Immersive virtual reality (IVR) offers new opportunities in physics education by enabling experiential learning environments, and its integration into serious games facilitates conceptual reinforcement through active engagement and situated learning. This study presents the design, iterative refinement, and evaluation of an IVR serious game for teaching projectile motion, analyzing its impact on user experience and conceptual understanding through a user- centered design (UCD) approach. The serious game was developed through two playtesting sessions involving physics professors and undergraduate students, whose feedback guided improvements in content, mechanics, and usability. A pilot study was conducted with 17 students using the Meta Quest 2 headset, and the evaluation included the Virtual Reality Neuroscientific Questionnaire (VRNQ), a perceived motivation scale, and a conceptual pre- and post-test. The iterative UCD process enhanced gameplay, immersion, and instructional clarity. Students rated the experience positively in terms of usability and motivation, while learning gain analysis showed an average normalized gain of 0.68 (Hake), a large effect size (d = 2.83), and an increase in students reaching the conceptual competency threshold from 12 to 100%. These results support the potential of Parabolic Basketball VR as an immersive and pedagogically grounded educational tool, demonstrating how user-centered, iterative development can contribute to. measurable learning gains and positive user engagement, and highlighting its value as a complementary strategy for enhancing conceptual understanding in physics education.

As video games continue to evolve, understanding what drives player enjoyment remains a key challenge. Player reviews provide valuable insights, but their unstructured nature makes large-scale analysis difficult. This study applies generative AI and machine learning, leveraging Microsoft Phi-4 small language model (SLM) and Google Cloud, to quantify and analyze game reviews from Steam and Meta Quest stores. The approach converts qualitative feedback into structured data, enabling comprehensive evaluation of key game design elements, monetization models, and platform-specific trends. The findings reveal distinct patterns in player preferences across PC and VR games, highlighting factors that contribute to higher player enjoyment. By using Google Cloud for large-scale data storage and processing, this study establishes a scalable framework for game review analysis. The study’s insights offer actionable guidance for game developers, helping optimize game mechanics, pricing strategies, and player engagement.

Abstract Litter pollution remains a pressing environmental issue, motivating the search for automated monitoring methods that can scale effectively. The problem is heightened by the difficulty of detecting small and diverse litter objects across wide areas, prompting interest in Unmanned Aerial Vehicles (UAVs) and deep learning as viable solutions. We conducted a systematic literature review by initially using Google Scholar, and then iteratively expanding our search through bibliographic references to identify relevant studies and datasets. In this review, we: synthesize the applicability of nine publicly available litter datasets; compile and analyze computer vision integrations with Litter Management Systems, with a particular emphasis towards UAV-based solutions; and review relevant literature addressing this issue; among others. Our analysis includes four UAV-based datasets (BDW, UAVVaste, HAIDA, SODA) and five non-UAV datasets (TrashNet, TACO, MJU-Waste, PlastOPol, ZeroWaste), examining dataset characteristics, preprocessing techniques, model architectures, and evaluation metrics across studies. Our synthesis of the literature highlights the varied approaches different studies undertake, reflecting the complexity of the task and the absence of standardised protocols. We conclude by discussing priorities for future work, notably the need for more publicly available in-the-wild UAV-acquired datasets and the potential of newer model architectures to address current limitations in automated litter detection.