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100% fundable

AI+ Game Design Practitioner™

Game development is changing fundamentally: AI tools take on complex tasks, speed up creative processes, and lay the groundwork for surprising interactive experiences. This course shows how intelligent systems can be put to targeted use.

Key topics

  • Developing and applying adaptive mechanics with AI
  • Generative methods for creating game content
  • Building efficient workflows for concept, prototype, and quality assurance
  • Using data-driven approaches for balanced gameplay
  • Overview of current AI trends around automated assets

Prerequisite
Familiarity with digital media or some exposure to game-related concepts is helpful – but no prior knowledge is required to get started.

Target group
From game design enthusiasts and media creators to the technically curious: anyone who wants to integrate AI purposefully into interactive projects will find practical approaches here.

AI and gaming thought together – for everyone who wants to take their creative projects to the next level with smart methods.

course content
  • What do we mean by artificial intelligence?
  • Historical development of AI in the gaming industry
  • Different types of AI and how they are used in games
  • Opportunities, limitations, and current innovations in game AI
  • Game mechanics and their impact on the player experience
  • The role of AI in gameplay and narrative design
  • Designing game environments for AI interaction
  • AI-driven systems compared to traditional script logic
  • Case study: Dynamic AI and narrative adaptation in "Middle-earth: Shadow of Mordor"
  • Practical exercise: Designing adaptive NPC behavior and interaction with the environment
  • Core AI concepts for use in games
  • Search algorithms and pathfinding methods at a glance
  • Behavior modeling and procedural content generation (PCG)
  • Introduction to machine learning and reinforcement learning
  • Case study: AI in Minecraft – procedural content generation and agent navigation
  • Practical exercise: Implementation of A* pathfinding and FSM for NPC behavior
  • Core concepts: states, actions, rewards, strategies, and Q-learning
  • Exploration and exploitation in learning systems
  • Overview of Deep Q Networks (DQN) and Policy Gradient Methods
  • Case study: Reinforcement learning in DeepMind's AlphaGo
  • Practical exercise: Training an RL model on OpenAI Gym's GridWorld
  • Minimax algorithm and alpha-beta pruning at a glance
  • Monte Carlo tree search (MCTS) for game-based decisions
  • Application examples from board games and real-time strategy games (RTS)
  • Case study: Strategic AI in StarCraft II
  • Practical implementation: Implementing the minimax algorithm for tic-tac-toe
  • Key differences between 2D and 3D game environments
  • Techniques for representing and modeling game worlds
  • Navigation and pathfinding in two- and three-dimensional spaces
  • Interaction and behavior systems in virtual environments
  • Case study: Navigation and interaction AI in "The Legend of Zelda: Breath of the Wild"
  • Practical exercise: Setting up basic navigation and interaction in 2D and 3D environments
  • Concepts and structure of adaptive systems in games
  • Principles of dynamic difficulty adaptation (DDA)
  • Adaptive storytelling, personalization, and player profiling
  • AI methods in adaptive system architectures
  • Implementation strategies and suitable tools
  • Case study: Dynamic enemy management and replayability with Left 4 Dead's AI Director
  • Practical exercise: Development of an adaptive system for dynamic difficulty in Unity
  • Generalist AI agents and transfer learning as the next evolutionary step
  • AI-supported tools for game design and quality assurance
  • Ethical considerations and transparency requirements in AI use
  • New technologies: VR/AR AI and AI in e-sports coaching
  • Independent implementation of an AI project in a gaming context

Up to 100% funding for your continuing education

Attractive funding opportunities are available for numerous continuing education programs – depending on the funding program, up to 100% funding is possible.
The requirement is that you live or work in Germany. Our funding opportunities are not available outside of Germany.

Funding opportunities are available for career starters, job seekers, career changers, startups, experienced professionals, those seeking career advancement, and specialists.