2026/2027

Качественные методы анализа данных с использованием языковых моделей (LLMs)

Статус: Маго-лего

Кто читает: Департамент политики и управления

Где читается: Факультет социальных наук

Когда читается: 1, 2 модуль

Охват аудитории: для всех кампусов НИУ ВШЭ

Преподаватели: Порецкова Анастасия Анатольевна

Язык: английский

Кредиты: 3

Контактные часы: 32

Course Syllabus

Abstract

This course develops practical and methodological competence in using Large Language Models (LLMs) to support qualitative data analysis. Students learn to integrate LLMs across several analytic traditions: thematic analysis (including the AI-supported frameworks GAITA, NITA, and CAAI), grounded theory, narrative analysis, and frame analysis. The course grounds all applied work in established qualitative methodology and in deliberate prompt design, so that the analysis stays transparent, reproducible, and defensible for reporting and peer-reviewed publication.

Learning Objectives

• ground LLM-assisted analysis in established qualitative methodology
• select and apply an appropriate approach (thematic, grounded theory, narrative, or frame) for a given research question and dataset
• design, document, and calibrate prompts so that the analysis can be reproduced and scrutinised
• critically evaluate the outputs of different models and tools, and report the use of AI in line with academic and ethical standards

Expected Learning Outcomes

• Explain what LLMs are and why their architecture suits qualitative methodology, language-centred inquiry
• Design, document, and calibrate prompts (including system prompts) for qualitative analytic goals
• Conduct LLM-assisted thematic coding (inductive and deductive) and build a transparent codebook for research
• Compare the behaviour of different models and AI-enabled CAQDAS tools, and assess reproducibility and reliability across them
• Apply at least three AI-supported thematic frameworks (GAITA, NITA, CAAI) and compare their assumptions and outputs
• Maintain the centrality of the researcher, critically appraising AI outputs rather than naturalizing them
• Apply grounded-theory coding (open, axial, selective) with LLM support, using constant comparison and memo-writing, and assess where the model aids versus limits theory-building
• Apply narrative analysis with LLMs, coding stories holistically and operationalizing theory as a codebook
• Apply frame analysis with LLMs, operationalizing a frame scheme as a codebook and distinguishing framing from emotional language

Course Contents

• Prompt engineering foundations for qualitative analysis
• Coding with LLMs: levels of abstraction, calibration, and reliability
• Three frameworks for AI-supported thematic analysis: GAITA, NITA, CAAI
• Grounded theory with LLMs
• Narrative analysis with LLMs
• Frame analysis with LLMs

Assessment Elements

• Group project: qualitative analysis
  Carried out individually or in groups of up to three students. A genuine qualitative analysis on a chosen dataset using one of the studied frameworks or methods, plus a live group presentation of the process and results. The project must demonstrate two things at once: (a) sound qualitative analysis (coding, theme or category development, connection to theory), and (b) competent work with different models and tools (comparison of at least two models or AI-CAQDAS tools, prompt calibration, a documented workflow). Groups present live; the project grade is shared by the group.
• Individual reflective report
• Seminar activity
• Mini-task: system-prompt design and calibration

Interim Assessment

• 2026/2027 2nd module
  0.4 * Group project: qualitative analysis + 0.2 * Seminar activity + 0.2 * Individual reflective report + 0.2 * Mini-task: system-prompt design and calibration

Bibliography

Recommended Core Bibliography

• Readme first for a user's guide to qualitative methods, Richards, L., Morse, J.M., 2013

Recommended Additional Bibliography

• Kathy Charmaz. (n.d.). Advances in Qualitative Methods Conference Premises, Principles, and Practices in Qualitative Research: Revisiting the Foundations. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.5A292B7C

Authors

• Poretskova Anastasiia Anatolevna