1. Demonstrate advanced and systematic knowledge of core and emerging academic AI concepts, theories and methodologies relevant to game-based artificial intelligence. Knowledge & Understanding Research Skills

2. Apply appropriate AI techniques, algorithms and computational approaches to design, implement and evaluate effective game-based agent solutions within complex or uncertain environments. Application & Problem-solving Digital Literacy

3. Critically analyse, compare and evaluate alternative AI strategies using academic research, experimentation and evidence-informed reasoning. Critical Reasoning & Collaboration

4. Conduct rigorous research into AI methods, critically evaluating data, algorithms and methodological limitations, and communicate findings effectively to specialist and non-specialist audiences. Research Skills Communication

Assessment Component 1 – Game Agent Learning Solution [Learning outcomes: 1, 2] Weighting 50%
Description
Students will design and implement a functional game-based AI prototype demonstrating the application of appropriate algorithms, computational techniques or agent architectures. The prototype should address a complex problem relevant to character behaviour, decision-making, navigation, emergent systems, or adaptive gameplay.

Students must submit supporting documentation that analyses design decisions, compares alternative AI approaches, and evaluates the effectiveness of the implemented solution. This documentation should include experimental results, performance analysis, justification of chosen methods, and a critical comparison with academically recognised alternatives.

Assessment Component 2 – Literature Review [Learning outcomes: 3, 4] Weighting 50%

Description
Students will produce a research report that critically explores an advanced AI method, system or concept relevant to contemporary or emerging game-based artificial intelligence. The report must demonstrate a systematic understanding of academic AI theory, critically evaluate data sources and methodologies, and discuss the implications of the selected techniques for interactive systems and player experience.

The report should clearly articulate the research focus, analytical approach, key insights and critical evaluation of limitations, and communicate findings in a clear and professional manner suitable for both specialist and non-specialist audiences

This module will enable students to gain a deeper understanding of key concepts in artificial intelligence. It focuses on the academic side of AI but with a focus on games. The curriculum extends to various types of learning, including genetic algorithms, neural networks, reinforcement learning, and large language models (LLM), whilst also delving into traditional concepts such as artificial life, game theory, and agent psychology.

This module equips you with the essential skills required to apply academic AI to game agents. You will develop the ability to evaluate implementations, develop within practical scenarios, and adapt techniques to game AI challenges, making you well-prepared for success in the growing specialism of game AI.

This module will enable students to gain understanding, apply knowledge, analyse and evaluate problems and create solutions through a variety of activities, including:

- Learning on all aspects of the indicative content will be facilitated by classroom-based lectures, tutorials, laboratory-based practical experiments.

- Independent study: reading, information gathering, presentations, student-centred learning, assignment preparation.

Peters, H. (2016) Game Theory: A Multi-Leveled Approach, Springer.
Prince, S. J. D. (2023) Understanding Deep Learning, The MIT Press.
Roberts, P. (2023) Artificial Intelligence in Games, CRC Press.
Atkinson-Abutridy, J. (2024) Large Language Models: Concepts, Techniques and Applications, CRC Press. Geetha, T. V., and Sendhilkumar, S. (2025) Machine Learning: Concepts, Techniques and Applications, CRC Press

Visual Studio
Unreal Engine / Unity / Other Suitable Engine
VLE (Such as Blackboard) Office Applications (Such as Microsoft Office)
University of Staffordshire Library
Internet Access
Digital Academy Forum
Digital Academy Upload