Automated Mentoring with ChatGPT – O’Reilly

Ethan and Lilach Mollick’s paper, “Assigning AI: Seven Approaches for Students with Prompts,” explores seven ways to incorporate AI into teaching. The article discusses the various roles that an AI bot, such as ChatGPT, can play in the education process, including Mentor, Tutor, Coach, Student, Teammate, Simulator, and Tool. Each role is accompanied by a detailed example of a prompt that can be used to implement it, as well as a ChatGPT session using the prompt, risks associated with using the prompt, guidelines for teachers, instructions for students, and guidance for teachers on creating their own prompts. The Mentor role is particularly relevant to O’Reilly’s work in training individuals in technical skills, as it emphasizes the importance of problem-solving and guidance. The author decided to test the Mentor prompt using some short programs they had written. While ChatGPT provided some useful advice, it also had limitations, and the author acknowledges that there is room for improvement in AI mentoring. The article goes on to describe the author’s experiences using ChatGPT to mentor them in two programming scenarios: a Ruby program for prime number sieving and a Python program for data analysis. In both cases, ChatGPT offered advice on improving the code and made suggestions for optimization. However, the author noticed that ChatGPT’s suggestions were not particularly insightful and lacked the level of critical thinking a human mentor would provide. Additionally, ChatGPT sometimes made incorrect or ineffective changes to the code. The author concludes that while ChatGPT is good at providing basic advice, it falls short when it comes to more advanced guidance. They suggest that a mentor should not generate code and that careful testing is necessary when implementing ChatGPT’s suggestions. Despite its limitations, the author recognizes the potential of AI mentoring and believes that with further development, it could become a valuable tool for learners.



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