Opleiding: AI+ Game Design Practitioner™

Build real-world gaming projects using cutting-edge AI technologies.

Develop the skills to create next-generation gaming experiences by combining artificial intelligence with modern game design. Learn how to build adaptive storytelling systems, intelligent NPCs, and AI-driven gameplay that deliver immersive, data-enhanced player experiences. Gain hands-on experience through real-world projects involving character behavior modeling, predictive player analytics, and intelligent game mechanics that strengthen both creativity and technical expertise. Earn a globally recognized certification that validates your AI gaming skills and enhances your professional credibility. Prepare for exciting career opportunities in game development, AI simulation, virtual production, and interactive entertainment while staying ahead with the latest advances in generative AI, immersive simulations, and intelligent gameplay systems.

• 1.1 What is AI?
• 1.2 Evolution of AI in the Gaming Industry
• 1.3 Types of AI in Games
• 1.4 Benefits, Challenges, and Innovations in Game AI

• 2.1 Understanding Game Mechanics and Player Experience
• 2.2 Role of AI in Gameplay and Narrative Design
• 2.3 Designing Game Environments for AI Interaction
• 2.4 AI-Driven Behavior vs Traditional Scripted Logic
• 2.5 Case Study: Dynamic AI and Narrative Adaptation in Middle earth: Shadow of Mordor
• 2.6 Hands-On Exercise: Designing Adaptive NPC Behavior and Environment Interaction

• 3.1 Core AI Concepts for Gaming
• 3.2 Search Algorithms and Pathfinding
• 3.3 AI Behavior Modeling and Procedural Content Generation (PCG)
• 3.4 Introduction to Machine Learning and Reinforcement Learning
• 3.5 Case Study: AI in Minecraft — Procedural Content Generation and Agent Navigation
• 3.6 Hands-On: Implementing A* Pathfinding and FSM for NPC Behavior

• 4.1 Core Concepts: States, Actions, Rewards, Policies, Q-Learning:
• 4.2 Exploration versus Exploitation in Learning Systems:
• 4.3 Overview of Deep Q Networks (DQN) and Policy Gradient Methods
• 4.4 Case Study: Reinforcement Learning in DeepMind’s AlphaGo
• 4.5 Hands-On: Train a Reinforcement Learning Model on OpenAI Gym’s GridWorld

• 5.1 Minimax Algorithm and Alpha-Beta Pruning
• 5.2 Monte Carlo Tree Search (MCTS)
• 5.3 Applications in Board Games and Real-Time Strategy (RTS) Games
• 5.4 Case Study: Strategic AI in StarCraft II – Combining Planning Algorithms for Real-Time Strategy
• 5.5 Hands-on Implementation: Guides on implementing the Minimax algorithm for Tic-Tac-Toe

• 6.1 Overview of 2D and 3D Game Environments
• 6.2 Environment Representation Techniques
• 6.3 Navigation and Pathfinding in 2D/3D Spaces
• 6.4 Interaction and Behavior Systems in Virtual Environments
• 6.5 Case Study: Navigation and Interaction AI in The Legend of Zelda: Breath of the Wild
• 6.6 Hands-On: Building Basic Navigation and Interaction in 2D and 3D Game Environments

• 7.1 Adaptive Systems Overview
• 7.2 Dynamic Difficulty Adjustment (DDA) Principles
• 7.3 Adaptive Storytelling, Personalization, and Player Profiling
• 7.4 AI Techniques in Adaptive Systems
• 7.5 Implementation Strategies and Tools
• 7.6 Case Study: Dynamic Enemy Management and Replayability with Left 4 Dead’s AI Director
• 7.7 Hands-On: Developing an Adaptive Dynamic Difficulty System in Unity

• 8.1 Generalist AI Agents and Transfer Learning
• 8.2 AI-Powered Game Design and Testing Tools
• 8.3 Ethical Considerations and AI Transparency
• 8.4 Emerging Technologies: VR/AR AI and AI in Esports Coaching

• Unity ML-Agents
• TensorFlow
• PyTorch
• Python
• OpenAI Gym
• Blender
• NVIDIA DeepStream
• Reinforcement Learning Frameworks
• Natural Language Processing Libraries
• Computer Vision SDKs
• Game Data Analytics Tools
• Behavior Tree Editors

Online proctored exam included, with one free retake.
Exam format:

50 questions, 70% passing, 90 minutes, online proctored exam

Access to all materials and exams is provided for 365 days after delivery.

Instructor-led OR Self-paced course + Official exam + Digital badge