ColorShapeLinks: A board game AI competition for educators and students
Project Overview
The document explores the integration of generative AI in education through the ColorShapeLinks framework, an innovative AI competition designed for educators and students involved in video game development. This framework promotes accessibility and openness, allowing students to engage with AI concepts in a hands-on manner via board game competitions. It has been effectively implemented in both internal and international competitions, significantly motivating students and enriching their learning experiences in AI for Games courses. The outcomes of utilizing this framework have been notably positive, as it fosters greater engagement and motivation among students, thereby enhancing their understanding of AI principles and applications within the context of game development. Overall, the document highlights the potential of generative AI to transform educational practices by making learning more interactive and enjoyable.
Key Applications
ColorShapeLinks AI competition framework
Context: Educational context for students in Videogame Development courses, specifically in AI for Games.
Implementation: Implemented as an assignment in the AI for Games course at Lusofona University, utilizing Unity and .NET for development.
Outcomes: Increased student engagement, motivation, and performance in AI concepts; successful participation in internal and international competitions.
Challenges: Initial lack of complexity in the game could lead to rapid obsolescence as solutions are developed; subjective assessment of student motivation without formal surveys.
Implementation Barriers
Engagement barrier
Students from non-CS backgrounds may struggle to engage with AI concepts.
Proposed Solutions: Implementing board game competitions to motivate students and make AI more approachable.
Complexity barrier
The underlying game (Simplexity) is relatively simple, which could lead to quick solutions and diminish the challenge.
Proposed Solutions: Continually update the framework to introduce new parameters and challenges to keep it relevant.
Project Team
Nuno Fachada
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Nuno Fachada
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai