AI/ML, Employability and Higher Education - Roundup 05 May 2025

The article discusses the increasing use of AI in software development at major tech companies, with Microsoft reporting that up to 30% of its code is now AI-generated. This trend has significant implications for universities in preparing students for future tech careers, as it suggests a shift in the required skillset for software developers and the potential restructuring of traditional software applications.

One article to highlight this week:

• Sharwood (2025) reports that Microsoft now uses AI to generate up to 30% of its code, particularly for new projects, with CEO Satya Nadella discussing the potential merging of traditional office applications. This article is relevant as it highlights the growing role of AI in software development and the changing nature of software applications, which universities must consider in their curriculum design.

The growing integration of AI in software development at major tech companies like Microsoft signals a significant shift in the tech industry. This trend has far-reaching implications for higher education, particularly in computer science and software engineering programs. Universities must adapt their curricula to prepare students for a future where AI-assisted coding is the norm, focusing on developing skills that complement AI capabilities while maintaining a strong foundation in computer science principles. The potential merging of traditional software applications also suggests a need for a more holistic approach to software development education, preparing students for a more fluid and interconnected software landscape.

1. Changes in the broader labour market: Sharwood (2025) suggests that AI is becoming an integral part of software development in major tech companies. Educators need to prepare students for a labour market where AI-assisted coding is commonplace, focusing on skills that complement AI capabilities such as problem-solving, system design, and understanding complex software architectures.

2. Changes in jobs and tasks: The article indicates that AI is particularly effective in writing new code rather than modifying existing code (Sharwood 2025). Educators should help students understand how this might change the nature of software development tasks, emphasizing skills in AI collaboration, code review, and high-level software design that may become more prominent as AI takes over routine coding tasks.

3. Student preparation: While Sharwood (2025) doesn’t directly address student preparation, the implications are clear. Students must develop a deep understanding of AI and machine learning principles, learn to work effectively with AI coding tools, and cultivate high-level analytical and critical thinking skills. Additionally, strong domain knowledge in computer science and software engineering will remain crucial as AI tools become more prevalent in the industry.

I highlight articles about computer programming and software engineering because this is the leading edge indicator for how other graduate jobs might be affected by AI/ML. Non-programming jobs also involve analysis of process, development of solutions, documentation of process and solution, testing efficiency and reliability of the processes, etc.

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