Background: Large Language Models (LLMs), as in the case of OpenAI ChatGPT-4 Turbo, are revolutionizing several industries, including higher education. In this context, LLMs can be personalised through customization process to meet the student demands on every particular subject, like statistics. Recently, OpenAI launched the possibility of customizing their model with a natural language web interface, enabling the creation of customised GPT versions deliberately conditioned to meet the demands of a specific task.
Methods: This preliminary research aims to assess the potential of the customised GPTs. After developing a Business Statistics Virtual Professor (BSVP), tailored for students at the Universidad Pontificia Comillas, its behaviour was evaluated and compared with that of ChatGPT-4 Turbo. Firstly, each professor collected 15-30 genuine student questions from "Statistics and Probability" and "Business Statistics" courses across seven degrees, primarily from second-year courses. These questions, often ambiguous and imprecise, were posed to ChatGPT-4 Turbo and BSVP, with their initial responses recorded without follow-ups. In the third stage, professors blindly evaluated the responses on a 0-10 scale, considering quality, depth, and personalization. Finally, a statistical comparison of the systems' performance was conducted.
Results: 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 responded in a more relatable and friendly tone, even incorporating a few minor jokes. Secondly, when explicitly asked for something like, "I would like to practice a programming exercise similar to those in R practice 4," BSVP could provide a far superior response. 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.
Conclusions: It appears that customised assistants trained with prompts present advantages as virtual aids for students, yet they do not constitute a substantial improvement over ChatGPT-4 Turbo.
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http://dx.doi.org/10.12688/f1000research.153129.2 | DOI Listing |
PLoS One
January 2025
Faculty of Dentistry, PHENIKAA University, Hanoi, Vietnam.
Objectives: This study aims to evaluate the performance of the latest large language models (LLMs) in answering dental multiple choice questions (MCQs), including both text-based and image-based questions.
Material And Methods: A total of 1490 MCQs from two board review books for the United States National Board Dental Examination were selected. This study evaluated six of the latest LLMs as of August 2024, including ChatGPT 4.
World J Mens Health
December 2024
Division of Urology, Department of Surgery, Far Eastern Memorial Hospital, New Taipei, Taiwan.
Purpose: Information retrieval (IR) and risk assessment (RA) from multi-modality imaging and pathology reports are critical to prostate cancer (PC) treatment. This study aims to evaluate the performance of four general-purpose large language model (LLMs) in IR and RA tasks.
Materials And Methods: We conducted a study using simulated text reports from computed tomography, magnetic resonance imaging, bone scans, and biopsy pathology on stage IV PC patients.
Cureus
November 2024
Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, USA.
Aims And Objectives: This study aimed to compare the accuracy of two AI models - OpenAI's GPT-4 Turbo (San Francisco, CA) and Meta's LLaMA 3.1 (Menlo Park, CA) - when answering a standardized set of pediatric radiology questions. The primary objective was to evaluate the overall accuracy of each model, while the secondary objective was to assess their performance within subsections.
View Article and Find Full Text PDFClin Anat
November 2024
College of Medicine, Alfaisal University, Riyadh, Kingdom of Saudi Arabia.
The increasing application of generative artificial intelligence large language models (LLMs) in various fields, including medical education, raises questions about their accuracy. The primary aim of our study was to undertake a detailed comparative analysis of the proficiencies and accuracies of six different LLMs (ChatGPT-4, ChatGPT-3.5-turbo, ChatGPT-3.
View Article and Find Full Text PDFSci Rep
November 2024
Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
This study aims to measure the performance of different AI-language models in three sets of pre-internship medical exams and to compare their performance with Iranian medical students. Three sets of Persian pre-internship exams were used, along with their English translation (six sets in total). In late September 2023, we sent requests to ChatGPT-3.
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