AI-Assisted Interview Transcription and Qualitative Coding Training Course

Course Introduction

AI-Assisted Interview Transcription and Qualitative Coding Training Course provides participants with the practical skills to transform recorded interviews, focus group discussions, key informant interviews, and open-ended survey responses into accurate, organized, and analytically useful qualitative data. The course introduces artificial intelligence and natural language processing techniques that support speech recognition, transcript preparation, text organization, preliminary coding, and thematic analysis. Participants learn to integrate these capabilities into established qualitative research workflows while preserving the meaning, context, and complexity of participant experiences.

The course examines the complete interview data-processing cycle, beginning with audio preparation, file organization, transcription requirements, and data management. Participants explore automated speech recognition, speaker identification, timestamps, punctuation restoration, and the handling of overlapping speech, background noise, accents, and multilingual conversations. Practical exercises demonstrate how to assess transcription quality, identify omissions and substitutions, correct technical errors, and maintain a reliable connection between original recordings and the corresponding transcripts. The emphasis is on producing usable research data rather than accepting automatically generated transcripts without verification.

Participants develop skills in preparing transcripts for qualitative analysis through consistent formatting, speaker labels, anonymization, text cleaning, and documentation of transcription conventions. The training addresses the differences between verbatim, intelligent verbatim, and edited transcripts, helping participants select an appropriate approach for the research question and analytical method. They also learn how to preserve pauses, emphasis, emotional expressions, local terminology, and other contextual information when these details are relevant to interpretation. Special attention is given to multilingual research, translation quality, code-switching, and the risks of losing meaning when speech is converted into standardized written language.

A major component of the course focuses on qualitative coding and thematic analysis using AI-assisted tools. Participants learn to develop deductive and inductive codes, construct codebooks, apply consistent coding rules, group related codes into categories, and develop themes grounded in the data. They practise using AI to suggest preliminary codes, identify recurring topics, retrieve potentially relevant passages, and compare patterns across participants or stakeholder groups. These techniques are examined alongside established qualitative approaches, with explicit attention to researcher reflexivity, contradictory accounts, minority perspectives, and the distinction between identifying recurring language and interpreting the significance of participants’ experiences.

The training integrates quality assurance, research ethics, and responsible AI use throughout the transcription and coding process. Participants examine confidentiality risks associated with uploading audio recordings, transcripts, or personal information to external platforms, and learn to assess tool suitability, data retention practices, access controls, consent requirements, and organizational policies before processing research material. They also practise checking automated outputs against source recordings, documenting human corrections, reviewing AI-generated interpretations, and protecting sensitive information through appropriate anonymization. The course reinforces the principle that researchers remain accountable for analytical decisions, the representation of participants, and the validity of reported findings.

By the end of the programme, participants will have completed a practical workflow that connects interview recordings to verified transcripts, structured codebooks, coded excerpts, thematic summaries, and a concise qualitative findings report. Through exercises using sample or appropriately authorized research data, they will learn to evaluate transcription accuracy, compare manual and AI-assisted coding, maintain an auditable analytical process, and present findings supported by traceable evidence. The acquired skills are intended to improve research efficiency and consistency while strengthening the credibility, transparency, and ethical integrity of qualitative studies, programme evaluations, stakeholder consultations, and organizational learning initiatives.

Duration

5 days

Who Should Attend

• Monitoring, Evaluation, Accountability and Learning (MEAL) professionals responsible for analyzing interviews, focus group discussions, stakeholder consultations, and qualitative feedback from programme participants.

• Qualitative researchers seeking to integrate AI-assisted transcription and text analysis into established research designs without compromising methodological rigour.

• Evaluation specialists and consultants who process interviews and key informant discussions for baseline studies, midterm reviews, endline evaluations, and impact assessments.

• Research officers and assistants responsible for audio transcription, transcript preparation, coding, thematic analysis, and qualitative data management.

• Social scientists and academic researchers conducting interview-based studies in development, public policy, education, health, governance, and related disciplines.

• Programme managers and technical advisers who need to interpret stakeholder experiences, implementation challenges, participant perceptions, and lessons learned.

• Accountability and community engagement officers analyzing complaints, suggestions, feedback narratives, and consultation records to improve programme responsiveness.

• Humanitarian assessment teams working with interviews and focus groups to understand community needs, service accessibility, protection concerns, and operational lessons.

• Monitoring and evaluation data analysts seeking to complement quantitative indicators with systematic analysis of narrative and textual information.

• Knowledge management and organizational learning specialists responsible for capturing institutional knowledge, synthesizing lessons, and documenting stakeholder experiences.

• Market and social research professionals who analyze customer interviews, user feedback, focus groups, and open-ended responses to identify emerging patterns.

• Public health researchers and programme officers working with qualitative interviews about service delivery, health-seeking behaviour, and community perceptions.

• Gender, disability, safeguarding, and social inclusion specialists who need to analyze sensitive qualitative data while protecting participant confidentiality and contextual meaning.

• Development practitioners and civil society organizations that collect community feedback and need practical methods for organizing and interpreting qualitative information.

• Project coordinators and research team leaders responsible for establishing consistent transcription standards, coding procedures, and qualitative quality assurance protocols.

• Academic postgraduate students undertaking dissertations, theses, or research projects involving interviews, focus groups, or thematic analysis.

• Data management specialists supporting research teams with secure document storage, transcript version control, metadata management, and qualitative analysis software.

• Policy analysts and evidence advisers who prepare qualitative findings, stakeholder insight reports, policy briefs, and recommendations for decision-makers.

Course Objectives

• Explain the principles of AI-assisted speech recognition and natural language processing, including their applications, limitations, and appropriate roles in qualitative research and evaluation.

• Prepare interview recordings for transcription by applying suitable file-management practices, audio-quality checks, transcription conventions, and documented data-handling procedures.

• Generate and verify machine-assisted transcripts by identifying speech-recognition errors, missing content, speaker attribution problems, and inaccuracies involving accents, technical terminology, or multilingual speech.

• Apply consistent transcript preparation procedures covering speaker labels, timestamps, punctuation, anonymization, translation notes, and formatting for subsequent qualitative analysis.

• Develop research-question-aligned coding frameworks and codebooks that define codes, categories, inclusion rules, exclusion rules, and examples for consistent application across transcripts.

• Use AI-assisted tools to propose preliminary codes, retrieve relevant text passages, identify recurring concepts, and support qualitative categorization while retaining researcher control over final coding decisions.

• Conduct thematic analysis by grouping related codes, examining patterns and differences, developing defensible themes, and grounding interpretations in verified participant statements.

• Evaluate transcription and coding quality through source checks, sample audits, coder comparisons, review of contradictory cases, and documentation of corrections and unresolved uncertainties.

• Apply ethical and responsible AI practices that address informed consent, confidentiality, data minimization, secure storage, access controls, bias, and transparent disclosure of AI assistance.

• Produce structured qualitative outputs, including coded transcripts, codebooks, thematic matrices, evidence-supported findings, and concise reports suitable for programme learning and evaluation decisions.

Comprehensive Course Outline

Module 1: Foundations of AI-Assisted Qualitative Research

• Explain the role of qualitative interviews, key informant discussions, focus groups, and open-ended responses in research, evaluation, and stakeholder feedback analysis.

• Introduce speech recognition, natural language processing, text classification, semantic search, and generative AI capabilities relevant to interview data processing.

• Compare manual, automated, and hybrid transcription and coding approaches according to research objectives, resource constraints, data sensitivity, and required accuracy.

• Identify the methodological limitations of AI-generated outputs, including omitted context, fabricated interpretations, stereotyping, and overreliance on frequency-based patterns.

Module 2: Audio Preparation and Automated Interview Transcription

• Assess audio quality, recording formats, background noise, overlapping speech, microphone placement, and other factors that influence automated transcription performance.

• Prepare audio files, establish consistent naming conventions, organize metadata, and select transcription settings appropriate to the language and recording conditions.

• Generate transcripts using suitable speech-to-text tools and examine available functions for speaker identification, timestamps, punctuation, and confidence indicators.

• Compare verbatim, intelligent verbatim, and edited transcription approaches, documenting the selected convention and preserving information necessary for the research purpose.

Module 3: Transcript Verification, Cleaning, and Anonymization

• Compare automated transcripts against original recordings to identify omitted words, incorrect substitutions, speaker attribution errors, and inaccurate technical or local terminology.

• Apply transcript formatting standards for interviewer questions, participant responses, speaker identifiers, timestamps, nonverbal expressions, and unintelligible speech.

• Remove or replace direct identifiers and assess indirect identification risks while maintaining a secure, controlled link between participant records and anonymized transcripts.

• Document verification procedures, correction decisions, unresolved ambiguities, and transcript versions to support traceability and reproducibility throughout the analysis.

Module 4: Natural Language Processing for Qualitative Text Preparation

• Examine text segmentation, tokenization, normalization, sentence boundaries, language detection, and other preprocessing methods used in natural language processing workflows.

• Prepare transcripts for analysis while preserving negation, contextual expressions, local terminology, and other linguistic features that may affect qualitative interpretation.

• Explore keyword extraction, phrase identification, text search, and semantic similarity as techniques for locating potentially relevant passages across multiple interviews.

• Assess the risks of removing meaningful context through excessive cleaning, translating nuanced expressions incorrectly, or treating text-processing outputs as substantive findings.

Module 5: Qualitative Coding Frameworks and Codebook Development

• Distinguish deductive coding based on research questions or conceptual frameworks from inductive coding developed through engagement with the interview data.

• Develop code definitions, inclusion and exclusion criteria, hierarchical categories, and illustrative examples that support consistent interpretation across transcripts.

• Use AI-assisted prompts to suggest candidate codes and organize preliminary code lists, then revise them through direct examination of the underlying data.

• Pilot the codebook on selected transcripts, resolve ambiguous definitions, document revisions, and establish procedures for applying the framework consistently.

Module 6: AI-Assisted Coding and Thematic Analysis

• Apply preliminary codes to transcript excerpts using suitable qualitative analysis software or structured workflows, retaining source references for every coded passage.

• Group related codes into broader categories and candidate themes while distinguishing repeated topics from deeper patterns of meaning and experience.

• Use AI to support retrieval, comparison, and summarization of coded excerpts, checking every proposed interpretation against the original participant statements.

• Examine negative cases, conflicting accounts, differences between stakeholder groups, and contextual variations that could challenge an initial thematic interpretation.

Module 7: Qualitative Analysis Software and Workflow Integration

• Explore how qualitative data analysis platforms support transcript storage, code management, annotation, memo writing, search, retrieval, and thematic organization.

• Organize transcripts, participant metadata, codebooks, analytical memos, and project documentation into a structured and maintainable research workspace.

• Integrate AI-assisted transcription and coding outputs into the selected analytical workflow while checking compatibility, access permissions, and export quality.

• Establish file naming, version control, backup, review, and access management procedures that support collaboration among researchers and evaluators.

Module 8: Quality Assurance, Researcher Reflexivity, and Bias Management

• Evaluate transcription accuracy using targeted source checks, correction logs, representative transcript samples, and additional review for high-risk or difficult recordings.

• Compare human and AI-assisted coding decisions to identify inconsistent code application, unsupported categorization, omissions, and interpretive disagreements.

• Reflect on researcher assumptions, model limitations, language bias, power relationships, and the risk of misrepresenting minority or less frequently expressed perspectives.

• Maintain analytical memos and decision logs documenting theme development, changes to the coding framework, contradictory evidence, and the rationale for interpretations.

Module 9: Ethics, Privacy, and Responsible AI Governance

• Establish informed consent and participant communication procedures that explain recording, transcription, AI assistance, data access, and relevant confidentiality limitations.

• Assess AI service terms, data retention, processing locations, access controls, model-training policies, and organizational requirements before uploading research material.

• Apply data minimization, anonymization, secure storage, controlled sharing, and retention or deletion procedures appropriate to the sensitivity of interview data.

• Document AI use and human oversight transparently, following applicable ethical requirements, institutional protocols, and relevant qualitative reporting guidance such as COREQ.

Module 10: Practical Capstone and Qualitative Findings Reporting

• Process a sample interview dataset from audio preparation and transcription through verification, cleaning, codebook application, and preliminary thematic analysis.

• Produce a structured coding framework, coded excerpts, thematic matrix, and analytical memo that link each major finding to supporting source material.

• Prepare a qualitative findings report containing clearly defined themes, carefully selected anonymized quotations, contrasting perspectives, limitations, and practical recommendations.

• Present the completed workflow for peer review, incorporate feedback, and develop an implementation checklist for applying AI-assisted qualitative analysis in participants' own organizations.

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.