How declining school performance, critical thinking, and AI skills could shape Germany’s future competitiveness
Germany faces a growing challenge: while businesses grapple with skilled labor shortages, digital transformation, and the rapid adoption of artificial intelligence (AI), student performance in mathematics, reading, and science continues to raise concerns. These issues are closely connected. The skills developed in school form the foundation for technological innovation, workforce readiness, and long-term economic growth.
The OECD’s PISA assessments offer important insights into the abilities that underpin a competitive, technology-driven economy. Mathematical reasoning, scientific literacy, and reading comprehension are essential for analyzing data, developing software, understanding technical documentation, and solving complex problems.
When these foundational skills weaken, the consequences extend beyond schools and universities. Businesses eventually feel the impact through increased training requirements and a smaller pool of suitably prepared talent.
Why STEM Skills Matter for Germany’s Workforce
Technical training cannot begin entirely from scratch. Companies can teach programming languages, databases, software architecture, and development tools. However, it becomes more difficult when trainees lack fundamental mathematical knowledge, structured thinking, reading comprehension, and the persistence needed to solve unfamiliar problems.
For Bremen-based software company Genese.de GmbH, which develops software for managing patents, trademarks, and other intellectual property rights, these challenges are part of the practical realities of vocational training.
Managing Director Oliver Otto emphasizes the importance of curiosity in software development.
“Curiosity is one of the most important qualities for an application developer. They need to understand why something works, why it fails elsewhere, and whether a better solution exists,” Otto explains.
Developers who accept the first available answer without questioning it may struggle to produce consistently reliable results, particularly as AI becomes more integrated into software development.
Curiosity and Academic Achievement Must Work Together
Children naturally ask questions, experiment, and explore how things work. Yet this curiosity can diminish when education focuses too heavily on producing correct answers quickly and avoiding mistakes.
In mathematics, science, and technology, progress rarely comes from knowing the solution immediately. Software developers test code, identify errors, and refine their approach. Engineers simulate designs, assess failures, and improve their work. Scientific discoveries often begin with recognizing that something remains unexplained.
Germany therefore needs both academic achievement and intellectual curiosity. Curiosity encourages exploration, while discipline and sustained effort turn questions into knowledge and practical skills.
An effective education system should maintain high expectations without discouraging students from questioning assumptions, learning from mistakes, and exploring alternative solutions.
Artificial Intelligence Makes Foundational Knowledge More Important
AI tools can now generate code, analyses data, draft content, and explain complex subjects in seconds. This may create the impression that foundational knowledge is becoming less important. In reality, the opposite is increasingly true.
Professionals must be able to evaluate AI-generated results, identify incorrect assumptions, check sources, and recognize missing information. Producing an answer is not the same as producing a reliable answer.
An experienced software developer can use AI to generate code quickly, but their expertise remains essential for assessing architecture, security, edge cases, and long-term maintainability. A beginner may generate similar-looking code without recognizing its weaknesses.
The same principle applies to business analysis, technical assessments, calculations, and written communication. AI can produce convincing results that are nevertheless inaccurate or incomplete.
Consequently, AI literacy requires more than writing effective prompts. It demands subject knowledge, critical thinking, analytical skills, and the confidence to challenge a machine's conclusions.
What AI Skills Mean for Education and Business
Students and employees should learn to use AI as a tool for investigation rather than simply accepting its first response. Effective AI use involves asking follow-up questions, comparing alternatives, verifying evidence, and reconsidering the original problem when necessary.
These habits closely resemble scientific reasoning. They also help businesses make better decisions and use technology more productively.
For companies such as Genese.de, which trains future application developers, the distinction is particularly relevant. Programming tools and AI systems can accelerate development, but they cannot eliminate the need for people who understand what they are building and why.
The challenge for Germany is to prepare a workforce that combines technical competence with independent judgement. As AI automates more routine tasks, employees who can understand complex systems, evaluate outputs, and make informed decisions will become increasingly valuable.
Why Companies Cannot Solve the Education Problem Alone
Businesses can support young talent through apprenticeships, internships, structured training, and practical experience. However, they cannot permanently compensate for weaknesses that develop throughout a student's school years.
Improving education also requires investment in teachers, modern learning environments, digital infrastructure, and effective teaching methods. Just as importantly, students must have opportunities to practice, make mistakes, and develop confidence in tackling difficult problems.
Learning requires effort. Watching tutorials or copying existing solutions is not enough to become a capable software developer. Real competence develops through experimentation, problem-solving, and the willingness to try again after failure.
Academic achievement and curiosity are not opposing goals. When combined, they create the conditions for deeper understanding and sustained innovation.
Germany's Economic Competitiveness Starts in the Classroom
Germany cannot rely on low labor costs to compete internationally. Its economic success depends on productivity, technological expertise, quality, and innovation. All require a workforce with strong foundational skills.
The country's STEM education challenge is therefore more than a school policy issue. It is a long-term economic concern that affects businesses, technical training, and Germany's ability to remain competitive in an increasingly AI-driven world.
Schools must teach knowledge and demand achievement while preserving students' desire to understand how things work. Businesses must continue developing talent, and employees must learn to use AI critically rather than unquestioningly.
Ultimately, Germany's future will depend not simply on how many people can operate AI tools, but on how many have the knowledge and judgement to evaluate their results, challenge their assumptions, and turn them into meaningful innovation.
The foundation of Germany's technological future is built in the classroom, long before the first line of code is written.
By Oliver Otto | Bremen | 23 September 2026
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