Python programming project with AI (5cr)

Evaluation methods and criteria

The maximum number of points available from course is 120. These 120 points are divided as follows: - 60 points from 6 individual exercises - 40 points from personal project work - 20 points from being present on the lectures The course evaluation scale is the following: Min points -> Grade 0 -> 0 40 -> 1 56 -> 2 72 -> 3 88 -> 4 104 -> 5 Please note this additional condition: You must get at least 25 points from the exercises and 15 points from the personal project work to pass the course. The personal project work points consist of two parts. 10 points come from peer-assessment of two fellow student project work submissions, and the remaining 30 points from student's own project work peer-assessed by other students. The points from being present are calculated using the following scale: Percentage of being present on the theoretical and practical lectures -> points 20% -> 5 40% ->10 60%->15 80%->20 Please also note that by being present you can earn some of the points available from the individual exercises working together with the instructor. You must be present in demonstrations. The attendance in those events does not accumulate your points of being present. If you are not present in the demonstrations, then there is a reduction of 25 % in the points of your returned exercises on these demos. There is also a reduction of 25 % in points of the exercises that are returned late. No exercises are accepted after the end date of the course implementation. After the end date of the course, no substitute or supplementary exercises or project work commission will be given either. The student must therefore make sure that he collects enough points from different performances during the time of the course. During the course, students are allowed to use AI responsibly and in accordance with the teacher’s instructions in exercises and project work.

Evaluation scale

H-5

Content scheduling

In this course, students gain a strong foundational understanding of programming and are introduced to AI-assisted learning and coding. The course combines traditional programming studies with modern AI tools (vibe coding environments), which help students understand programming more quickly, identify errors, and learn best practices. Students will gain hands-on experience in how artificial intelligence can support programming and enhance the learning process. These skills are applied to a personal project throughout the course. CONTENTS: * Introduction to programming and computational thinking * Setting up the development environment and AI-assisted tools (e.g., ChatGPT, GitHub Copilot, Replit, or other vibe coding environments) * Basic syntax, variables, and data types in Python * Input and output operations, expressions, and operators * Conditional statements and control flow * Loops and iteration structures * Functions and modular programming * Lists, dictionaries, and other basic data structures * Debugging and error handling with AI support * Code style, commenting, and best practices * Applying AI tools for code generation, optimization, and explanation * Designing and implementing a small personal programming project using AI-assisted methods

Objective

In this course, students gain a strong foundational understanding of programming and are introduced to AI-assisted learning and coding. The course combines traditional programming studies with modern AI tools (vibe coding environments), which help students understand programming more quickly, identify errors, and learn best practices. Students will gain hands-on experience in how artificial intelligence can support programming and enhance the learning process. These skills are applied to a personal project throughout the course. By the end of the course, students will: * Understand the basic concepts and logic of programming * Write simple Python programs * Use conditional statements, loops, and functions * Utilize basic data structures and types * Solve problems with AI assistance and evaluate AI-generated code * Use AI tools for code generation, optimization, and error handling * Apply learned skills to in a wider context (personal project)

Content

* Introduction to programming and computational thinking * Setting up the development environment and AI-assisted tools (e.g., ChatGPT, GitHub Copilot, Replit, or other vibe coding environments) * Basic syntax, variables, and data types in Python * Input and output operations, expressions, and operators * Conditional statements and control flow * Loops and iteration structures * Functions and modular programming * Lists, dictionaries, and other basic data structures * Debugging and error handling with AI support * Code style, commenting, and best practices * Applying AI tools for code generation, optimization, and explanation * Designing and implementing a small personal programming project using AI-assisted methods

Materials

To be published later

Teaching methods

- Participation in lectures and demonstrations - Reading the learning materials - Individual practical assignments and project work

Exam schedules

There is no exam in this course

International connections

The course content includes responsible and safe use of artificial intelligence as part of learning programming in Python. In this context, aspects of sustainable development are also addressed.

Completion alternatives

No alternative completion methods

Student workload

5 credits: 27 * 5 = 135 hours of work Duration: 12.1. - 30.4.2025 (14-15 weeks + winter break on week 8) 20 h reading and watching course material approx. 36 h contact lessons (30 hours of lectures + 6 hours of demonstrations) 48 h personal exercises approx. 30 h personal project work

Qualifications

No prerequisites