Teaching Methodologies
The teaching methodologies adopted in this course unit include theoretical-practical classes, where content is structured, followed by moments of analysis, discussion, and debate to deepen the topics. The interrogative and interactive methodology stimulates critical thinking, encouraging active student participation before introducing fundamental concepts. The demonstration and exemplification of content are applied through practical use in real con-authentics, mainly using digital tools, artificial intelligence, and communication platforms in nutrition and health. The analysis of case studies will allow the exploration of innovative technological applications in nutrition and metabolism, as well as the evaluation of the impact of new technologies on professional practice. Additionally, integrative exercises are developed that relate specific professional situations to theoretical concepts, enabling students to apply acquired knowledge to real-world scenarios. The conduct of guided debates and discussions will contribute to exploring different perspectives on the challenges and opportunities presented by new technologies in nutrition, promoting a critical and multidimensional understanding of the topic. These methodologies ensure a dynamic and applied learning experience, facilitating the development of essential skills for using and evaluating digital technologies in the fields of nutrition, food, and health.
The assessment of the course unit reflects this theoretical-practical approach, allowing students to demonstrate their understanding of the content and their ability to apply it critically. The periodic evaluation consists of A summative written exam (60%) with open-ended questions to evaluate students’ analytical ability regarding the impact of digital technologies and artificial intelligence on nutrition and health. A group project (40%), where a critical analysis of a technological application in nutrition and metabolism will be conducted, encouraging collaborative work and content application to real-world contexts. Alternatively, students may opt for a final written exam with open-ended questions. The passing grade will follow the current academic regulations, requiring a minimum score of 9.5/20.
Learning Results
At the end of the course unit, students will be able to:
1.Understand the impact of new technologies and artificial intelligence on nutrition, metabolism, and health.
2.Apply digital tools and artificial intelligence to analyse and manage nutritional and metabolic data.
3.Understand and utilise digital communication techniques in Nutrition, Food, and Health.
4.Critically evaluate the use of emerging technologies in professional nutrition practice.
Program
1. New Technologies and Artificial Intelligence in Nutrition, Metabolism, and Health
2. Digital Tools and Artificial Intelligence in Nutritional and Metabolic Data Analysis and Management
3. Digital Communication in Nutrition, Food, and Health
4. Critical Evaluation of Emerging Technologies in Professional Nutrition Practice
Internship(s)
NAO
Bibliography
Géron, A. (2022). Hand-On Machine Learning with Scikit-Learn & TensorFlow (third edition). O’Reilly Media Inc.??
Goldmeier, J. (2023). Data Smart: Using Data Science to Transform Information into Insight (2nd edition). Wiley.
Kelleher, J., Mac Namee, B. and D’Arcy, A. (2020). Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (2nd edition). MIT Press.
Artigos selecionados sobre tópicos relevantes para os conteúdos programáticos.