Structured prompt framework for LLM-assisted inductive coding (role assignment, format specification, reasoning-based prompting)¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article's prompt engineering strategy specifies that prompts should meet "three core criteria: (1) clearly specifying the model's role, (2) defining input and output formats, and (3) incorporating reasoning -based strategies". Prompts distinguished open, axial, and selective coding phases, used role-based framing (instructing the model to act as a qualitative researcher), and chain-of-thought prompting to elicit rationales for each coding decision. Follow-up prompts requested clearer code definitions, additional examples, and attribution of quotes to participants, supporting auditability and researcher control over analytic content.

Design Implications¶

Context¶

Requirements¶

• Iterative prompt development by multiple researchers aligned to the study's research questions, and a platform permitting multi-turn prompting

Constraints¶

• Because CreateAI Chat did not support persistent system-level prompts, all role instructions had to be delivered through the initial user prompt of each session

Target Learners¶

• Qualitative researchers and educational technology design teams

Target Learning Goals¶

• Rigorous, transparent inductive thematic analysis of user feedback data

Related Strategies¶

• Curricular chain-of-thought prompting: extract key pedagogical elements before competency inference
• Use structured multi-stage prompts with clearly defined constructs when eliciting LLM topic labels for qualitative coding
• Use LLMs to generate initial course metadata labels, shifting human taggers to a confirmatory role
• Instruction-guided LLM annotation with human-in-the-loop prompt refinement for large-scale interaction labeling

Examples¶

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Key Sources¶

• Potter, A.; Serhan, Z.; Patne, N. A.; Öncel, P.; Ahmed, I.; Arner, T.; Islam, R.; Roscoe, R. D.; Allen, L. A.; Crossley, S. A.; McNamara, D. S. (2026). Human-AI Collaboration for Qualitative Analysis in Participatory Design: Refining the Writing Analytics Tool. Journal of Educational Data Mining, Volume 18, No 1. https://osf.io/8j9y6/?view_only=3a21ed900475418a8c258ea595cbe53b