant attention in recent years. However, understanding the perceptions of various stakeholders is crucial for successful implementation. This systematic review provides a comprehensive analysis of stakeholder perceptions regarding the use of educational video games (EVGs) in learning environments. In this study, we examined the perspectives of administrators, teachers, students, and parents associated with EVGs. We employed a rigorous systematic literature review methodology to identify relevant literature. Four major electronic databases—Education Source, Academic Search Ultimate, APA PsycInfo, and ERIC—were systematically searched using specific keywords related to EVGs and stakeholder perceptions. A total of 1,212 articles were initially identified through the database search. Following a systematic screening process based on predetermined inclusion criteria, 344 studies were selected for full-text assessment. These studies provided a robust evidence base for the analysis of stakeholder perceptions. Findings from the systematic review indicate that stakeholders generally hold positive perceptions of EVGs. Stakeholders recognize the potential of EVGs to enhance student engagement and motivation in the learning process. The optimal balance between game difficulty and skill emerges as a critical factor for promoting effective learning experiences. Students, particularly at the elementary and high school levels, respond positively to EVGs, expressing interest and enthusiasm for their use in the classroom. Administrators play a crucial role in the implementation of EVGs and face challenges related to resource allocation and teacher training. Concerns about the cost of EVGs, training requirements, and budget constraints often influence their decision-making process. Teachers share similar concerns regarding time constraints and the need for adequate training to effectively integrate EVGs into their teaching practices. They also emphasize the importance of appropriate assessment strategies within EVGs to evaluate student performance accurately. Parental perceptions vary, with some expressing concerns about potential negative effects of video games, such as inappropriate content and addiction risks. However, positive parental views emerge when they perceive EVGs as contributing to their children's learning outcomes, especially in areas such as soft skills and physical learning. The systematic review highlights the need to address stakeholder concerns to facilitate the successful integration of EVGs in educational settings. Recommendations include the development of EVGs that align with educational objectives, the provision of targeted professional development opportunities for teachers, clear communication with parents regarding content moderation and safety measures, and collaborative partnerships with administrators to address resource allocation challenges. By synthesizing and analyzing stakeholder perspectives, this systematic review provides valuable insights for educators, policymakers, and game developers involved in the design and implementation of EVGs in educational contexts.

We explore the usage of generative artificial intelligence (GAI) for image synthesis and design as learning tools. The emergence of GAI is expected to change the ways architects, designers, and students work by reducing their design cycle, lightening their design load, and providing them with richer and more diverse design ideas. We foresee that in the near future, the field of design work will become increasingly integrated with AI technologies. In this study, we trained a GAI model for image synthesis in a design task and evaluated its effectiveness in education. An experimental setup is scheduled for the coming months to evaluate our design in the context of diverse STEM major students.

To determine a molecular basis for prognostic differences in glioblastoma multiforme (GBM), we employed a combinatorial network analysis framework to exhaustively search for molecular patterns in protein-protein interaction (PPI) networks. We identified a dysregulated molecular signature distinguishing short-term (survival<225 days) from long-term (survival>635 days) survivors of GBM using whole genome expression data from The Cancer Genome Atlas (TCGA). A 50-gene subnetwork signature achieved 80% prediction accuracy when tested against an independent gene expression dataset. Functional annotations for the subnetwork signature included "protein kinase cascade," "IκB kinase/NFκB cascade," and "regulation of programmed cell death" - all of which were not significant in signatures of existing subtypes. Finally, we used label-free proteomics to examine how our subnetwork signature predicted protein level expression differences in an independent GBM cohort of 16 patients. We found that the genes discovered using network biology had a higher probability of dysregulated protein expression than either genes exhibiting individual differential expression or genes derived from known GBM subtypes. In particular, the long-term survivor subtype was characterized by increased protein expression of DNM1 and MAPK1 and decreased expression of HSPA9, PSMD3, and CANX. Overall, we demonstrate that the combinatorial analysis of gene expression data constrained by PPIs outlines an approach for the discovery of robust and translatable molecular signatures in GBM.