The Significance of Artificial Intelligence in University Education System and Course Syllabuses
- 1 Coordination of Foreign Language, Okan University, Istanbul, Türkiye
- 2 Faculty of Pharmacy, Kocaeli Health and Technology University, Izmit, Türkiye
- 3 Kocaeli Health and Technology University, Izmit, Türkiye
Abstract
This research paper meticulously examines the profound and dynamic impact of Artificial Intelligence (A.I.) on the University Education System, with a specific focus on the integration of A.I. within course syllabuses spanning disciplines such as chemistry, medicine, pharmacy (being the chief working areas of authors SA and ME), and various professional branches. The research delves into the transformative nature of A.I. in education, emphasizing the imperative need for its adaptation to enhance the learning experience and better equip students for the ever-evolving demands of their chosen professions. Through a meticulous analysis, this paper explores the multifaceted aspects of incorporating A.I. into university courses (MIT and Harvard are among the best universities in terms of Organic Chemistry courses), addressing challenges, seizing opportunities, and outlining essential considerations for a successful implementation strategy and yielding the result of A.I. need in the course content and syllabus preparation and guidance since the span of knowledge of A.I. compared to a university professor will be much more.
- Alyahyan, E., & Düştegör, D. (2020). Predicting Academic Success in Higher Education: Literature Review and Best Practices. International Journal of Educational Technology in Higher Education, 17, Article No. 3. https://doi.org/10.1186/s41239-020-0177-7
- Chen, L., Chen, P., & Lin, Z. (2020). Artificial Intelligence in Education: A Review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510
- Chiu, T. K. F., & Chai, C. (2020). Sustainable Curriculum Planning for Artificial Intelligence Education: A Self-Determination Theory Perspective. Sustainability, 12, Article 5568. https://doi.org/10.3390/su12145568
- Järvelä, S., & Bannert, M. (2019). Temporal and Adaptive Processes of Regulated Lear n ing: What Can Multimodal Data Tell? Learning and Instruction.
- Järvelä, S., Malmberg, J., Haataja, E., Sobocinski, M., & Kirschner, P. A. (2021). What Multimodal Data Can Tell Us about the Students’ Regulation of Their Learning Process? Learning and Instruction, 72, Article ID: 101203. https://doi.org/10.1016/j.learninstruc.2019.04.004
- Kleinheksel, A. J., Rockich-Winston, N., Tawfik, H., & Wyatt, T. R. (2020). Demystifying Content Analysis. American Journal of Pharmaceutical Education, 84, 7113. https://doi.org/10.5688/ajpe7113
- Lahut, S., Ozes, B., Agar, S., & Basak, A. N. (2012). TDP-43 Proteinopathies: A New Player in Neurodegenerative Diseases with Defective Protein Folding/TDP-43 Proteinopatileri: Norodejeneratif konformasyon bozuklugu hastaliklarinda yeni bir oyuncu. Ar c hives of the Turkish Dermatology and Venerology, 18, 1-11. https://doi.org/10.4274/Tnd.58561
- Morrison, B. B., Quinn, B. A., Bradley, S., Buffardi, K., Harrington, B., Hu, H. H., Kallia, M., McNeill, F., Ola, O., Parker, M., Rosato, J., & Waite, J. (2021). Evidence for Teaching Practices that Broaden Participation for Women in Computing. In Proceedings of the 2021 Working Group Reports on Innovation and Technology in Computer Science Education (pp. 57-131). Association for Computing Machinery. https://doi.org/10.1145/3502870.3506568
- RAISE (2022). MIT AI Literacy Units. https://raise.mit.edu/resources.html
- Song, J., Zhang, L., Yu, J., Peng, Y., Ma, A., & Lu, Y. (2022). Paving the Way for Novices: How to Teach AI for K-12 Education in China. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 12852-12857. https://doi.org/10.1609/aaai.v36i11.21565
- Speziale, H. S., & Carpenter, D. R. (2011). Qualitative Research in Nursing: Advancing the Humanistic Imperative (5th ed.). Wolters Kluwer Health/Lippincott Williams & Wilkins.