Information Package / Course Catalogue
| Course Code | : TUR645 |
| Course Type | : Area Elective |
| Couse Group | : Third Cycle (Doctorate Degree) |
| Education Language | : Turkish |
| Work Placement | : N/A |
| Theory | : 3 |
| Prt. | : 0 |
| Credit | : 3 |
| Lab | : 0 |
| ECTS | : 5 |
The aim of this course is to enable doctoral students to understand the role of Generative Artificial Intelligence (GenAI) technologies in academic research and publication processes; and to provide them with knowledge about the working principles, strengths and weaknesses, application areas, and limitations of large language models. The course aims to enable students to use generative AI tools ethically, transparently, and effectively in research problem development, literature review, academic writing, scientific communication, and publication processes. Furthermore, by examining the policies and principles of universities and scientific publishing organizations regarding the use of AI, the course aims to raise awareness of the use of AI within the framework of research integrity, academic honesty, and good scientific practices.
This lesson; It covers the basic concepts of generative artificial intelligence technologies, the working principles of large language models, and current approaches and applications for the use of artificial intelligence in academic research and writing processes. Within the scope of the course, artificial intelligence tools used in the development of research ideas, literature discovery, academic text creation, scientific communication and publication processes will be introduced. In addition, ethical principles regarding the use of artificial intelligence, university and journal policies, research integrity, copyrights, data privacy, risks such as bias, hallucination and misinformation production and strategies to reduce them will be discussed. The course aims to help students use productive artificial intelligence technologies consciously, critically and responsibly through applied examples and case studies.
| 1. | It can explain the basic working principles of generative artificial intelligence systems. |
| 2. | It can evaluate the strengths and weaknesses of large language models. |
| 3. | It can identify suitable application areas for artificial intelligence in academic research processes. |
| 4. | Artificial intelligence tools can be used effectively in literature review and academic writing processes. |
| 5. | It can explain the ethical and scientific responsibilities related to the use of artificial intelligence. |
| 6. | It can identify errors and risks arising from artificial intelligence. |
| 7. | It can implement transparent reporting principles regarding the use of artificial intelligence. |
| 1. | Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M. ve diğerleri (2023). Artificial Intelligence (AI): Multidisciplinary Perspectives on Emerging Challenges, Opportunities, and Agenda for Research, Practice and Policy. |
| 2. | Mollick, E. (2024). Co-Intelligence: Living and Working with AI. New York: Portfolio. |
| 3. | Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th Edition). Pearson. |
| 4. | Eaton, S. E. (2024). Teaching, Learning and Researching in the Age of Artificial Intelligence. University of Calgary Press. |
| 5. | Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (Ed.) (2023). Handbook of Artificial Intelligence in Education. |
| 6. | UNESCO (2023). Guidance for Generative AI in Education and Research. Committee on Publication Ethics (COPE). COPE Position Statement on Authorship and AI Tools. Elsevier. Generative AI Policies for Authors. |
| 7. | International Committee of Medical Journal Editors (ICMJE). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work. |
| Type of Assessment | Count | Percent |
|---|---|---|
| Report | 5 | %20 |
| Project | 2 | %20 |
| Midterm Examination | 1 | %20 |
| Final Examination | 1 | %40 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 14 | 1 | 3 | 56 |
| Project | 2 | 10 | 2 | 24 |
| Report | 5 | 3 | 2 | 25 |
| Midterm Examination | 1 | 8 | 1 | 9 |
| Final Examination | 1 | 10 | 1 | 11 |
| TOTAL WORKLOAD (hours) | 125 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | PÇ-8 | PÇ-9 | PÇ-10 | PÇ-11 | PÇ-12 | PÇ-13 | PÇ-14 | |
OÇ-1 | 4 | 4 | 1 | 5 | 5 | 2 | 4 | 5 | 5 | 4 | 5 | 4 | ||
OÇ-2 | 4 | 5 | 1 | 4 | 4 | 3 | 4 | 5 | 4 | 5 | 4 | 4 | 4 | |
OÇ-3 | 5 | 5 | 1 | 5 | 5 | 3 | 5 | 5 | 5 | 4 | 4 | 4 | 5 | |
OÇ-4 | 4 | 4 | 1 | 5 | 4 | 3 | 5 | 4 | 4 | 4 | 5 | 5 | 4 | |
OÇ-5 | 5 | 4 | 1 | 4 | 4 | 2 | 3 | 4 | 5 | 4 | 5 | 4 | 4 | |
OÇ-6 | 5 | 4 | 1 | 4 | 5 | 1 | 3 | 4 | 5 | 5 | 5 | 5 | 5 | |
OÇ-7 | 4 | 4 | 1 | 5 | 5 | 1 | 5 | 4 | 4 | 5 | 4 | 5 | 4 | |