Course Detail
Course Detail
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| DIGITALIZATION and AI in NUTRITION and DIETETICS | BES3216429 | Spring Semester | 1+1 | 1,5 | 3 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | Turkish |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Elective |
| Course Coordinator | Lect. Merve GÜNEY COŞKUN |
| Name of Lecturer(s) | Lect. Merve GÜNEY COŞKUN |
| Assistant(s) | |
| Aim | The aim of this course is to enable students to understand the digitalization processes and artificial intelligence applications in the field of nutrition and dietetics, to learn how to integrate these technologies into professional practice, and to comprehend the digital transformation of contemporary health services. |
| Course Content | This course contains; Introduction, Course Overview and the Importance of Digitalization in Nutrition,Introduction to Digital Health Technologies and Basic Concepts,Fundamentals of Artificial Intelligence and Machine Learning,Mobile Health (m-Health) Applications in Nutrition,Student Presentations: Examples of Mobile Health Applications (Posters),Student Presentations: Examples of Mobile Health Applications (Posters),Developing Nutrition Materials and Recipes Using Artificial Intelligence,Personalized Nutrition: AI-Supported Diet Planning Systems,Use of AI in Scientific Writing: Application Areas and Limitations,AI-Based Case Studies in Nutrition (Obesity, Type 2 Diabetes, Allergy),Assessing Nutritional Status with Digital Methods,Data Privacy, Ethical Issues, and Artificial Intelligence,Virtual Reality (VR), Augmented Reality (AR) and Their Use in Nutrition Education,Emerging Trends and Future Perspectives – General Course Evaluation and Student Feedback. |
| Course Learning Outcomes | Teaching Methods | Assessment Methods |
| Define the concepts of digital health technologies and artificial intelligence. | 3, 4 | A |
| Analyze digital tools and applications used in the field of nutrition. | 3, 4 | A, F |
| Evaluate AI-assisted personalized nutrition plans and explain the areas of application of AI-based systems | 3, 4 | A, F |
| Follow current digital health trends and develop the ability to adapt them into professional practice. | 3, 4 | A, F |
| Discuss data security and ethical issues within the context of healthcare. | 3, 4 | A |
| Teaching Methods: | 3: Problem Baded Learning Model, 4: Inquiry-Based Learning |
| Assessment Methods: | A: Traditional Written Exam, F: Project Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction, Course Overview and the Importance of Digitalization in Nutrition | None |
| 2 | Introduction to Digital Health Technologies and Basic Concepts | Reading previous lecture notes |
| 3 | Fundamentals of Artificial Intelligence and Machine Learning | Reading previous lecture notes |
| 4 | Mobile Health (m-Health) Applications in Nutrition | Reading previous lecture notes |
| 5 | Student Presentations: Examples of Mobile Health Applications (Posters) | Conducting sample application research and preparing posters |
| 6 | Student Presentations: Examples of Mobile Health Applications (Posters) | Conducting sample application research and preparing posters |
| 7 | Developing Nutrition Materials and Recipes Using Artificial Intelligence | Reading previous lecture notes |
| 8 | Personalized Nutrition: AI-Supported Diet Planning Systems | Reading previous lecture notes |
| 9 | Use of AI in Scientific Writing: Application Areas and Limitations | Reading previous lecture notes |
| 10 | AI-Based Case Studies in Nutrition (Obesity, Type 2 Diabetes, Allergy) | Preparing sample case study |
| 11 | Assessing Nutritional Status with Digital Methods | Reading previous lecture notes |
| 12 | Data Privacy, Ethical Issues, and Artificial Intelligence | Reading previous lecture notes |
| 13 | Virtual Reality (VR), Augmented Reality (AR) and Their Use in Nutrition Education | Reading previous lecture notes |
| 14 | Emerging Trends and Future Perspectives – General Course Evaluation and Student Feedback | Reading recent articles |
| Resources |
| 1. Limketkai, B.N., Mauldin, K., Manitius, N. et al. The Age of Artificial Intelligence: Use of Digital Technology in Clinical Nutrition. Curr Surg Rep 9, 20 (2021). 2. Chatelan, A., Clerc, A., & Fonta, P. A. (2023). ChatGPT and future artificial intelligence chatbots: what may be the influence on credentialed nutrition and dietetics practitioners?. Journal of the Academy of Nutrition and Dietetics, 123(11), 1525-1531. 3. Armand, T. P. T., Nfor, K. A., Kim, J. I., & Kim, H. C. (2024). Applications of artificial intelligence, machine learning, and deep learning in nutrition: a systematic review. Nutrients, 16(7), 1073. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | PQ-1. They have basic knowledge in the field of Nutrition and Dietetics. | X | |||||
| 2 | PQ-2. Use vocational knowledge and skills for the development of health at individual and community level, for the prevention and treatment of diseases. | X | |||||
| 3 | PQ-3. Provide training and counseling in the field of nutrition and dietetics including individuals, families and organizations. | X | |||||
| 4 | PQ-4. Gains effective knowledge and skills in mass food service systems and manages processes such as menu planning, production, hygiene, and food safety. | X | |||||
| 5 | PQ-5. Carries out independent research on the field using knowledge accumulation, and takes part in research, projects and events in collaboration with the health team and other disciplines. | X | |||||
| 6 | PQ-6. Conducts profession in accordance with scientific researches, evidence, developing technology, current literature and health policies. | X | |||||
| 7 | PQ-7. Carry out its professional practices in line with ethical principles and standards. His behavior becomes an example to colleagues and public. | X | |||||
| 8 | PQ-8. Uses at least one foreign language to reach, evaluate and use it for professional development. | X | |||||
| 9 | PQ-9. Use critical thinking, problem solving and decision making skills in the field of nutrition and dietetics. | X | |||||
| 10 | PQ-10. Communicates effectively both verbally and written in occupational field. | X | |||||
| 11 | PQ-11. They have general cultural knowledge that profession requires. | X | |||||
| 12 | PQ-12. Develop positive attitudes toward lifelong learning and turn them into behavior. | X | |||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 40 | |
| Rate of Final Exam to Success | 60 | |
| Total | 100 | |
| ECTS / Workload Table | ||||||
| Activities | Number of | Duration(Hour) | Total Workload(Hour) | |||
| Course Hours | 14 | 1 | 14 | |||
| Guided Problem Solving | 0 | 0 | 0 | |||
| Resolution of Homework Problems and Submission as a Report | 0 | 0 | 0 | |||
| Term Project | 0 | 0 | 0 | |||
| Presentation of Project / Seminar | 2 | 10 | 20 | |||
| Quiz | 0 | 0 | 0 | |||
| Midterm Exam | 1 | 14 | 14 | |||
| General Exam | 1 | 20 | 20 | |||
| Performance Task, Maintenance Plan | 2 | 10 | 20 | |||
| Total Workload(Hour) | 88 | |||||
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(88/30) | 3 | |||||
| ECTS of the course: 30 hours of work is counted as 1 ECTS credit. | ||||||
Detail Informations of the Course
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| DIGITALIZATION and AI in NUTRITION and DIETETICS | BES3216429 | Spring Semester | 1+1 | 1,5 | 3 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | Turkish |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Elective |
| Course Coordinator | Lect. Merve GÜNEY COŞKUN |
| Name of Lecturer(s) | Lect. Merve GÜNEY COŞKUN |
| Assistant(s) | |
| Aim | The aim of this course is to enable students to understand the digitalization processes and artificial intelligence applications in the field of nutrition and dietetics, to learn how to integrate these technologies into professional practice, and to comprehend the digital transformation of contemporary health services. |
| Course Content | This course contains; Introduction, Course Overview and the Importance of Digitalization in Nutrition,Introduction to Digital Health Technologies and Basic Concepts,Fundamentals of Artificial Intelligence and Machine Learning,Mobile Health (m-Health) Applications in Nutrition,Student Presentations: Examples of Mobile Health Applications (Posters),Student Presentations: Examples of Mobile Health Applications (Posters),Developing Nutrition Materials and Recipes Using Artificial Intelligence,Personalized Nutrition: AI-Supported Diet Planning Systems,Use of AI in Scientific Writing: Application Areas and Limitations,AI-Based Case Studies in Nutrition (Obesity, Type 2 Diabetes, Allergy),Assessing Nutritional Status with Digital Methods,Data Privacy, Ethical Issues, and Artificial Intelligence,Virtual Reality (VR), Augmented Reality (AR) and Their Use in Nutrition Education,Emerging Trends and Future Perspectives – General Course Evaluation and Student Feedback. |
| Course Learning Outcomes | Teaching Methods | Assessment Methods |
| Define the concepts of digital health technologies and artificial intelligence. | 3, 4 | A |
| Analyze digital tools and applications used in the field of nutrition. | 3, 4 | A, F |
| Evaluate AI-assisted personalized nutrition plans and explain the areas of application of AI-based systems | 3, 4 | A, F |
| Follow current digital health trends and develop the ability to adapt them into professional practice. | 3, 4 | A, F |
| Discuss data security and ethical issues within the context of healthcare. | 3, 4 | A |
| Teaching Methods: | 3: Problem Baded Learning Model, 4: Inquiry-Based Learning |
| Assessment Methods: | A: Traditional Written Exam, F: Project Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction, Course Overview and the Importance of Digitalization in Nutrition | None |
| 2 | Introduction to Digital Health Technologies and Basic Concepts | Reading previous lecture notes |
| 3 | Fundamentals of Artificial Intelligence and Machine Learning | Reading previous lecture notes |
| 4 | Mobile Health (m-Health) Applications in Nutrition | Reading previous lecture notes |
| 5 | Student Presentations: Examples of Mobile Health Applications (Posters) | Conducting sample application research and preparing posters |
| 6 | Student Presentations: Examples of Mobile Health Applications (Posters) | Conducting sample application research and preparing posters |
| 7 | Developing Nutrition Materials and Recipes Using Artificial Intelligence | Reading previous lecture notes |
| 8 | Personalized Nutrition: AI-Supported Diet Planning Systems | Reading previous lecture notes |
| 9 | Use of AI in Scientific Writing: Application Areas and Limitations | Reading previous lecture notes |
| 10 | AI-Based Case Studies in Nutrition (Obesity, Type 2 Diabetes, Allergy) | Preparing sample case study |
| 11 | Assessing Nutritional Status with Digital Methods | Reading previous lecture notes |
| 12 | Data Privacy, Ethical Issues, and Artificial Intelligence | Reading previous lecture notes |
| 13 | Virtual Reality (VR), Augmented Reality (AR) and Their Use in Nutrition Education | Reading previous lecture notes |
| 14 | Emerging Trends and Future Perspectives – General Course Evaluation and Student Feedback | Reading recent articles |
| Resources |
| 1. Limketkai, B.N., Mauldin, K., Manitius, N. et al. The Age of Artificial Intelligence: Use of Digital Technology in Clinical Nutrition. Curr Surg Rep 9, 20 (2021). 2. Chatelan, A., Clerc, A., & Fonta, P. A. (2023). ChatGPT and future artificial intelligence chatbots: what may be the influence on credentialed nutrition and dietetics practitioners?. Journal of the Academy of Nutrition and Dietetics, 123(11), 1525-1531. 3. Armand, T. P. T., Nfor, K. A., Kim, J. I., & Kim, H. C. (2024). Applications of artificial intelligence, machine learning, and deep learning in nutrition: a systematic review. Nutrients, 16(7), 1073. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | PQ-1. They have basic knowledge in the field of Nutrition and Dietetics. | X | |||||
| 2 | PQ-2. Use vocational knowledge and skills for the development of health at individual and community level, for the prevention and treatment of diseases. | X | |||||
| 3 | PQ-3. Provide training and counseling in the field of nutrition and dietetics including individuals, families and organizations. | X | |||||
| 4 | PQ-4. Gains effective knowledge and skills in mass food service systems and manages processes such as menu planning, production, hygiene, and food safety. | X | |||||
| 5 | PQ-5. Carries out independent research on the field using knowledge accumulation, and takes part in research, projects and events in collaboration with the health team and other disciplines. | X | |||||
| 6 | PQ-6. Conducts profession in accordance with scientific researches, evidence, developing technology, current literature and health policies. | X | |||||
| 7 | PQ-7. Carry out its professional practices in line with ethical principles and standards. His behavior becomes an example to colleagues and public. | X | |||||
| 8 | PQ-8. Uses at least one foreign language to reach, evaluate and use it for professional development. | X | |||||
| 9 | PQ-9. Use critical thinking, problem solving and decision making skills in the field of nutrition and dietetics. | X | |||||
| 10 | PQ-10. Communicates effectively both verbally and written in occupational field. | X | |||||
| 11 | PQ-11. They have general cultural knowledge that profession requires. | X | |||||
| 12 | PQ-12. Develop positive attitudes toward lifelong learning and turn them into behavior. | X | |||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 40 | |
| Rate of Final Exam to Success | 60 | |
| Total | 100 | |