Exploring the synergy of human and AI-driven approaches in thematic analysis for qualitative educational research
Bibliographic record
Abstract
This paper investigates the integration of Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs) such as GPT-4, into qualitative analysis in educational research. Utilizing TRACER (Transcript Analysis and Concept Extraction Resource), a GenAI-driven tool, the study evaluated its efficiency, reproducibility, and synergy with human analytical expertise. The research demonstrated that TRACER significantly streamlined thematic analysis, efficiently handled large data volumes, and maintained consistency in theme identification. The findings reveal that integrating TRACER’s computational power with human interpretive skills enriches research outcomes, suggesting a collaborative approach for optimal results. Despite its efficacy, limitations such as data scope and current GenAI capabilities are acknowledged, indicating areas for future development. This paper contributes to the understanding of GenAI’s role in qualitative research, proposing it as a valuable tool for overcoming traditional challenges in the field and highlighting the importance of human-AI collaboration for comprehensive and nuanced analyses in educational research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.263 | 0.329 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".