  Large Language Models (LLMs), as the case of OpenAI ChatGPT-4 Turbo, are
revolutionizing several industries, including higher education. In this
context, LLMs can be personalized through a fine-tuning process to meet the
student demands on every particular subject, like statistics. Recently, OpenAI
has launched the possibility to fine-tune their model with a natural language
web interface, enabling the possibility to create customized GPT version
deliberately conditioned to meet the demands of a specific task. The objective
of this research is to assess the potential of the customized GPTs that have
recently been launched by OpenAI. After developing a Business Statistics
Virtual Professor (BSVP), tailored for students at the Universidad Pontificia
Comillas, its behavior was evaluated and compared with that of ChatGPT-4 Turbo.
The results lead to several conclusions. Firstly, a substantial modification in
the style of communication was observed. Following the instructions it was
trained with, BSVP provided responses in a more relatable and friendly tone,
even incorporating a few minor jokes. Secondly, and this is a matter of
relevance, when explicitly asked for something like, "I would like to practice
a programming exercise similar to those in R practice 4," BSVP was capable of
providing 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, regarding
overall performance, quality, depth, and alignment with the specific content of
the course, no statistically significant differences were observed in the
responses between BSVP and ChatGPT-4 Turbo. It appears that customized
assistants trained with prompts present advantages as virtual aids for
students, yet they do not constitute a substantial improvement over ChatGPT-4
Turbo.

Computation and Language;Artificial Intelligence