SemesterSpring Semester, 2025
DepartmentMaster's Program in Global Communication and Innovation Technology, First Year Master's Program in Global Communication and Innovation Technology, Second Year
Course NameDigital Humanities and Artificial Intelligence
InstructorLU HSIN-TSE
Credit3.0
Course TypeElective
Prerequisite
Course Objective
Course Description
Course Schedule





































































































































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. 　

Teaching Methods
Teaching Assistant

Tiffany Hsu (tiffanyhsu0774@gmail.com)


Requirement/Grading

  • Class Participation (10%): Active participation in class discussions, group activities, and engagement with course content are essential for this component.

  • Assignments and Exercises (30%): Students are required to complete regular assignments and exercises, which may include individual and group tasks, as well as case study analyses.

  • Research Project and Report (20%): Each student or group will select a topic in digital humanities, apply AI technologies, and present their findings in a comprehensive report.

  • Final Presentation (40%): At the end of the course, students will showcase their research project, explaining their process, the application of AI technologies, and the outcomes during the final presentation session.


Textbook & Reference

 



 


Urls about Course
Attachment