"Assessing Qualitative Research Methods in the Context of AI": Presented by Dr Michelle de Jong [SoPH]

"Assessing Qualitative Research Methods in the Context of AI": Presented by Dr Michelle de Jong [SoPH]

Thursday, October 22, 2026

Dr Michelle de Jong (Senior Lecturer and Master of Public Health Programme Coordinator) presented at the CIECT AI Colloquium. Her presentation, “Assessing Qualitative Research Methods in the Context of AI: Cultivating Reflexivity and Critical Thinking Among Postgraduate Students,” addressed a central question facing modern higher education: How can researchers leverage AI while ensuring that critical thinking, reflexivity, and human engagement remain at the center of analysis?

Video Recording

Click on the link to view her presentation (video recording): https://youtu.be/G1GDCPSt9AQ?si=zR9nbPh-lUVPY1dL

Understanding Qualitative Research in the Age of AI

Qualitative research relies on interpreting human experiences, meanings, and socio-cultural contexts. While Generative AI excels at organizing information, summarizing material, and identifying broad themes, it cannot replace human engagement with contextual nuances. Dr de Jong demonstrated how AI can be incorporated as a comparative tool—assisting with data processing while keeping the researcher responsible for interpreting the true meaning and significance of the findings.

Reflexivity and the Researcher’s "Fingerprints"

Reflexivity requires researchers to critically examine how their personal experiences, assumptions, and positionality shape the research process. In a classroom exercise analyzing interview transcripts on barriers to contraceptive access, students experienced emotional reactions—such as anger, sadness, or sympathy—that directly influenced their initial coding choices. Reflecting on these reactions helped students identify their personal "fingerprints" on the data. The rise of AI adds another reflexive layer: researchers must now evaluate not only their own assumptions, but also how AI tools influence the questions asked, the data encountered, and the interpretations generated.

Critical Evaluation

Dr de Jong emphasized that AI-generated summaries and patterns are merely starting points that require systematic evaluation. Researchers must evaluate AI outputs by asking:

• Is the information accurate?
• What implicit assumptions are present?
• What key information or context was omitted?
• Does the interpretation reflect the specific research context?
• How has AI framing influenced the way information is presented?
• Can these conclusions be supported by the original research material?
• What valid insights did the AI reveal that the researcher overlooked?

Human Nuance vs. AI Neutrality

Side-by-side comparisons of student and AI coding highlighted clear differences. AI-generated codes were organised and neutral, but frequently flattened emotional, systemic, and contextual meanings. In contrast, student coding captured rich human nuance and structural realities. Crucially, comparing the outputs also helped students identify their own analytical blind spots—showing how AI can serve as an effective sounding board to challenge assumptions without replacing human judgment.

Developing Critical AI Literacy in Postgraduate Education

Developing AI literacy goes beyond prompt engineering; it requires learning to question outputs, verify interpretations, and recognize analytical limitations. Dr de Jong highlighted that reflexivity should not be treated as a single, isolated assignment. Instead, postgraduate programs should embed continuous reflexive practice across modules through weekly journaling, oral reflections, and scenario-based activities.

Keeping the Researcher at the Centre

AI tools offer valuable analytical support, but sense-making, interpretation, and conclusion-drawing remain distinctly human responsibilities. Engaging directly with qualitative data builds student confidence, reinforcing that human analytical skills remain indispensable.

CIECT extends its sincere gratitude to Dr Michelle de Jong for her valuable contribution to the AI Colloquium 2026 and for leading the conversation on critical, reflexive AI integration in research education.