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Large Language Models (LLMs), as the case of OpenAI ChatGPT-4 Turbo, arerevolutionizing several industries, including higher education. In thiscontext, LLMs can be personalized through a fine-tuning process to meet thestudent demands on every particular subject, like statistics. Recently, OpenAIhas launched the possibility to fine-tune their model with a natural languageweb interface, enabling the possibility to create customized GPT versiondeliberately conditioned to meet the demands of a specific task. The objectiveof this research is to assess the potential of the customized GPTs that haverecently been launched by OpenAI. After developing a Business StatisticsVirtual Professor (BSVP), tailored for students at the Universidad PontificiaComillas, its behavior was evaluated and compared with that of ChatGPT-4 Turbo.The results lead to several conclusions. Firstly, a substantial modification inthe style of communication was observed. Following the instructions it wastrained with, BSVP provided responses in a more relatable and friendly tone,even incorporating a few minor jokes. Secondly, and this is a matter ofrelevance, when explicitly asked for something like, "I would like to practicea programming exercise similar to those in R practice 4," BSVP was capable ofproviding a far superior response: having access to contextual documentation,it could fulfill the request, something beyond ChatGPT-4 Turbo's capabilities.On the downside, the response times were generally higher. Lastly, regardingoverall performance, quality, depth, and alignment with the specific content ofthe course, no statistically significant differences were observed in theresponses between BSVP and ChatGPT-4 Turbo. It appears that customizedassistants trained with prompts present advantages as virtual aids forstudents, yet they do not constitute a substantial improvement over ChatGPT-4Turbo.

Real Customization or Just Marketing: Are Customized Versions of Chat GPT Useful?