CollabCoder
A Lower-barrier, Rigorous Workflow for Inductive Collaborative Qualitative Analysis with Large Language Models
CollabCoder is a lower-barrier, rigorous workflow for inductive collaborative qualitative analysis. By integrating Large Language Models into the coding process, CollabCoder helps research teams achieve higher quality and more efficient qualitative analysis.
The system supports the full qualitative analysis pipeline — from independent open coding with AI-assisted suggestions, through iterative discussion and conflict resolution between coders, to final codebook development with LLM-generated code groupings. CollabCoder lowers the barrier to rigorous qualitative research while maintaining methodological standards.
On-demand code suggestions from LLMs help coders produce initial codes with higher confidence.
Conflict mediation within the coding team produces a list of agreed-upon code decisions.
LLM-generated suggestions help form code groups based on the decided codes.
CollabCoder provides an integrated workspace for collaborative qualitative analysis powered by LLMs.
Coders can input customized codes for raw data, choose from GPT recommendations or top relevant codes, add keyword support, assign certainty levels (1-5), and review their individual codebook.
Two coders' codes are displayed side-by-side. The system calculates similarity between code pairs and Inter-Rater Reliability (IRR), helping teams identify agreements and resolve disagreements through discussion or GPT-suggested decisions.
Code decisions are compiled into unique codes. Users can group them manually or use AI-generated themes, drag codes into groups, and save the final codebook.
