The RaceVision project, an MSc in Computer Science team initiative, provided students with a multidisciplinary learning experience, covering data science, software development, UX/UI design, machine learning, and gamification. The goal of the project was to create a progressive web app (PWA) that enables Formula 1 fans and journalists to explore detailed race data, analyze driver and team statistics, visualize race performance, and make predictions using AI-driven insights. The key learning outcomes from the development of RaceVision includes demonstrating how hands-on projects bridge the gap between academia and industry by equipping students with practical skills, problem-solving capabilities, and real-world experience in data-driven software development.

The development of RaceVision enabled students to gain full-stack development experience, working with front-end, back-end, and cloud-based technologies. Students learned to work with large-scale, multi-source datasets, applying:
- Data cleaning & preprocessing techniques to structure race data from Ergast API & Kaggle datasets.
- Time-series data analysis for race performance predictions.
- Feature extraction & engineering to create predictive models.

This experience helped students develop critical thinking skills in handling incomplete, noisy, and complex data.

Students were encouraged to evaluate the ethical implications of data-driven sports analytics, particularly focusing on bias in machine learning models and data privacy. They identified potential biases in AI predictions, such as driver and team favoritism due to historical dominance and inconsistent race conditions, including weather and technical issues, that could impact prediction accuracy. This experience deepened their understanding of bias mitigation techniques in machine learning. Additionally, students prioritized responsible data handling by implementing privacy measures, ensuring data transparency, and designing user controls that allow individuals to manage their data preferences securely. Through these challenges, they gained valuable insights into the ethical considerations necessary for responsible AI development in sports analytics.

The RaceVision project provided students with a comprehensive learning experience, developing technical expertise, UX skills, AI knowledge, and project management abilities. By working on a real-world, interdisciplinary project, students bridged the gap between theoretical knowledge and industry applications. This case study demonstrates the value of hands-on, data-driven projects in computing education, emphasizing the importance of AI, gamification, and user-centered design in modern software development.

Keywords: e-learning, Formula 1, Teamwork.