Skills of IT-professionals seem to have changed a lot over time. From assembler language and structured coding to machine-learning algorithms and quantum-computing programming. From waterfall models to agile sprints. From working in a cubicle to online team programming and virtual reality sessions. From a technology-centered to a customer-centric view. So are there any basic competencies that every IT-professional will still learn in a decade or will Artificial Intelligence take over coding and managing IT-systems completely?

Current studies show that code-quality suffers significantly, and security issues arise as the usage of large language models for producing code snippets increases, because inexperienced developers neglect quality assurance and rely too much on AI-generated code.

So what elemental skills should future students of computer science focus on? Are trending micro- or nanodegrees for special-purpose software paradigms really an alternative to basic topics like algorithms and data structures, foundations of databases, etc.? Can skillful prompt engineering replace classical computer science education?

In this article, the discussion of necessary IT-skills as well as their evolvement over time is structured along the three dimensions coding skills, software engineering paradigms and collaboration technologies & teamwork. We identify the basic competencies in each dimension that students need to acquire independent of any IT “hype”. Thereby we refer to existing frameworks like the recommendations for Bachelor's and Master's programs in computer science programs at universities of the German Gesellschaft for Informatik (GI). In each dimension, we distinguish between professional, methodological and social competencies, thus creating a kind of “knowledge map”. For example, using a concrete project management software would be regarded as a professional skill in collaboration technologies & teamwork competencies. Similarly, familiarity with algorithmic patterns can be seen as a methodological competency in software engineering paradigms, etc.

We then map actual technological advancements like AI tools to this scheme to show how they make use of these basics, creating “paths” in this knowledge map. Making these relations transparent, students and potential career changers can better understand the necessity to learn the basics and how this helps them to stay up to date with current advancements.

Universities (especially universities of applied sciences) should guide students by providing such knowledge maps that show the interconnectedness of different technological fields and methods. They help make curricula more transparent and easier to understand. Doing so, students would gain confidence that along their study journey, more and more “white gaps” are filled with knowledge and that courses that are thematically overlapping are rather a satisfying affirmation, not a nuisance. This would also enable them to explain to other students why the topics they are working on are valuable: Laying proper knowledge foundations instead of serving short-span hypes will be a better preparation for work life and will give these students advantages over career changers lacking a sound background.

Keywords: Computer science skills, IT-skills, competencies, artificial intelligence, student motivation.