The ChessAIThon project (2025-1-ES01-KA220-VET-000354329) is co-funded by the European Union. The views and opinions expressed in this publication are those of the author(s) only and do not necessarily reflect those of the European Union or the Spanish Service for the Internationalisation of Education (SEPIE). Neither the European Union nor the National Agency SEPIE can be held responsible for them.

Chess data: formats structures and dataset files, control version

Home

Table of Contents

Use Chessboard2 or python Chess to represent boards

To make the abstract concepts of FEN strings and the numerical arrays tangible, guide students to use Python libraries like Python Chess and potentially a visualization tool like Chessboard2 (if available or a similar visual library).

Explain that while the AI processes data numerically, humans need to see the result. Libraries like Python Chess can take a raw FEN string and render a textual ASCII-based chessboard, instantly allowing students to verify their manual FEN conversions. This offers immediate visual feedback, reinforcing the accuracy of their parsing algorithms.

Visualizing the AI's Perspective

For a richer experience, demonstrate how to use a visualization library to take the abstract matrix board representation—a complex, multi-layered NumPy array—and convert it back into a standard, graphical chessboard. This reverse engineering of the AI's data shows the students:

  • Translation of Tensors: How a dense numerical tensor is translated back into a comprehensible physical model.
  • Practical Value of Libraries: They handle the complex task of drawing the board, letting students focus on the critical data transformation logic.

This step connects the highly technical data formats to the familiar chess game, solidifying their understanding of the entire data pipeline.