Abstract
This study refines the NExGEN Prompt Generator–ChatGPT Framework for personalized training and nutrition planning in team sport athletes using the Fuzzy Delphi method. It addresses the limitations of current costly and time-intensive personalized health solutions, focusing on scalable, technology-driven alternatives to improve accessibility and effectiveness [1]. This study refined the NExGEN Prompt Generator–ChatGPT Framework through expert input using the Fuzzy Delphi Method. A purposive sample of 21 experts from nutrition, exercise, medicine, psychology, and AI evaluated personalized planning criteria via surveys. Data were analyzed using Triangular Fuzzy Numbers and defuzzification, ensuring consensus on effectiveness metrics. Expert feedback, collected through Likert scales and open-ended responses, informed iterative framework improvements.
Metadata
| Item Type: | Book Section |
|---|---|
| Creators: | Creators Email / ID Num. Roslin, Adam Irman UNSPECIFIED Mohd Dan, Azwa Suraya UNSPECIFIED Sazali, Razif UNSPECIFIED Md Yusoff, Yusandra UNSPECIFIED Zulqarnain, Muhammad UNSPECIFIED Haziq, Amrun UNSPECIFIED Linoby, Adam UNSPECIFIED |
| Subjects: | G Geography. Anthropology. Recreation > GV Recreation. Leisure > Sports |
| Divisions: | Universiti Teknologi MARA, Negeri Sembilan > Seremban Campus |
| Keywords: | Personalized training, Nutrition planning, Fuzzy Delphi method, AI chatbot framework, Prompt engineering |
| Date: | February 2025 |
| URI: | https://ir.uitm.edu.my/id/eprint/116213 |
- Edit Item
![[thumbnail of 116213.pdf]](https://ir.uitm.edu.my/style/images/fileicons/text.png)
