Code: S1C4
ECTS: 6
Semester: 1st
This course dives into the realm of intelligent systems, specifically focusing on how Artificial Intelligence (AI) and Machine Learning (ML) can be used to create dynamic and adaptive user experiences. We’ll explore the principles and techniques behind systems that learn from user interactions and modify their behaviour to optimise outcomes.
Key topics covered will include:
- Fundamentals of AI and ML in Interactive Systems: We’ll establish a foundation in the core concepts of AI and ML, including supervised learning, unsupervised learning, reinforcement learning, and their relevance to interactive applications.
- Context-Aware Computing: Explore how systems can gather and interpret contextual information (user preferences, location, time, device, etc.) to personalise content, interface, and interaction.
- Dynamic Content Adaptation: Learn how AI can be used to tailor content delivery in real-time based on user behaviour, preferences, and learning goals. This includes applications in personalised recommendations, adaptive learning platforms, and intelligent tutoring systems.
- Adaptive User Interfaces: Examine techniques for designing interfaces that dynamically adjust their layout, features, and interaction modalities based on user needs and context. This includes topics like responsive design, personalization algorithms, and user modelling.
- Reinforcement Learning for Interaction Optimization: Dive into how reinforcement learning can be used to optimise user interactions and guide users towards desired outcomes. This includes applications in game design, interactive storytelling, and personalised recommendations.
- Evaluation of Adaptive Systems: Learn how to assess the effectiveness of intelligent interactive systems, measuring user engagement, satisfaction, and learning outcomes.
- Ethical Considerations: Discuss the ethical implications of AI-driven adaptation, including issues related to bias, fairness, transparency, and user privacy.
Introduction to artificial intelligence and machine learning in interactive systems; supervised, unsupervised, and reinforcement learning in application-oriented perspective; intelligent adaptation in digital interfaces and interactive experiences; user modelling, profiling, and behavioural data interpretation; context-aware systems and the use of contextual variables for dynamic adaptation; personalised content delivery and adaptive interaction flows; adaptive user interfaces and responsive interaction strategies; optimisation of interaction through learning-based mechanisms; prototyping and evaluation of intelligent interactive systems; interpretability, fairness, privacy, bias, and ethical considerations in AI-driven interaction design.
- Search, analysis and synthesis of data and information, with the use of the necessary technology
- Adaptability to new situations
- Decision-making
- Working independently
- Team work
- Working in an interdisciplinary environment
- Production of new research ideas
- Project planning and management
- Showing social, professional and ethical responsibility and sensitivity to gender issues
- Criticism and self-criticism
- Production of free, creative and inductive thinking
Upon successful completion of the course, students will be able to:
Knowledge
- explain core concepts of artificial intelligence and machine learning relevant to interactive systems;
- describe approaches to context-aware computing, user modelling, adaptive interfaces, and intelligent content delivery;
- discuss the opportunities and challenges of AI-driven adaptation in interactive environments.
Skills
- analyse interaction scenarios and identify how AI/ML methods may support dynamic adaptation;
- design and prototype interactive systems that adapt content, interface, or interaction behaviour based on contextual or behavioural data;
- evaluate the effectiveness of adaptive systems using appropriate user-centred criteria.
Competences
- critically assess the implications of intelligent adaptation in relation to transparency, fairness, privacy, and user autonomy;
- integrate computational reasoning with design-oriented thinking in the development of intelligent experiences;
- make informed design and evaluation decisions in the context of adaptive and data-driven interaction.
