Type of Credit: Elective
Credit(s)
Number of Students
This course explores the application of artificial intelligence (AI) in the field of digital humanities, encompassing education, literature, history, and philosophy. It will cover core concepts in digital humanities research and demonstrate how AI technologies can be used to solve research problems, analyze data, and provide new perspectives. Through a combination of theoretical discussions and practical exercises, students will learn how to integrate AI technologies into digital humanities research, enhancing both research efficiency and depth.
This course is designed in collaboration with multiple digital humanities-related projects and NCCU Innofest. Therefore, students enrolling in this course are required to participate in project exhibitions and workshops to showcase their course outcomes.
In addition, this class involves a lot of Python, so I expect students who take this course to have a basic understanding of Python and the operation of Google Colab.
能力項目說明
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
| Week | Date | Topic | Content | Study | |
| 1 | 2月18日 | Course Introduction | Definitions, scope, and challenges of digital humanities and AI; course expectations and structure. | ||
| 2 | 2月25日 | Fundamentals of AI Technologies | Digitization processes and methods in the humanities (text analysis, corpus studies, network analysis, etc.). | Jänicke, Stefan, et al. "On Close and Distant Reading in Digital Humanities: A Survey and Future Challenges." EuroVis (STARs) 2015 (2015): 83-103. | |
| 3 | 3月4日 | Fundamentals of AI Technologies | Introduction to AI tools, including LLMs, NLP and CV tools. | ||
| 4 | 3月11日 | Fundamentals of AI Technologies | Time series analysis, historical map generation, and data visualization tools. | ||
| 5 | 3月18日 | The Allegory of the Cave | Learning the Allegory of the Cave with AI | ||
| 6 | 3月25日 | The Allegory of the Cave | Using NLP to evaluate the quality of course reflection texts | 1. Devlin, J. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.2. Gou, J., Yu, B., Maybank, S. J., & Tao, D. (2021). Knowledge distillation: A survey. International Journal of Computer Vision, 129(6), 1789-1819. | |
| 7 | 4月1日 | Flexible | |||
| 8 | 4月8日 | The Allegory of the Cave | Correlation analysis between text quality and performance, and the problem of human bias | ||
| 9 | 4月15日 | Guest Lecture | (tentative) Topics related to education or philosophy | ||
| 10 | 4月22日 | Richard III | Who is Richard III? | ||
| 11 | 4月29日 | Richard III | Using Generative AI to uncover the mystery of Richard III | Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., ... & Wang, H. (2023). Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997. | |
| 12 | 5月6日 | NCCU Innofest | Designing and planning projects that integrate digital humanities with AI technologies. | ||
| 13 | 5月13日 | Along the River During the Qingming Festival | History of Along the River During the Qingming Festival | ||
| 14 | 5月20日 | 校慶 | |||
| 15 | 5月27日 | Along the River During the Qingming Festival | Using CV technology to explore the content of Along the River During the Qingming Festival | Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., & Terzopoulos, D. (2021). Image segmentation using deep learning: A survey. IEEE transactions on pattern analysis and machine intelligence, 44(7), 3523-3542. | |
| 16 | 6月2日 | NCCU Innofest | Students present their final research projects and analysis reports. | ||
| 17 | 6月10日 | Flexible | |||
| 18 | 6月17日 | Final Course Review and Reflection | Summarizing course insights, reflecting on learning outcomes, and exploring next steps for research or practice. | ||