(in Polish) Workshop on gAI: Using New Technologies in Humanities and Social Studies 3700-ISSC-gAI-WOR
The rapid emergence of generative AI since 2022 has transformed the landscape of academic work, writing, research, and creativity in ways that are still unfolding. This workshop is designed specifically for students in humanities and social sciences, where both enthusiasitc adoption and full rejection are equally inadequate responses. The course proceeds through four themes. Glossary. We begin by building a shared vocabulary: what LLMs actually are, how they work, and what terms like “hallucination,” “prompt,” “alignment,” or “sycophancy” actually mean. Reading: Science, Pop-Science, and Science Fiction. Students read a selection of current and very recent non-fiction, from AI researchers, philosophers, and journalists, plus a selection of SF short fiction and novellas - for thinking about AI personhood, agency, and social consequence. Practice: Hands-On with gAI Applications. The largest component. Students experiment individually and collectively with chat-based LLMs, image generation, music generation, research-support tools, and data processing applications. We may even build an agent. Ethics Perspective. Omnipresent throughout the course: authorship, intellectual property, labor displacement, environmental cost, epistemic risk (slop, hallucination, contamination, laziness), and the growing aspects of access (with looming monetization of LLM). The workshop prioritizes active, experimental engagement over passive reception. Students are expected to try, fail, reflect, and iterate.
Course coordinators
Learning outcomes
Learning Outcomes
Upon completing the course, the student:
Knowledge
• P_W01 - understands generative AI as a driver of contemporary social change and can situate it within multi-disciplinary theoretical frameworks. (K_W02)
• P_W02 - knows and correctly uses core gAI terminology (LLM, prompt, hallucination, alignment, emergent behaviour, bias) and understands the interdependencies between its technical, economic, and humanistic dimensions. (K_W04)
• P_W03 - knows advanced gAI-enabled methods of data analysis and presentation (multimodal generation, automated research support, agent-based workflows) and can situate them critically within social research practice. (K_W08)
• P_W04 - knows the key methods of interdisciplinary inquiry applied to gAI, drawing on computer science, philosophy, cultural studies, and science and technology studies. (K_W09)
Abilities
• P_U01 - is able to critically assess information produced by or about gAI systems, identifying hallucinations, slop, and epistemic contamination across source types. (K_U01)
• P_U02 - is able to use gAI tools to organise, process, and present research data, and is aware of the possibilities and limitations of AI-assisted workflows. (K_U02)
• P_U03 - is able to present individual and group work on gAI topics in appropriate academic form while adhering to ethical principles governing authorship and transparency of AI use. (K_U06)
• P_U04 - is able to produce popular-science and applied texts on gAI topics addressed to a broad non-specialist audience. (K_U08)
• P_U05 - is able to plan and carry out collaborative practical projects using gAI tools, including goal-setting, role distribution, and critical evaluation of collective outputs. (K_U11)
Social Competences
• P_K01 - is ready to engage in teamwork, including research-oriented collaboration, on questions raised by generative AI. (K_K02)
Assessment criteria
Continuous Assessment (60%)
• Active participation in seminar discussions and lab sessions: 30%
• At least one presentation (15 minutes), on a topic connected to a present theme: 30%
Final Project (40%)
• A comprehesieva SWOT analysis of a chosen gAI-based app: 40%