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.

When exploring data, analysts construct narratives about what the data means by asking questions, generating visualizations, reflecting on patterns, and revising their interpretations as new insights emerge. Yet existing analysis tools treat narrative as an afterthought, breaking the link between reasoning, reflection, and the evolving story from exploration. Consequently, analysts lose the ability to see how their reasoning evolves, making it harder to reflect systematically or build coherent explanations. To address this gap, we propose Narrative Scaffolding (NS), a framework for narrative-driven exploration that positions narrative construction as the primary interface for exploration and reasoning. We implemented this framework in a system that externalizes iterative reasoning through narrative-first entry, semantically aligned view generation, and reflection support via insight provenance and inquiry tracking. In a within-subject study (N = 20), we demonstrated that narrative scaffolding facilitates broader exploration, deeper reflection, and more defensible narratives. An evaluation with visualization literacy experts (N = 6) confirmed that the system produced outputs aligned with narrative intent and facilitated intentional exploration.

Efficiently navigating and understanding academic papers is crucial for scientific progress. Traditional linear formats like PDF and HTML can cause cognitive overload and obscure a paper’s hierarchical structure, making it difficult to locate key information. While LLM-based chatbots offer summarization, they often lack nuanced understanding of specific sections, may produce unreliable information, and typically discard the document’s navigational structure. Drawing insights from a formative study on academic reading practices, we introduce Treereader, a novel language model-augmented paper reader. Treereader decomposes papers into an interactive tree structure where each section is initially represented by an LLM-generated concise summary, with underlying details accessible on demand. This design allows users to quickly grasp core ideas, selectively explore sections of interest, and verify summaries against the source text. A user study was conducted to evaluate Treereader’s impact on reading efficiency and comprehension. Treereader provides a more focused and efficient way to navigate and understand complex academic literature by bridging hierarchical summarization with interactive exploration.