AI in Humanities and Social Sciences 2401-OG-EN-AiHaSS
1. Introduction
2. AI Basics and Research
3. Algorithms, Data & AI
4. Digital Social Sciences Tolls
5. AI and Behavioral Modeling
6. Prompt Engineering Basics and Mid
7. Advanced Prompt Engineering
8. Building AI Agents
9. AI Ethics and Bias
10. A Guest Class
11. Political and Social Implications of AI
12. Economic Impact of AI
13. AI and Earth Resources
14. Future AI Scenarios and Emerging Fields
15. Summary
Total student workload
Learning outcomes - knowledge
Learning outcomes - skills
Learning outcomes - social competencies
Course coordinators
Assessment criteria
Standards
1. You have to attend the class. Yyou may skip only 2 meetings after introduction class.
2. You have to be prepared, that is, be familiar with assigned readings and ready to the discussion. I don’t expect you’ll understand every assigned reading immediately, but I require that you’ll give a try.
3. Don’t raise your hand in class. I expect you to speak up when you have something interesting and contributing to say, but don’t speak over anyone and respect the opinions of others.
4. You can contact me during my office hours (and by appointment) and via email (wojtkowski@umk.pl).
Grading
1. A participation in the discussion (10 points): active class participation and familiarity with readings
2. Tasks and problems (10 points): active in-group participation in solving the practice-oriented case studies.
3. Presentation (10): 10-15 slides critical presentation on one of selected topics.
to 50% – fail
from 51% – satisfactory
from 61% – satisfactory plus
from 71% – good
from 81% – good plus
from 91% – very good
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: