Course Introduction
Generative AI for Interview and Focus Group Analysis Training Course provides researchers and professionals with practical skills for applying modern artificial intelligence to the analysis of interview transcripts and focus group discussions. Participants will learn how generative AI can support the organisation, coding, interpretation, comparison, and synthesis of rich qualitative information.
Interview and focus group data often contain extensive narratives, diverse perspectives, contextual details, emotional expressions, and contradictory viewpoints that require careful interpretation. This course introduces structured AI-assisted approaches for transforming lengthy transcripts into manageable analytical units while preserving participant context, meaning, nuance, and the connection between evidence and research questions.
Participants will explore how generative AI can support qualitative activities such as transcript summarisation, initial coding, codebook development, thematic analysis, category generation, quote identification, comparison of participant perspectives, and analytical memo creation. Emphasis is placed on using AI as a research-support tool while retaining researcher oversight and methodological control throughout the analytical process.
The course also addresses prompt engineering and workflow design for qualitative research. Participants will learn how to create precise instructions, establish analytical frameworks, manage lengthy transcripts, use structured outputs, conduct iterative analysis, and compare AI-generated findings with human interpretation. Practical approaches for improving consistency and reducing analytical errors will be explored.
Responsible use of generative AI is a central component of the programme. Participants will examine confidentiality, participant privacy, informed consent, data security, bias, hallucinations, fabricated quotations, contextual misunderstanding, and other risks associated with processing sensitive qualitative data using AI systems. Strategies for validation, audit trails, transparency, and ethical governance are incorporated throughout the course.
By completing the Generative AI for Interview and Focus Group Analysis Training Course, participants will be able to design practical and responsible AI-assisted workflows for qualitative research. The course connects generative AI capabilities with established research methods, enabling participants to increase analytical efficiency while maintaining contextual sensitivity, methodological transparency, participant protection, and researcher judgement.
Duration
5 days
Who Should Attend
-
Qualitative researchers conducting interviews, focus groups, and narrative-based research
-
University lecturers and academics using qualitative and mixed-methods research approaches
-
Postgraduate students analysing interview and focus group data for dissertations and theses
-
Social scientists studying attitudes, experiences, perceptions, behaviours, and participant narratives
-
Market researchers analysing customer interviews, consumer discussions, and stakeholder perspectives
-
Policy researchers examining stakeholder interviews, consultations, and community discussions
-
Monitoring and evaluation professionals analysing programme interviews and focus group findings
-
Public health researchers working with patient interviews, community discussions, and participant narratives
-
Human resources professionals analysing employee interviews, engagement discussions, and organisational feedback
-
Research consultants conducting thematic analysis and qualitative evidence synthesis projects
-
Data analysts and research professionals seeking to incorporate generative AI into qualitative workflows
-
Research managers responsible for developing efficient, ethical, and reproducible interview analysis processes
Course Objectives
-
Develop a comprehensive understanding of generative AI and its practical applications for analysing interviews, focus groups, transcripts, and other qualitative research materials.
-
Enable participants to prepare, structure, anonymise, segment, and organise interview and focus group transcripts for effective and responsible AI-assisted analysis.
-
Apply generative AI techniques to support transcript summarisation, initial coding, categorisation, theme discovery, comparison, and synthesis of qualitative evidence.
-
Develop effective prompt engineering skills for creating precise, consistent, transparent, and research-appropriate instructions for AI-assisted interview and focus group analysis.
-
Enable participants to build structured coding frameworks and codebooks that combine established qualitative methodologies with AI-assisted coding and categorisation.
-
Explore practical approaches for identifying recurring themes, divergent opinions, participant perspectives, relationships, patterns, and meaningful contextual insights across multiple transcripts.
-
Develop methods for validating AI-generated summaries, codes, themes, interpretations, and quotations through systematic researcher review and qualitative quality assurance.
-
Identify and manage hallucinations, fabricated information, contextual misunderstandings, bias, inconsistent outputs, and other risks associated with generative AI in qualitative analysis.
-
Examine ethical requirements involving participant confidentiality, informed consent, privacy, data security, sensitive information, intellectual property, and responsible AI governance.
-
Design an end-to-end generative AI workflow for interview and focus group analysis that improves efficiency while preserving methodological rigour, transparency, participant context, and researcher judgement.
Comprehensive Course Outline
Module 1: Foundations of Generative AI for Qualitative Analysis
-
Understanding generative AI, large language models, natural language processing, and their applications in qualitative research
-
Distinguishing AI-assisted interview analysis from conventional manual coding, thematic analysis, content analysis, and interpretation
-
Identifying appropriate interview and focus group analysis tasks that can benefit from generative AI support
-
Examining the capabilities, limitations, emerging applications, and methodological implications of generative AI in qualitative research
Module 2: Preparing Interview and Focus Group Data
-
Preparing interview transcripts, focus group transcripts, field notes, participant responses, and supporting documents for AI-assisted analysis
-
Applying formatting, cleaning, anonymisation, speaker identification, segmentation, metadata management, and other transcript preparation techniques
-
Managing lengthy transcripts through structured chunking, hierarchical analysis, context preservation, and systematic document organisation
-
Developing reproducible preparation workflows that protect participant information while maintaining analytical context and research meaning
Module 3: Prompt Engineering for Interview Analysis
-
Designing structured prompts for transcript summarisation, coding, theme identification, comparison, categorisation, and evidence extraction
-
Applying role instructions, analytical frameworks, examples, constraints, output formats, and evaluation criteria to improve AI-generated results
-
Developing reusable prompt templates for different interview research objectives, methodologies, participant groups, and analytical stages
-
Testing and refining prompts systematically while documenting model responses, prompt variations, analytical decisions, and quality checks
Module 4: AI-Assisted Coding and Codebook Development
-
Using generative AI to support open coding, descriptive coding, focused coding, pattern coding, and other qualitative coding approaches
-
Developing and refining codebooks through interaction between researcher-defined concepts, participant evidence, and AI-generated coding suggestions
-
Comparing AI-generated codes with researcher coding to identify omissions, inconsistencies, contextual errors, and alternative interpretations
-
Establishing human-in-the-loop coding processes that improve efficiency while retaining researcher responsibility for analytical decisions
Module 5: Thematic Analysis of Interviews and Focus Groups
-
Using generative AI to identify recurring themes, subthemes, concepts, relationships, and patterns across qualitative transcripts
-
Supporting theme development through evidence grouping, thematic comparison, category refinement, and analytical memo generation
-
Preserving participant voice, contradictory evidence, minority perspectives, emotional context, and nuanced meanings during automated analysis
-
Integrating AI-assisted theme discovery with established qualitative frameworks to support systematic and defensible research interpretation
Module 6: Comparative and Cross-Participant Analysis
-
Comparing participant responses across interviews, focus groups, demographic categories, locations, organisations, or other meaningful research dimensions
-
Identifying areas of agreement, disagreement, convergence, divergence, and variation across participant perspectives and experiences
-
Using generative AI to structure comparative matrices, evidence summaries, participant profiles, and cross-case analytical observations
-
Examining contextual differences and avoiding inappropriate generalisations when comparing qualitative findings across different participant groups
Module 7: Validation, Accuracy and Researcher Oversight
-
Evaluating AI-generated summaries, codes, themes, interpretations, and extracted quotations through systematic researcher review
-
Identifying hallucinations, fabricated quotations, unsupported conclusions, missing context, incorrect attribution, and other AI-generated analytical errors
-
Comparing outputs across prompts, models, analytical configurations, and human reviewers to investigate consistency and reliability
-
Creating audit trails that document AI contributions, researcher corrections, analytical decisions, validation evidence, and changes throughout the research process
Module 8: Ethics, Privacy, Confidentiality and Responsible AI
-
Examining informed consent, participant confidentiality, privacy, data protection, and ethical requirements for AI-assisted qualitative research
-
Identifying risks associated with submitting identifiable interviews, sensitive focus group discussions, confidential information, or restricted research materials to AI systems
-
Addressing algorithmic, cultural, linguistic, and representational biases that may affect interpretation of participant responses
-
Developing responsible governance procedures for secure data handling, access management, transparency, accountability, and human oversight
Module 9: Reporting, Documentation and Reproducible Workflows
-
Documenting prompts, models, datasets, analytical stages, coding decisions, AI outputs, validation procedures, and researcher interventions
-
Developing reproducible workflows that allow qualitative researchers to review, audit, explain, and where appropriate replicate AI-assisted analytical processes
-
Reporting generative AI involvement transparently within research methods, analytical procedures, findings, limitations, and publications
-
Integrating AI-assisted analysis with qualitative research software, coding frameworks, evidence repositories, and established research management systems
Module 10: Applied Projects and Emerging Generative AI Methods
-
Designing an end-to-end interview or focus group analysis project incorporating generative AI from transcript preparation through reporting
-
Exploring emerging approaches including multimodal AI, agentic research systems, retrieval-augmented analysis, and automated qualitative evidence synthesis
-
Examining multilingual interview analysis, domain-specific language models, context-aware prompting, and advanced AI-supported qualitative workflows
-
Developing practical strategies for adopting emerging generative AI capabilities while maintaining methodological rigour, transparency, ethics, and researcher control
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.
