Reference 1
Glickman, M. E. (1999). Parameter estimation in large dynamic paired comparison experiments. Journal of the Royal Statistical Society: Series C, 48(3), 377–394.
A foundational text explaining performance evaluation in competitive systems, useful for understanding scoring and ranking logic relevant to AI tournaments.

Guide to Chess Tournament Formats
https://help.chessbase.com/playchess/7/eng/index.html?tournament_types.htm
This overview of the most common tournament structures, including Swiss, Round-Robin, and elimination systems.

Reference 2
Nguyen, T., Shen, Z., & Nahrstedt, K. (2019). A Survey of API Design Practices in Modern Systems. ACM Computing Surveys, 52(3), 1–36.
Provides insight into how APIs are structured, used, and secured—supporting students’ understanding of communication protocols in AI competitions.

Understanding RESTful APIs
https://www.smashingmagazine.com/2018/01/understanding-using-rest-api/
A beginner-friendly explanation of how APIs work, including requests, responses, endpoints, and data formats. It helps students understand the principles behind competition communication systems.

Reference 3
Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.).
A foundational textbook covering agent behavior, decision-making, and system orchestration—concepts crucial for understanding event management in AI competitions.

Lichess Tournament System Explained
https://lichess.org/tournament/help
This resource provides an accessible explanation of how live online chess tournaments are structured, automated, and monitored. It helps students relate classroom competitions to real-world systems.

Reference 4
Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
Offers an accessible exploration of AI ethics, responsibility, and fairness—relevant background for understanding ethical conduct in AI competitions.

ACM Code of Ethics and Professional Conduct
https://www.acm.org/code-of-ethics
This resource outlines ethical standards for computing professionals. It provides a practical foundation for teaching responsible AI behavior and fair-play expectations.

Reference 5
Dweck, C. (2006). Mindset: The New Psychology of Success. Random House.
A foundational work on growth mindset, providing teachers with insights into helping students embrace challenges and learn from setbacks.

Teaching Teamwork in Computer Science
https://csteachingtips.org/tips/teach-teamwork-explicitly
A practical collection of teaching strategies that help educators guide students in effective teamwork, communication, and collaboration during technical projects such as AI competitions.

Reference 6
Stuart Russell & Peter Norvig – Artificial Intelligence: A Modern Approach
It is one of the most recognised references in AI. It explains fundamental concepts such as tree search, adversarial algorithms (Minimax), heuristics, and decision making — essential for understanding how chess AIs work, even if the reader does not program directly

Reference 7
Jonathan Schaeffer – One Jump Ahead: Challenging Human Supremacy in Checkers
Practical report on the development of a competitive AI system. Helps to understand the real challenges of competitions between humans and machines, performance management, and algorithmic strategy.

Reference 8
Garry Kasparov – Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins
Strategic and ethical perspective on AI in chess, including the experience against Deep Blue. Highly relevant for pedagogical reflection and discussion on human-machine collaboration.

Reference 9
Lichess API Documentation
Practical documentation for integrating bots and using APIs in a real environment. Useful for technical organisers and for understanding how automated tournament platforms work.

Chess Programming Wiki
Specialised technical resource on chess engines, position evaluation, algorithms, and optimisation. Most suitable for organisers or participants with in-depth technical interest.