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Qualitative Research

● high · created 2026-08-24 · updated 2026-08-24 · qualitative-research, research-methods-aied, thematic-analysis, grounded-theory, interviews, discourse-analysis, case-study, phenomenography

Qualitative research — the family of empirical methods that study how people experience, interpret, and make meaning of phenomena, typically through words, observations, and artifacts rather than numbers. In AI in education, qualitative methods reveal how students and teachers actually experience AI tools — the meanings, tensions, harms, and mechanisms that standardized measures miss. Because AI-in-education is fast-moving and its effects are often mediated by context, perception, and contested constructs like Trust and Agency, qualitative work is essential alongside quantitative designs (see Research Methods AIED).

Qualitative research is not a single method but a family organized by what they study and how evidence is gathered and analyzed. What unites them is an emphasis on meaning-making, context, and depth over breadth and causal control. Qualitative findings are typically not generalizable in the statistical sense, but they are often conceptually generalizable — revealing mechanisms, categories, and dynamics that transfer to other settings. In the wiki's corpus, qualitative work is prominent for studying AI acceptance, trust, harm, teaching practice, and learning processes.

Major qualitative approaches

Thematic analysis

Thematic analysis identifies, codes, and interprets patterns ("themes") across qualitative data — typically interview or focus-group transcripts, open-ended survey responses, or documents. It is the most widely used approach in the wiki's qualitative studies. A study of the trust–utility gap in physics uses thematic analysis of student interview data to surface domain-specific skepticism and adoption preferences; a comparison of GenAI vs. teacher feedback analyzes student perceptions of usefulness and trustworthiness; and guidelines for adult AI learning derive design principles from thematic coding of expert and learner input. How AI changes teaching workflows relies on thematic analysis of educator accounts.

Grounded theory

Grounded theory builds a theory from the data rather than testing an a priori framework, using iterative coding (open → axial → selective) until theoretical saturation. It is ideal for constructing new theory about emergent AI-in-education phenomena. Liu et al. develop a grounded theory of tool, tutor, or crutch — a three-mode typology of how students cognitively scaffold or offload onto AI in programming education — directly theorizing Cognitive Offloading. A grounded-theory study of critical AI tutors examines whether such tutors empower or enslave learners. Human-centered GenAI feedback design uses grounded analysis across a multisite study. See also Theory Development AIED for how such grounded theories feed the field's theory building.

Phenomenology and phenomenography

Phenomenological approaches study the lived experience of a phenomenon — what it is like to learn with AI — while phenomenography studies the qualitatively different ways people experience and understand a phenomenon (producing "categories of description"). The Absent Cognitive Baseline draws on students' lived experiences of self-assessment under AI to theorize a structural gap; a phenomenological study captures the experience of completing work with AI while knowingly not understanding it; and a socio-cultural, interpretive study analyzes how GenAI becomes a "runaway object" in mathematics academic practice.

Discourse analysis

Discourse analysis examines how language-in-use constructs meaning, identities, and power — analyzing classroom talk, written text, or interactional sequences. NSPA conducts long-horizon classroom discourse analysis (here computationally assisted) to mitigate dialect bias in understanding classroom interaction; a study of ethnic-minority preparatory students analyzes collaborative discourse in prompt-engineering tasks. Discourse analysis bridges qualitative interpretation with computational methods when combined with Educational NLP.

Observations and ethnography

Observation studies watch behavior in context; ethnography extends this to sustained, immersive study of a setting, often with the researcher as participant-observer. A trio-ethnography of LLM-supported programming education traces how students' interpretations evolve; a classroom case study observes AI-literacy integration in biology. Observational methods capture actual behavior (what learners do with AI) rather than reported behavior — complementing the self-report surveys that dominate quantitative measurement of attitudes.

Case studies

A case study is an in-depth investigation of a bounded case (a course, an institution, a single learner) using multiple data sources. A business-school case study generates a student-informed teaching and learning framework for GenAI; a biology case study documents AI-literacy integration. Case studies trade breadth for depth and are strong for theory generation and transferable insight rather than generalization.

Interviews and focus groups

Semi-structured interviews and focus groups are the primary data-collection instruments across all the above approaches. They elicit rich, contextual accounts. The wiki's qualitative corpus is built substantially on interviews (e.g., expertise pathways, youth leaders across five countries) and focus groups (e.g., the text-to-image competence paradox, AI Adult Learning Guidelines Dis2026). Quality depends on careful question design, sampling for variation, and rigorous analysis.

How qualitative research appears in the wiki

AI and qualitative analysis

A distinctive recent development is using LLMs to assist qualitative coding. The wiki's evidence is cautionary: Human Vs LLM Ordered Coding shows LLM and human coding diverge, with errors cascading through temporal analysis; Agreement Is Not Quality shows that human–LLM coding agreement is not the same as coding quality when human consensus is not ground truth. LLM-assisted coding can scale and accelerate qualitative analysis, but its outputs require verification against human judgment — an important intersection of qualitative research with Educational NLP and AI Ed Evaluation.

Strengths and limitations

  • Strengths: deep ecological and conceptual insight; surfaces unexpected phenomena, risks, and mechanisms; essential for theory-building (see Theory Development AIED); captures meaning, context, and contested constructs; centers under-represented perspectives; strong for studying fast-moving phenomena where standardized measures lag.
  • Limitations: limited statistical generalizability; interpretive and researcher-dependent (reliability concerns); small samples; weaker support for causal claims; findings can be hard to synthesize across studies; time- and labor-intensive.

Qualitative and quantitative methods are complements, not rivals — see Research Methods AIED for how they contrast and triangulate, and mixed methods for designs that combine them.

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