Learn Qualitative Research AI Tools and Workflows With >90% Validated Accuracy: Peer-Reviewed, Audit-Ready Methods for Health Professions Education

Date: Saturday, 26 September 2026 | Time: 1120 - 1250 | Venue: L2-S3 Exp, Academia (SGH Campus)

Track Type: Workshop

Main Facilitator:

Overview:

This interactive, hands-on workshop equips healthcare educators and health professions education (HPE) researchers with the same AI tools and qualitative workflows already used in peer-reviewed studies, with an emphasis on audit-ready outputs that stand up to reviewer scrutiny.

Participants will learn a repeatable, researcher-in-the-loop workflow for common HPE data types (interviews, focus groups, reflective writing, open-ended surveys, teaching evaluations, and debrief transcripts). The workflow is designed to support transparent coding and theme development, with structured points where human judgement is essential (analytic focus, resolving ambiguity, refining theme boundaries, and writing defensible interpretations).

A core feature is validation: participants will practise pragmatic approaches used in published work to stabilise outputs (e.g., calibration on a subset, codebook anchoring, double-coding checks, and discrepancy review) so that AI assistance improves efficiency without over-claiming. The session also covers practical safeguards for bias, hallucination, and uncertainty management, including “show-the-evidence” reporting where each theme is traceable to quotations and source locations.

To extend beyond analysis, participants will also learn how to set up an evidence-grounded dataset chatbot (to query themes and retrieve supporting excerpts) and how to prototype an AI avatar interviewer for education research and evaluation—useful for piloting interview guides, practising difficult conversations, or scaling exploratory interviews in a consistent manner.

Participants may use their own de-identified data (where appropriate) or work with provided HPE sample datasets.

Learning Outcome(s):

By the end of the workshop, participants will be able to:

• Apply a peer-reviewed, step-by-step AI qualitative workflow (from raw text to coded excerpts, themes, and evidence-linked reporting).
• Conduct inductive (theme discovery) and deductive (codebook-based) AI-assisted coding with explicit human oversight points.
• Implement quality and validation checks (e.g., calibration, sampling, discrepancy review, and uncertainty logging) to produce defensible findings.
• Produce traceable outputs suitable for methods/results write-up: coded excerpts linked to source text, theme summaries, and cross-segment comparisons (e.g., by profession, training level, site).
• Build and safely use (a) an evidence-grounded analysis chatbot and (b) an AI avatar interviewer for piloting and scalable qualitative data collection, including consent and governance considerations in Singapore settings.

Target Audience:

Healthcare educators, clinician-educators, education researchers, programme evaluators, and postgraduate trainees involved in HPE research/evaluation.

Prerequisites: Basic familiarity with qualitative research concepts (e.g., coding/themes).