AI Coding Assistants Boost Confidence, Expose Gaps in Fundamentals

🧩 The Gist

An exploratory study of novice programmers looked at how students perceive AI coding assistants in an introductory course. Using a two part exam, students first solved a task with AI support, then extended the solution without it. Students reported that AI tools helped them grasp code concepts and feel more confident during initial development. When asked to continue unaided, many struggled, pointing to overreliance and weak knowledge transfer. The authors argue for pedagogical approaches that integrate AI while strengthening core programming skills.

🚀 Key Highlights

• Study examined student perceptions of AI coding assistants in an introductory programming course.
• Twenty students completed a two part exam, first with AI access, then without it for an extension.
• Researchers collected both Likert scale and open ended responses on perceived challenges.
• Students viewed AI as helpful for understanding code concepts and building confidence early in development.
• Difficulty increased when students had to work without AI, suggesting potential overreliance.
• Findings highlight the need for teaching methods that integrate AI and still enhance foundational skills.

🎯 Strategic Takeaways

• Education design: Combine AI supported activities with unaided tasks to check real understanding and knowledge transfer.
• Assessment: Capture perceptions and challenges with mixed methods, then align grading rubrics to reward core reasoning, not just output.
• Curriculum: Teach AI literacy while reinforcing fundamentals, so assistants augment learning rather than replace it.
• Policy and guidance: Set clear norms for when AI use is appropriate in coursework, and when independent work is required.

🧠 Worth Reading

New Kid in the Classroom: Exploring Student Perceptions of AI Coding Assistants (arXiv). The paper reports that AI tools can aid concept comprehension and confidence at the start of coding, yet many students face challenges when continuing without help. The practical takeaway is to intentionally design learning experiences that use AI for support while ensuring students practice and demonstrate unaided problem solving.