27210052 - Adaptive Interactive Systems

This course trains students in the design of adaptive interactive systems that can adapt to users and their specific context of use. It covers the theoretical foundations, user and context models, and adaptation mechanisms. The course will also cover current issues in adaptive systems, including ambient intelligence and the distribution of interactions across multi-surface spaces (such as screen walls and interactive tables). Through a case study, students will design a prototype of an adaptive system based on a real-life usage scenario, addressing the issues of usability, ethics, and transparency in adaptive systems.

Estimation of private study (outside of contact hours): 20 hours


TARGETED KNOWLEDGE AND SKILLS

Targeted knowledge
At the end of the course, students will have acquired an understanding of:

• The nature, objectives, and challenges of adaptive interactive systems.
• The key concepts related to personalization, adaptation, and recommendation.
• The essential dimensions of modelling users and contexts of use.
• The different mechanisms and forms of adaptation in interactive systems.
• The ethical, technical, and interactional implications of adaptation.

Targeted skills
At the end of the course, students will be able to:

• Design user and context models adapted to a specific scenario.
• Develop coherent, user-centered adaptation logics.
• Design an interactive scenario incorporating adaptation mechanisms, particularly in a multi-surface environment.
• Assess and discuss the ethical issues and limitations of adaptive systems.
• Apply this knowledge and these skills practically to a case study.

COURSE OUTLINE

1.    Introduction to adaptive interactive systems
2.    User modeling
3.    Context modeling and adaptation approaches
4.    Adaptive interfaces and distributed interactions
5.    Ethics, transparency and finalisation of the case study

PRESCRIBED TEXTS AND PUBLICATIONS

1. Germanakos, P., Dimitrova, V. G., & Kleanthous, S. (2020). Article Collection on the Human Aspects in Adaptive and Personalized Interactive Environments. Frontiers in Artificial Intelligence, 3, 606068.
2. Calvary, G., Coutaz, J., Thevenin, D., Limbourg, Q., Bouillon, L., and Vanderdonckt, J. (2003). A Unifying Reference Framework for multi-target user interfaces. Interacting with Computers, 15(3):289–308.

RECOMMENDED TEXTS AND PUBLICATIONS

1. Lin, J., Dai, X., Xi, Y., Liu, W., Chen, B., Zhang, H., ... & Zhang, W. (2025). How can recommender systems benefit from large language models: A survey. ACM Transactions on Information Systems, 43(2), 1-47.
2. Omid Gheibi, Danny Weyns, and Federico Quin. 2021. Applying Machine Learning in Self-adaptive Systems: A Systematic Literature Review. ACM Trans. Auton. Adapt. Syst. 15(3), 1- 37 pages. https://doi.org/10.1145/3469440

TEXTS AND PUBLICATIONS OF IAELYON FACULTY ON THE SUBJECT OF THE COURSE  

Hallifax, S., Serna, A., Marty, J.-C., and Lavoué, (2019b). Adaptive Gamification in Education: A Literature Review of Current Trends and Developments. In Scheffel, M., Broisin, J., Pammer-Schindler, V., Ioannou, A., and Schneider, J., editors, Transforming Learning with Meaningful Technologies, pages 294–307.

Individual grade
Reflexive Report

Other grade(s)
Design report

Pondération : 40% / 60%

TEACHING METHODS

- Interactive lessons: theoretical contributions accompanied by concrete examples and discussions.
- Group case studies: progressive application of concepts to a specific scenario.
- Reflexivity: individual report to reflect on the choices made and the issues at stake.

NATURE OF MATERIALS

Lesson slides, videos (concrete examples), case studies

TEACHING INNOVATIONS AND USE OF TECHNOLOGY

All materials available on Moodle. Modeling and prototyping tools (e.g., Figma)