150142 VU Methods in East Asian Studies: The use of AI for thesis development (2025S)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 10.02.2025 10:00 bis Mi 26.02.2025 10:00
- Abmeldung bis Mo 31.03.2025 23:59
Details
max. 25 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- Dienstag 04.03. 13:15 - 14:45 Seminarraum Ostasienwissenschaften 1 UniCampus Hof 5 2I-O1-05
- Dienstag 11.03. 13:15 - 14:45 Seminarraum Ostasienwissenschaften 1 UniCampus Hof 5 2I-O1-05
- Dienstag 18.03. 13:15 - 14:45 Seminarraum Ostasienwissenschaften 1 UniCampus Hof 5 2I-O1-05
- Dienstag 25.03. 13:15 - 14:45 Seminarraum Ostasienwissenschaften 1 UniCampus Hof 5 2I-O1-05
- Dienstag 01.04. 13:15 - 14:45 Seminarraum Ostasienwissenschaften 1 UniCampus Hof 5 2I-O1-05
- Dienstag 08.04. 13:15 - 14:45 Seminarraum Ostasienwissenschaften 1 UniCampus Hof 5 2I-O1-05
- N Dienstag 29.04. 13:15 - 14:45 Seminarraum Ostasienwissenschaften 1 UniCampus Hof 5 2I-O1-05
- Dienstag 06.05. 13:15 - 14:45 Seminarraum Ostasienwissenschaften 1 UniCampus Hof 5 2I-O1-05
- Dienstag 13.05. 13:15 - 14:45 Seminarraum Ostasienwissenschaften 1 UniCampus Hof 5 2I-O1-05
- Dienstag 20.05. 13:15 - 14:45 Seminarraum Ostasienwissenschaften 1 UniCampus Hof 5 2I-O1-05
- Dienstag 27.05. 13:15 - 14:45 Seminarraum Ostasienwissenschaften 1 UniCampus Hof 5 2I-O1-05
- Dienstag 03.06. 13:15 - 14:45 Seminarraum Ostasienwissenschaften 1 UniCampus Hof 5 2I-O1-05
- Dienstag 10.06. 13:15 - 14:45 Seminarraum Ostasienwissenschaften 1 UniCampus Hof 5 2I-O1-05
- Dienstag 17.06. 13:15 - 14:45 Seminarraum Ostasienwissenschaften 1 UniCampus Hof 5 2I-O1-05
- Dienstag 24.06. 13:15 - 14:45 Seminarraum Ostasienwissenschaften 1 UniCampus Hof 5 2I-O1-05
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
Workload
Despite the strategic direction of this course being clear and established, the details will be highly path dependent. We will first brainstorm the whole project and determine the best way to proceed. This will include questions like what software to use, whether and how to train that software, how to test it, and how to make it available to the end users. Students will be required to actively participate in all sessions of the course, take on individual assignments, and work in teams focused on single tasks.
Despite the strategic direction of this course being clear and established, the details will be highly path dependent. We will first brainstorm the whole project and determine the best way to proceed. This will include questions like what software to use, whether and how to train that software, how to test it, and how to make it available to the end users. Students will be required to actively participate in all sessions of the course, take on individual assignments, and work in teams focused on single tasks.
Mindestanforderungen und Beurteilungsmaßstab
Assessment
Grading will be done based on the commitment and contribution of the students to the tasks as outlined above. Attendance is mandatory. There will be specific assignments that will be graded individually, and in case of team assignments the performance of the team will be graded. The final grade for the course will be calculated as a weighted arithmetic average of the grades for these various components.Attendance
This course is highly interactive cand participatory. Regular attendance is essential. Attending at least 80% of all in-class sessions is a minimal requirement for passing the course.Registration
You have registered for this course via the University of Vienna's electronic registration system. This registration, combined with attendance at least one session, constitutes a legally binding registration for examination. In other words, you will receive a grade. De-registration from the course is possible until the deadline set by the University of Vienna's central teaching administration. If you withdraw from the course after this deadline, you will still be graded.
--> Please note: Maintaining your registration for the course will be regarded as agreement with these terms.
Grading will be done based on the commitment and contribution of the students to the tasks as outlined above. Attendance is mandatory. There will be specific assignments that will be graded individually, and in case of team assignments the performance of the team will be graded. The final grade for the course will be calculated as a weighted arithmetic average of the grades for these various components.Attendance
This course is highly interactive cand participatory. Regular attendance is essential. Attending at least 80% of all in-class sessions is a minimal requirement for passing the course.Registration
You have registered for this course via the University of Vienna's electronic registration system. This registration, combined with attendance at least one session, constitutes a legally binding registration for examination. In other words, you will receive a grade. De-registration from the course is possible until the deadline set by the University of Vienna's central teaching administration. If you withdraw from the course after this deadline, you will still be graded.
--> Please note: Maintaining your registration for the course will be regarded as agreement with these terms.
Prüfungsstoff
See Moodle.
Literatur
See Moodle.
Zuordnung im Vorlesungsverzeichnis
WM4
Letzte Änderung: Sa 08.02.2025 21:06
The purpose of this course is to develop an AI supported tool to help EcoS students with the writing of their term papers and master’s theses (henceforth: thesis/theses). The focus will be on the theoretical part of such theses according to the standard EcoS structure. The single components are: (1) finding a good topic and research question; (2) demonstrating the relevance of the research question; (3) creating an appropriate structure for the literature review; (4) writing the literature review; (5) turning the learnings into a high-quality analytical framework.
The final goal of this course is the development of a simple-to-use, AI-based tool for students to get feedback on their submissions for each of these five components.