Vibe Coding: AI-Assisted Software Development

After completing the course, the student should be able to:

• Explain concepts of AI-assisted software development and its relationship to traditional programming practices
• Describe how contemporary AI systems can support software development workflows
• Explain central concepts related to human–AI collaboration in programming and digital production
• Demonstrate understanding of key programming concepts relevant for AI-assisted software development
• Reflect on the broader technological, societal, and ethical implications of AI-assisted development practices

After completing the course, the student should be able to:

• Formulate programming tasks and development problems suitable for AI-assisted development approaches
• Develop and adapt software solutions through constructive use of AI-assisted development tools and workflows
• Identify limitations, errors, or inconsistencies in AI-assisted software artefacts and development outputs
• Utilise fundamental programming concepts and knowledge to support the development and evaluation of AI-assisted software solutions
• Critically assess the reliability, correctness, and usefulness of AI-assisted outputs and solutions

After completing the course, the student should be able to:

• Independently apply AI-assisted development approaches in practical digital development contexts
• Critically reflect on the opportunities and limitations of artificial intelligence in software development and digital technologies
• Integrate human judgment, domain knowledge, and computational reasoning when working with AI-assisted systems
• Collaborate with others in exploratory and experimental digital development environments
• Adapt to evolving technological environments and emerging AI-supported development practices
• Define and delimit academic issues within AI-assisted software development, and critically analyse and address them using relevant theories, methods, and current research

The teaching explores a scientific introduction to AI-assisted software development with emphasis on emerging practices within human–AI collaboration in programming. The course explores how artificial intelligence systems can support different stages of software development, such as problem formulation, assisted code generation, debugging, documentation, and iterative refinement. Students are introduced to conceptual frameworks for understanding human–AI collaboration in programming, as well as practical techniques for working productively with AI-supported development environments. The subject also examines broader perspectives on reliability, accountability, authorship, and creativity in AI-assisted development. Through practical exercises and projects, students experiment with designing, building, and refining artefacts while critically reflecting on the evolving role of programming skills in an AI-augmented technological landscape. The course also develops the ability to define and delimit academic issues within AI-assisted software development, and to critically examine, analyse, and address these using relevant theories, methods, and current research.

The course is organized around a sequence of thematic elements exploring AI-assisted development practices. These may include:

• Introduction to AI-assisted programming and concepts such as “vibe coding”
• Human–AI collaboration and computational thinking in AI-supported environments
• Working with AI systems for assisting with generating, modifying, explaining, and evaluating code
• Core programming concepts relevant for supporting the development and evaluation of AI-assisted software solutions
• Iterative development workflows within AI-assisted development
• Data-driven workflows and experimentation with AI-assisted data science analysis, modelling, and visualization
• Emerging developments in AI-supported software development
• Specific tools, technologies, and examples used in the course may vary in order to look to reflect current developments in the field.

The following teaching and working methods may be used in the course: lectures, group teaching, discussions, exercises, e-learning activities, workshops, practical coding activities, reflection exercises, and project-based work. Teaching is organized around a combination of conceptual lectures and hands-on exploration where students actively engage with AI-assisted development practices. Students will work both individually and collaboratively to develop artefacts, test ideas, and reflect on the strengths and limitations of AI-assisted solutions. Feedback may take the form of feedback provided by the teacher and peers in connection with exercises, discussions, and practical work. 

The teaching is organized in such a way that it supports the humanities model for active learning and activating teaching, as described in the curriculum section Didactic, pedagogical basis and contact with the research environment. At the start of the course, the teacher informs students about how the study activities are organized.

The activities in the course are primarily aimed at achieving the learning objectives and preparing the student for the examination form, which consists of a portfolio, but may also include written reflections, practical exercises, and collaborative exploration

Teaching in the subject may involve students participating in the following teaching and study formats:

• Teaching room where the teacher has planning responsibility and is present
• Study rooms where the teacher has planning responsibility but is not present
• Teaching room where the teacher is present, but students have planning responsibility for specific sub-activities
• Study rooms where students have planning responsibility and the teacher is not present