Matt Reid, Web Developer, Technology-Assisted Lifelong Learning, Department for Continuing Education
As a web developer, Matt Reid often faced the challenge of solving tricky coding problems. Before using ChatGPT Edu, his workflow involved searching Google with carefully chosen keywords, trawling through coding forums, testing potential fixes, and often repeating the process when solutions fell short. While sometimes fruitful, this trial-and-error approach could result in hours lost.
With ChatGPT Edu, Matt has reshaped this process. He created a custom GPT tailored to his environment by providing it with details such as his operating system, remote server setup, institutional context, and the software he works with. This means ChatGPT can give context-aware answers, concise at first but ready to expand into detail on request. The integration with tools like his terminal and Visual Studio Code further streamlined the process, reducing the need to copy, paste or reframe context.
The impact has been tangible: Matt estimates the time he spends fixing bugs and implementing new features has been reduced by about one-third. Beyond efficiencies, ChatGPT has also boosted his confidence to tackle more advanced or unfamiliar problems. Its chat history sidebar helps him to group related ideas by project, which prevents repetition and makes it easier for him to track progress across multiple tasks.
Matt also values the reassurance that ChatGPT Edu inputs are all private and all data is retained within the University. Because the tool does not record sensitive information, he feels he can focus fully on problem-solving, without worrying about feeling the need to censor his prompts.
Matt’s advice to others is to approach ChatGPT Edu like a competent colleague: provide assumptions, attempted solutions and context early, and ask for alternatives, with pros and cons, before implementing any new solutions. He also highlights the value of integrating ChatGPT with coding tools for richer, faster feedback, and recommends switching between different large language models if progress stalls.
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