Santiago Berrezueta-Guzman

Esin Mehmedova, Santiago Berrezueta-Guzman, Stefan Wagner

Designing effective user interfaces (UIs) for virtual reality (VR) is essential to enhance user immersion, usability, comfort, and accessibility in virtual environments. Despite the growing adoption of VR across domains, there is a noticeable lack of unified and comprehensive design guidelines for VR UI design. To address this gap, we conducted a systematic literature review to identify existing best practices and propose 28 unified guidelines for UI development in VR. Building on these insights, this research proposes a framework to guide the creation of more effective VR interfaces. To demonstrate and validate these practices, we developed a VR application called FlUId and an interactive Web Tool that serves as a guideline explorer and project planning resource for developers. A user study was conducted to evaluate the impact of the proposed guidelines. The findings aim to bridge the gap between theory and practice, offering concrete recommendations and digital tools for VR designers and developers.

Santiago Berrezueta-Guzman, Ivan Parmacli, Stephan Krusche et al.

Enhancing interaction and feedback collection in a first-semester computer science course poses a significant challenge due to students' diverse needs and engagement levels. To address this issue, we created and integrated a command-based chatbot on the course communication server on Discord. The DiscordBot enables students to provide feedback on course activities through short surveys, such as exercises, quizzes, and lectures, facilitating stress-free communication with instructors. It also supports attendance tracking and introduces lectures before they start. The research demonstrates the effectiveness of the DiscordBot as a communication tool. The ongoing feedback allowed course instructors to dynamically adjust and improve the difficulty level of upcoming activities and promote discussion in subsequent tutor sessions. The data collected reveal that students can accurately perceive the activities' difficulty and expected results, providing insights not possible through traditional end-of-semester surveys. Students reported that interaction with the DiscordBot was easy and expressed a desire to continue using it in future semesters. This responsive approach ensures the course meets the evolving needs of students, thereby enhancing their overall learning experience.

Süeda Özkaya, Santiago Berrezueta-Guzman, Stefan Wagner

The integration of Large Language Models (LLMs) into Virtual Reality (VR) games marks a paradigm shift in the design of immersive, adaptive, and intelligent digital experiences. This paper presents a comprehensive review of recent research at the intersection of LLMs and VR, examining how these models are transforming narrative generation, non-player character (NPC) interactions, accessibility, personalization, and game mastering. Drawing from an analysis of 62 peer reviewed studies published between 2018 and 2025, we identify key application domains ranging from emotionally intelligent NPCs and procedurally generated storytelling to AI-driven adaptive systems and inclusive gameplay interfaces. We also address the major challenges facing this convergence, including real-time performance constraints, memory limitations, ethical risks, and scalability barriers. Our findings highlight that while LLMs significantly enhance realism, creativity, and user engagement in VR environments, their effective deployment requires robust design strategies that integrate multimodal interaction, hybrid AI architectures, and ethical safeguards. The paper concludes by outlining future research directions in multimodal AI, affective computing, reinforcement learning, and open-source development, aiming to guide the responsible advancement of intelligent and inclusive VR systems.

Santiago Berrezueta-Guzman, Vanesa Metaj, Stefan Wagner

As the landscape of software engineering evolves, introductory programming courses must go beyond teaching syntax to foster comprehensive technical competencies and professional soft skills. This paper reports on a pedagogical experience in a "Fundamentals of Programming" course that used a Project-Based Learning (PBL) framework to develop a 2D "Maze Runner"-style game. While game development serves as a high-engagement vehicle for mastering core concepts, such as multidimensional arrays, control structures, and logic, the core of this study focuses on implementing a rigorous, multifaceted assessment model structured across four distinct dimensions: (1) an in-situ technical demonstration, evaluating real-time code execution and algorithmic robustness; (2) a technical screencast, requiring students to articulate their work in a concise audiovisual format; (3) a formal presentation to instructors, defending their project's design patterns and problem-solving strategies; and (4) a structured peer-review process, where students evaluated their colleagues' projects. Our findings suggest that this multi-dimensional approach not only improves student retention of programming fundamentals but also significantly enhances communication skills and critical thinking. By integrating peer evaluation and multimedia documentation, the course successfully bridges the gap between basic coding and the collaborative requirements of modern software engineering. This paper details the curriculum design, the challenges of implementing diverse assessment pillars, and the measurable impact on student performance and engagement, providing a scalable roadmap for educators looking to modernize introductory computing curricula.

Santiago Berrezueta-Guzman, Vanesa Metaj, Stefan Wagner

Requirements elicitation is among the most communication-intensive activities in software engineering, yet it receives limited explicit treatment in undergraduate curricula. This paper presents a case study of an Introduction to Software Engineering course in which 20 student teams applied requirements elicitation practices to a Java-based 2D game they had built in a prior programming course, engaging 18 campus doctoral and postdoctoral researchers as authentic clients. Structured across four phases--preparation, client meeting, requirements elaboration, and a prototype sprint--the activity produced 203 elicited requirements, SRS documents with a mean quality score of $6.79 \pm 1.08$ out of 10, and prototype demonstrations scoring $7.21 \pm 1.15$. A pre/post self-assessment survey revealed statistically significant improvements across all eight measured soft-skill dimensions, with the largest gains in Stakeholder Empathy ($Δ= +1.33$) and Negotiation ($Δ= +1.13$). Thematic analysis of reflective reports identified four dominant learning themes, with the tension between client wishes and technical feasibility cited as the most professionally relevant experience. Our findings suggest that anchoring elicitation practice to a student-authored artifact lowers cognitive barriers while increasing authenticity, and that campus researchers serve as an accessible and effective proxy client for programs without established industry partnerships.

Santiago Berrezueta-Guzman, Stephan Krusche, Stefan Wagner

The rapid adoption of AI powered coding assistants like ChatGPT and other coding copilots is transforming programming education, raising questions about assessment practices, academic integrity, and skill development. As educators seek alternatives to traditional grading methods susceptible to AI enabled plagiarism, structured peer assessment could be a promising strategy. This paper presents an empirical study of a rubric based, anonymized peer review process implemented in a large introductory programming course. Students evaluated each other's final projects (2D game), and their assessments were compared to instructor grades using correlation, mean absolute error, and root mean square error (RMSE). Additionally, reflective surveys from 47 teams captured student perceptions of fairness, grading behavior, and preferences regarding grade aggregation. Results show that peer review can approximate instructor evaluation with moderate accuracy and foster student engagement, evaluative thinking, and interest in providing good feedback to their peers. We discuss these findings for designing scalable, trustworthy peer assessment systems to face the age of AI assisted coding.

Esra Mehmedova, Santiago Berrezueta-Guzman, Stefan Wagner

The Apple Vision Pro is equipped with accurate eye-tracking capabilities, yet the privacy restrictions on the device prevent direct access to continuous user gaze data. This study introduces iTrace, a novel application that overcomes these limitations through click-based gaze extraction techniques, including manual methods like a pinch gesture, and automatic approaches utilizing dwell control or a gaming controller. We developed a system with a client-server architecture that captures the gaze coordinates and transforms them into dynamic heatmaps for video and spatial eye tracking. The system can generate individual and averaged heatmaps, enabling analysis of personal and collective attention patterns. To demonstrate its effectiveness and evaluate the usability and performance, a study was conducted with two groups of 10 participants, each testing different clicking methods. The 8BitDo controller achieved higher average data collection rates at 14.22 clicks/s compared to 0.45 clicks/s with dwell control, enabling significantly denser heatmap visualizations. The resulting heatmaps reveal distinct attention patterns, including concentrated focus in lecture videos and broader scanning during problem-solving tasks. By allowing dynamic attention visualization while maintaining a high gaze precision of 91 %, iTrace demonstrates strong potential for a wide range of applications in educational content engagement, environmental design evaluation, marketing analysis, and clinical cognitive assessment. Despite the current gaze data restrictions on the Apple Vision Pro, we encourage developers to use iTrace only in research settings.

Santiago Berrezueta-Guzman, Mohanad Kandil, María-Luisa Martín-Ruiz et al.

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition characterized by inattention, hyperactivity, and impulsivity, which can significantly impact an individual's daily functioning and quality of life. Occupational therapy plays a crucial role in managing ADHD by fostering the development of skills needed for daily living and enhancing an individual's ability to participate fully in school, home, and social situations. Recent studies highlight the potential of integrating Large Language Models (LLMs) like ChatGPT and Socially Assistive Robots (SAR) to improve psychological treatments. This integration aims to overcome existing limitations in mental health therapy by providing tailored support and adapting to the unique needs of this sensitive group. However, there remains a significant gap in research exploring the combined use of these advanced technologies in ADHD therapy, suggesting an opportunity for novel therapeutic approaches. Thus, we integrated two advanced language models, ChatGPT-4 Turbo and Claude-3 Opus, into a robotic assistant to explore how well each model performs in robot-assisted interactions. Additionally, we have compared their performance in a simulated therapy scenario to gauge their effectiveness against a clinically validated customized model. The results of this study show that ChatGPT-4 Turbo excelled in performance and responsiveness, making it suitable for time-sensitive applications. Claude-3 Opus, on the other hand, showed strengths in understanding, coherence, and ethical considerations, prioritizing safe and engaging interactions. Both models demonstrated innovation and adaptability, but ChatGPT-4 Turbo offered greater ease of integration and broader language support. The selection between them hinges on the specific demands of ADHD therapy.