Course Introduction
Large Language Models for Qualitative Data Analysis Training Course provides researchers and analysts with practical knowledge for applying advanced artificial intelligence to qualitative data. Participants will learn how large language models can support the analysis of interviews, focus groups, open-ended surveys, documents, transcripts, observations, and other rich sources of textual information.
The course examines how large language models can assist with important qualitative research activities such as coding, categorisation, theme identification, summarisation, comparison, interpretation, and synthesis. Participants will explore ways to integrate AI-supported analysis into established qualitative methodologies without treating automated outputs as substitutes for researcher judgement, contextual understanding, or methodological rigour.
Particular attention is given to prompt design, structured instructions, iterative analysis, contextual grounding, and evaluation of model outputs. Participants will learn how to develop effective workflows for processing qualitative data while maintaining consistency across large datasets. The course also explores how researchers can use AI to identify patterns and generate analytical possibilities while retaining control over coding decisions and interpretations.
Modern large language models can process substantial volumes of qualitative information, but they also introduce important methodological challenges. Participants will examine hallucinations, inconsistent coding, contextual misunderstanding, confirmation bias, automation bias, model sensitivity, and other factors that can influence AI-assisted qualitative analysis. Strategies for human validation and systematic quality assurance are therefore incorporated throughout the programme.
The course also addresses ethical and governance considerations associated with using AI in qualitative research. Topics include confidentiality, privacy, personally identifiable information, informed consent, data security, intellectual property, responsible prompting, research transparency, and the documentation of AI involvement. Participants will consider how these issues affect the design and implementation of responsible AI-supported research.
By completing the Large Language Models for Qualitative Data Analysis Training Course, participants will be better prepared to incorporate generative AI into qualitative research workflows in a structured and responsible manner. The course connects emerging AI capabilities with established research principles, helping participants improve analytical efficiency while preserving methodological transparency, contextual sensitivity, and researcher oversight.
Duration
5 days
Who Should Attend
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Qualitative researchers conducting interviews, focus groups, observations, or document-based studies
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University academics integrating generative AI into qualitative and mixed-methods research
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Postgraduate students analysing thesis and dissertation data using qualitative methodologies
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Social scientists working with large collections of transcripts, narratives, interviews, and open-ended responses
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Market researchers analysing consumer interviews, feedback, reviews, and customer narratives
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Policy researchers examining stakeholder submissions, consultation responses, reports, and public commentary
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Research consultants conducting thematic analysis, content analysis, and evidence synthesis projects
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Monitoring and evaluation professionals analysing qualitative programme and stakeholder data
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Public health researchers working with interviews, patient narratives, focus groups, and open-ended responses
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Research librarians and knowledge professionals supporting AI-assisted document and qualitative analysis
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Data analysts seeking to incorporate large language models into text-heavy research workflows
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Researchers and professionals responsible for developing ethical and reproducible AI-supported analytical processes
Course Objectives
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Develop a comprehensive understanding of large language models and their applications in qualitative data analysis across academic, professional, and applied research settings.
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Enable participants to identify appropriate qualitative research tasks where large language models can provide analytical support without replacing researcher interpretation and methodological judgement.
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Apply effective prompt engineering techniques to support coding, categorisation, summarisation, comparison, theme discovery, and structured interpretation of qualitative datasets.
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Develop practical workflows for analysing interviews, focus groups, survey responses, documents, transcripts, and other textual materials using AI-assisted qualitative methods.
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Explore strategies for maintaining consistency, contextual sensitivity, and analytical transparency when using large language models to process extensive qualitative datasets.
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Enable participants to critically evaluate AI-generated coding, themes, summaries, interpretations, and classifications through systematic human validation and methodological quality assurance.
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Identify and manage hallucinations, bias, contextual errors, inconsistent outputs, automation bias, prompt sensitivity, and other limitations associated with AI-assisted qualitative analysis.
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Examine ethical requirements involving confidentiality, informed consent, privacy, sensitive information, intellectual property, data governance, and responsible use of research data.
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Develop transparent and reproducible approaches for documenting prompts, models, analytical decisions, validation processes, AI contributions, and researcher interventions throughout a project.
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Design an end-to-end AI-assisted qualitative analysis workflow that integrates large language models with established research methodologies and produces credible, defensible analytical findings.
Comprehensive Course Outline
Module 1: Foundations of Large Language Models for Qualitative Research
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Understanding large language models, generative AI, natural language processing, and their growing role in qualitative research
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Distinguishing AI-assisted analysis from conventional qualitative coding, thematic analysis, content analysis, and researcher-led interpretation
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Identifying qualitative research tasks that can appropriately benefit from large language model assistance and computational support
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Examining capabilities, limitations, emerging applications, and methodological considerations surrounding AI-supported qualitative research
Module 2: Preparing Qualitative Data for AI-Assisted Analysis
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Preparing interview transcripts, focus groups, survey responses, documents, field notes, and other qualitative datasets for language model processing
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Applying cleaning, formatting, segmentation, anonymisation, metadata management, and other preparation techniques before AI-assisted analysis
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Managing long transcripts and large datasets through chunking, hierarchical processing, structured inputs, and context-management strategies
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Developing systematic data preparation workflows that protect research context while improving consistency and analytical efficiency
Module 3: Prompt Engineering for Qualitative Data Analysis
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Designing clear, structured prompts that guide large language models through specific qualitative coding and analytical tasks
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Applying role instructions, analytical frameworks, examples, constraints, output schemas, and iterative prompting to improve analytical consistency
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Developing prompts for open coding, focused coding, categorisation, summarisation, comparison, theme identification, and analytical memo generation
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Testing and refining prompts systematically while documenting prompt variations, model responses, analytical decisions, and validation procedures
Module 4: AI-Assisted Coding and Categorisation
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Using large language models to support initial coding, descriptive coding, pattern coding, and categorisation across qualitative research materials
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Developing structured coding frameworks that combine researcher-defined codes with AI-assisted suggestions and classification processes
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Comparing AI-generated codes with researcher coding to identify similarities, discrepancies, omissions, contextual errors, and opportunities for refinement
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Establishing human-in-the-loop workflows that preserve researcher authority while benefiting from automated processing and large-scale coding support
Module 5: Theme Discovery and Qualitative Interpretation
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Applying large language models to identify recurring themes, concepts, relationships, patterns, narratives, and contrasting perspectives within qualitative data
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Supporting thematic analysis through AI-assisted theme generation, theme refinement, evidence grouping, and comparison across participant responses
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Exploring strategies for preserving participant voice, contextual meaning, contradictory evidence, minority perspectives, and nuanced qualitative interpretations
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Combining AI-generated analytical suggestions with established thematic frameworks and researcher-led interpretation to strengthen analytical depth
Module 6: Advanced Analysis of Large Qualitative Datasets
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Applying hierarchical analysis techniques to process extensive collections of interviews, documents, responses, and other qualitative materials
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Comparing themes and perspectives across demographic groups, cases, locations, time periods, organisations, or other analytical dimensions
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Using structured extraction to identify entities, relationships, events, concepts, viewpoints, and evidence from large qualitative datasets
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Exploring semantic comparison, clustering, embeddings, retrieval approaches, and other AI-supported methods for advanced qualitative investigation
Module 7: Validation, Reliability and Researcher Oversight
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Evaluating AI-generated codes, themes, summaries, and interpretations through systematic human review and methodological validation procedures
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Identifying hallucinations, fabricated evidence, incorrect interpretations, missing context, inconsistent coding, and other model-generated analytical errors
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Comparing outputs across prompts, models, analytical configurations, and human coders to investigate consistency and reliability
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Developing audit trails and quality assurance procedures that document researcher decisions, AI contributions, corrections, and analytical changes
Module 8: Ethics, Privacy, Security and Responsible AI
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Examining confidentiality, informed consent, privacy, data protection, and ethical requirements when processing sensitive qualitative research materials
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Identifying risks associated with uploading participant data, personal information, confidential documents, or restricted research materials to AI systems
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Addressing algorithmic bias, cultural bias, linguistic bias, representational limitations, and potential effects on qualitative interpretation
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Developing responsible AI governance practices covering data handling, access controls, documentation, transparency, accountability, and researcher oversight
Module 9: Reproducibility, Documentation and Research Reporting
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Developing transparent documentation practices for prompts, models, datasets, analytical stages, coding decisions, and AI-generated outputs
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Establishing reproducible workflows that enable researchers to understand, review, replicate, and audit AI-assisted qualitative analysis processes
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Reporting the role of large language models clearly in research methods, analytical procedures, limitations, and publication outputs
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Integrating AI-assisted analysis with qualitative software, research databases, coding frameworks, evidence repositories, and established research management practices
Module 10: Emerging AI Methods and Applied Qualitative Research Projects
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Designing an end-to-end qualitative research project incorporating large language models from data preparation through coding, interpretation, validation, and reporting
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Exploring emerging approaches including multimodal language models, agentic research systems, retrieval-augmented analysis, and AI-assisted evidence synthesis
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Examining developments in automated qualitative research, domain-specific language models, multilingual analysis, and context-aware AI research workflows
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Developing practical strategies for responsible adoption of emerging AI capabilities while maintaining methodological rigour, transparency, human oversight, and research integrity
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
