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2024　Collaboration across STEM and Liberal Arts:AI design concept and technology that supports digital humanities[2]

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Academic unit or major
Humanities and social science courses
Instructor(s)
Yamamoto Hilofumi 
Class Format
Lecture / Exercise     
Media-enhanced courses
Day/Period(Room No.)
-
Group
-
Course number
LAH.C654
Credits
2
Academic year
2024
Offered quarter
3Q
Syllabus updated
2024/3/14
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

［Collaboration across STEM and Liberal Arts］are study group-type subjects that will start in 2024. Each time, students will engage in discussions with guest lecturers who are active leaders in their various fields. Together with participating doctoral graduate students, we will explore new developments and possibilities in convergence science.
The maximum number of students is 50 per class. If the number is exceeded, a lottery will be held.
Group work will be conducted in English, but Japanese may be used if consensus can be reached within the group. Moderators and instructors will give lectures in Japanese, to be translated by ZOOM's translation function.
Students will take an e-learning session on research ethics in the first class. Submission of the “session-completion certificate” is required.

Student learning outcomes

The goal is to think about and discuss applications of AI that contribute to digital humanities (digital archives, humanities domain science with information science applications) and to create a system using AI (LLM).

Keywords

AI, LLM, digital humanities, language, history, religion, education, culture, Github, digital archives, Python, OpenAI, API access, programming

Competencies that will be developed

✔ Specialist skills Intercultural skills ✔ Communication skills ✔ Critical thinking skills ✔ Practical and/or problem-solving skills

Class flow

ents will attend lectures and engage in discussions on digitization, digital technologies, perspectives, and research sites in a wide range of humanities fields. Next, based on the basic technologies of AI, students will take one target humanities area, develop an AI program that may contribute to the subject, give a presentation, and discuss the program. The code will be published on Github, and a report will be submitted with development objectives, results, and evaluation.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Orientation How to proceed with the class, rules, etc.
Class 2 Current status of digital humanities and the potential of AI. Design and planning of the system to be developed (1)
Class 3 Basic technologies for digital humanities Design and planning of the system to be developed (2)
Class 4 Current status and research examples of DH in the art and music domain Problems and solutions in the system under development (1)
Class 5 Current status and research examples of DH in archaeology, history, and religious domains Problems and solutions in the system under development (2)
Class 6 Current status and research examples of DH in the language and literature domain Evaluation of the developed system and discussion (1)
Class 7 Problems of using AI technology in DH Evaluation of the developed system and discussion (2)

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.

Textbook(s)

Fundamentals of Digital Humanities in the Western World
Institute for Humanities and Informatics (Supervisor), Naoki Kofu (Author, Editor), Jun Ogawa (Author, Editor)
Naoki Kokaze, Jun Ogawa, Munenori Oida, Soichi Nagano, Michio Yamanaka, So Miyagawa, Ikki Omukai, Kiyonori Nagasaki

Reference books, course materials, etc.

Introduction to Text Data Construction for the Humanities: Toward Compliance with the TEI Guidelines
Institute for Humanities and Informatics (Supervisor) Yuri Ishida / Ikki Omukai / Ayano Kofu / Kennobu Nagasaki / Hajime Miyagawa / Yoichiro Watanabe (eds.)

Assessment criteria and methods

Listen to each lecture seriously, participate in the discussion, and express constructive opinions (20%).
To develop a program by applying AI technology, demonstrate the result of the program development through presentation, and obtain evaluation (20%).
Students are expected to collaborate with each other to propose solutions to the program development (30%).
To publish the results on Github and submit a comprehensive report of the results for evaluation (30%).

Related courses

  • LAH.A551
  • LAH.A411

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Students should be able to use ChatGPT-4, and have already acquired basic knowledge and skills in Python programming and Github repository operations.
Students must be able to use Colab (formally known as "Colaboratory"), write and run Python in a browser, and present their AI systems.

Other

Students must be enthusiastic about program development, as there will be a lot of actual discussion and development work as well as lectures.

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