# Scholastic: Graphical Human-Al Collaboration for Inductive and Interpretive Text Analysis

arXiv: https://arxiv.org/abs/2208.06133 · HTML: https://mlsanity.com/paper/2208.06133/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 55 references that are in the map.
- First posted on arXiv 12 Aug 2022.

## Builds on (most cited first, 7 of 7)

- [Multilayer Networks](https://mlsanity.com/paper/1309.7233/) (2013)
- [Bayesian stochastic blockmodeling](https://mlsanity.com/paper/1705.10225/) (2017)
- [A network approach to topic models](https://mlsanity.com/paper/1708.01677/) (2017)
- [Integrating Document Clustering and Topic Modeling](https://mlsanity.com/paper/1309.6874/) (2013)
- [Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections](https://mlsanity.com/paper/1908.00475/) (2019)
- [Multilayer Networks for Text Analysis with Multiple Data Types](https://mlsanity.com/paper/2106.15821/) (2021)
- [Tending Unmarked Graves: Classification of Post-mortem Content on Social Media](https://mlsanity.com/paper/1904.05326/) (2019)

## Built on by (most cited first, 12 of 12)

- [Concept Induction: Analyzing Unstructured Text with High-Level Concepts Using LLooM](https://mlsanity.com/paper/2404.12259/) (2024)
- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (2024)
- [HICode: Hierarchical Inductive Coding with LLMs](https://mlsanity.com/paper/2509.17946/) (2025)
- [ScholarMate: A Mixed-Initiative Tool for Qualitative Knowledge Work and Information Sensemaking](https://mlsanity.com/paper/2504.14406/) (2025)
- [Human-AI Collaboration in Thematic Analysis using ChatGPT: A User Study and Design Recommendations](https://mlsanity.com/paper/2311.03999/) (2023)
- [The Design Space of Recent AI-assisted Research Tools for Ideation, Sensemaking, and Scientific Creativity](https://mlsanity.com/paper/2502.16291/) (2025)
- [Reflexis: Supporting Reflexivity and Rigor in Collaborative Qualitative Analysis through Design for Deliberation](https://mlsanity.com/paper/2601.15445/) (2026)
- [Exploring the Human-LLM Synergy in Advancing Theory-driven Qualitative Analysis](https://mlsanity.com/paper/2405.05758/) (2024)
- [Generative AI Tools in Academic Research: Applications and Implications for Qualitative and Quantitative Research Methodologies](https://mlsanity.com/paper/2408.06872/) (2024)
- [Interactive Topic Models with Optimal Transport](https://mlsanity.com/paper/2406.19928/) (2024)
- [TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models](https://mlsanity.com/paper/2507.18945/) (2025)
- [Designing Computational Tools for Exploring Causal Relationships in Qualitative Data](https://mlsanity.com/paper/2602.06506/) (2026)

## Abstract (arXiv)

Interpretive scholars generate knowledge from text corpora by manually sampling documents, applying codes, and refining and collating codes into categories until meaningful themes emerge. Given a large corpus, machine learning could help scale this data sampling and analysis, but prior research shows that experts are generally concerned about algorithms potentially disrupting or driving interpretive scholarship. We take a human-centered design approach to addressing concerns around machine-assisted interpretive research to build Scholastic, which incorporates a machine-in-the-loop clustering algorithm to scaffold interpretive text analysis. As a scholar applies codes to documents and refines them, the resulting coding schema serves as structured metadata which constrains hierarchical document and word clusters inferred from the corpus. Interactive visualizations of these clusters can help scholars strategically sample documents further toward insights. Scholastic demonstrates how human-centered algorithm design and visualizations employing familiar metaphors can support inductive and interpretive research methodologies through interactive topic modeling and document clustering.