KIUA2000 Interactive simulations
- Course codeKIUA2000
- Number of credits10
- Teaching semester2026 Spring
- Language of instructionNorwegian/English
- CampusHamar
- Required prerequisite knowledge
None
In this course, students will immerse themselves in the fundamental concepts of system thinking and how machine learning can be used to solve complex problems. The course teaches students how to understand algorithmic behaviour, examine interactions with environments and the difficulties associated with causality mapping of the dynamics in neural networks. Students will also learn how ML techniques can be used to optimise simulations. Teaching will be delivered in the form of practical projects in which students will use AI techniques in connection with simulation models to simulate and address real-world challenges in society. Students will gain proficiency in key tools for simulation software and will optimise simulation models using AI-driven methods. The course culminates in an extensive cornerstone project, emphasising the harmonic integration of AI and simulation for effective problem solving.
Learning Outcome
Upon successfully passing the course, students will have achieved the following learning outcomes:
The student will have
- sound knowledge of system analysis, dynamics and machine learning principles
- sound knowledge of complex systems, challenges linked to modelling of such systems and the convergence of AI technologies with system dynamics for improved decision-making and prediction
The student will be able to
- seamlessly integrate system dynamics models with machine learning algorithms using appropriate software tools to analyse system behaviour and predict outcomes
- perform sensitivity analyses in the context of AI, for example in neural networks, in order to understand and assess the impact of model parameters on system behaviour
- interpret simulation results from a system dynamics and AI perspective and draw insights based on data generated from integrated models
The student will be able to
- utilise system dynamics and AI methodologies to meet real-world challenges
- identify complex system dynamics and recommend effective data-driven strategies for system improvements
- communicate and collaborate in interdisciplinary projects and ensure effective transfer of AI and system dynamics concepts to a broad audience
- analyse and make informed decisions rooted in simulation results using the hybrid approach to system dynamics and AI for strategic planning and policy development
The course comprises a combination of lectures, practical exercises, independent study and academic supervision.
- 2 group assignments
Compulsory coursework requirements that have been passed are valid for 12 months only. Students wishing to take examinations after 12 months must pass the compulsory coursework requirements again in connection with the next scheduled delivery of the course.
| Form of assessment | Grading scale | Grouping | Duration of assessment | Support materials | Proportion | Comment |
|---|---|---|---|---|---|---|
Written examination with an adjusting oral examination | ECTS - A-F | Group |
| 100 |
- 1 project-based group assignment
- 1 oral presentation that can adjust the final grade up or down by one grade
All group members must contribute to the oral presentation. When taking part in group examinations, all participants in the group are responsible for all content of the task/product/work.
The assignment is assessed using a grading scale from A-F, where E is the lowest passing grade.
Students are able to choose which language to use for their examination. The available options are Norwegian Bokmål, Nynorsk and English.
Permitted aids:
- Literature
- All printed and written resources
- Any use of AI-generated text and content must be clarified with the lecturer, clearly labelled and academically justified in the submission