Tools like ChatGPT are capable of solving many introductory programming tasks. In fact, one can often just copy and paste the instructions without any additional effort and get a detailed solution and explanation back.
This has caused a lot of discussion about the learning effects of LLMs that generate code from natural language descriptions on programming education (e.g., The Robots Are Coming). Educators are concerned about students becoming over-reliant on AI tools and not learning effectively. In contrast, these tools might also potentially lower the barriers of entry to programming, and even broaden participation into computing!
Austin Z. Henley and Majeed Kazemitabaar summarize two research papers looking at this.
We performed a thematic analysis on 1666 usages of the AI code generator by students in the Codex group from the seven training sessions. We focused on how they used the tool, what prompts they used, and how did they verify and use the AI-generated code. We discovered various signs of over-reliance and signs of self-regulation.
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