AI copilots are changing how coding is taught

Role of AI copilots in learning to code

• Many see LLMs as powerful assistants for boilerplate, glue code, and unfamiliar APIs or frameworks, especially for experienced developers who can debug and evaluate output.
• Some instructors and devs argue they can accelerate learning new languages/frameworks (e.g., Swift, Metal, CUDA, Python) by iterating on generated code and asking for explanations.
• Others report juniors often paste code they don’t understand, struggle to debug hallucinations, and get lost when simple examples don’t match real-world complexity.

Foundations vs “prompt engineering”

• Strong sentiment that AI tools are “calculators for those who already know math”: useful only after fundamentals (syntax, control flow, data structures, architecture) are internalized.
• Several worry that shifting curricula away from syntax and low-level practice will stunt fluency; understanding is seen as emerging from lots of hands-on coding and debugging.
• Many reject the idea that learning to prompt an LLM is a substitute for learning programming; some would allow a dedicated “AI for SE” course but not AI during core exams.

Impact on code quality and professional practice

• Reports of AI-generated code increasing weird abstractions, subtle bugs, and inconsistent styles; reviewers can often spot “AI smell” in PRs.
• Strong emphasis on code review, testing, and the ability to explain one’s own code as defenses against blind AI use.
• Concern that AI makes it easier to flood codebases with low-quality snippets, amplifying existing problems of shallow library/Stack Overflow copy-paste.

Changes in what programmers do

• Many argue most commercial work is already mundane (e.g., data shuffling, integration), and AI will automate much of that 95%, leaving only the hardest 5% where deep expertise matters.
• Others fear juniors will never develop that expertise if AI handles the easy parts from day one.

Parallels, analogies, and broader worries

• Frequent comparisons to calculators in math and Google Translate in language learning: helpful aids, but harmful if introduced before basics.
• Broader concern about “dumbing down” across tech (cloud networking, devops, trades), where people can operate tools but lack underlying understanding.
• Unclear how far “natural language programming” can really go given LLMs’ probabilistic, sometimes wrong behavior.