# Reflexis: Supporting Reflexivity and Rigor in Collaborative Qualitative Analysis through Design for Deliberation

arXiv: https://arxiv.org/abs/2601.15445 · HTML: https://mlsanity.com/paper/2601.15445/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 95 references that are in the map.
- First posted on arXiv 21 Jan 2026.

## Builds on (most cited first, 18 of 18)

- [Supporting Qualitative Analysis with Large Language Models: Combining Codebook with GPT-3 for Deductive Coding](https://mlsanity.com/paper/2304.10548/) (2023)
- [Concept Induction: Analyzing Unstructured Text with High-Level Concepts Using LLooM](https://mlsanity.com/paper/2404.12259/) (2024)
- [Can LLM feedback enhance review quality? A randomized study of 20K reviews at ICLR 2025](https://mlsanity.com/paper/2504.09737/) (2025)
- [Human Creativity in the Age of LLMs: Randomized Experiments on Divergent and Convergent Thinking](https://mlsanity.com/paper/2410.03703/) (2024)
- [Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies](https://mlsanity.com/paper/2502.08554/) (2025)
- [Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Making](https://mlsanity.com/paper/2403.16812/) (2024)
- [CollabCoder: A Lower-barrier, Rigorous Workflow for Inductive Collaborative Qualitative Analysis with Large Language Models](https://mlsanity.com/paper/2304.07366/) (2023)
- [CoAIcoder: Examining the Effectiveness of AI-assisted Human-to-Human Collaboration in Qualitative Analysis](https://mlsanity.com/paper/2304.05560/) (2023)
- [Large Language Models in Qualitative Research: Uses, Tensions, and Intentions](https://mlsanity.com/paper/2410.07362/) (2024)
- [LLMs as Meta-Reviewers' Assistants: A Case Study](https://mlsanity.com/paper/2402.15589/) (2024)
- [Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review](https://mlsanity.com/paper/2501.12557/) (2025)
- [Scholastic: Graphical Human-Al Collaboration for Inductive and Interpretive Text Analysis](https://mlsanity.com/paper/2208.06133/) (2022)
- [Supporting Serendipity: Opportunities and Challenges for Human-AI Collaboration in Qualitative Analysis](https://mlsanity.com/paper/2102.03702/) (2021)
- [Contestable Camera Cars: A Speculative Design Exploration of Public AI That Is Open and Responsive to Dispute](https://mlsanity.com/paper/2302.04603/) (2023)
- [Generative AI in Knowledge Work: Design Implications for Data Navigation and Decision-Making](https://mlsanity.com/paper/2503.18419/) (2025)
- [ScholarMate: A Mixed-Initiative Tool for Qualitative Knowledge Work and Information Sensemaking](https://mlsanity.com/paper/2504.14406/) (2025)
- [Narrative Scaffolding: A Narrative-First Framework for Data-Driven Sensemaking](https://mlsanity.com/paper/2512.18920/) (2025)
- [Crystallizing Schemas with Teleoscope: Thematic Curation of Large Text Corpora on Reddit](https://mlsanity.com/paper/2402.06124/) (2024)

## Built on by (most cited first, 3 of 3)

- [AI Agents Push Humans Out of the Loop](https://mlsanity.com/paper/2608.23642/) (2026)
- [Agent-as-Peer-Debriefer: A Multi-Agent Framework with Perspective-Based Refinement for Qualitative Analysis](https://mlsanity.com/paper/2605.24600/) (2026)
- [MOONWALK: Mediating Operations with Intent-Evidence-Action Alignment Across Junior-Supervisor Review Workflows in Animation/VFX Pre-Production](https://mlsanity.com/paper/2609.10385/) (2026)

## Abstract (arXiv)

Reflexive Thematic Analysis (RTA) is a critical method for generating deep interpretive insights. Yet its core tenets, including researcher reflexivity, tangible analytical evolution, and productive disagreement, are often poorly supported by software tools that prioritize speed and consensus over interpretive depth. To address this gap, we introduce Reflexis, a collaborative workspace that centers these practices. It supports reflexivity by integrating in-situ reflection prompts, makes code evolution transparent and tangible, and scaffolds collaborative interpretation by turning differences into productive, positionality-aware dialogue. Results from our paired-analyst study (N=12) indicate that Reflexis encouraged participants toward more granular reflection and reframed disagreements as productive conversations. The evaluation also surfaced key design tensions, including a desire for higher-level, networked memos and more user control over the timing of proactive alerts. Reflexis contributes a design framework for tools that prioritize rigor and transparency to support deep, collaborative interpretation in an age of automation.