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arxiv: 2509.18297 · v2 · submitted 2025-09-22 · 💻 cs.HC

Not a Collaborator or a Supervisor, but an Assistant: Striking the Balance Between Efficiency and Ownership in AI-incorporated Qualitative Data Analysis

Anoushka Puranik , Ester Chen , Roshan L Peiris , Hidy Kong This is my paper

Pith reviewed 2026-05-18 13:55 UTC · model grok-4.3

classification 💻 cs.HC
keywords AI-assisted qualitative data analysisresearcher preferences for AIhuman-AI collaboration in QDAdelegation levels in automationefficiency and ownership balancetrust in AI toolsthematic analysis and coding
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The pith

Qualitative researchers prefer AI as an assistant in data analysis rather than a collaborator or supervisor to balance efficiency with ownership.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines how qualitative researchers want to use AI for tasks like coding and thematic analysis. Interviews reveal openness to AI handling repetitive work for efficiency, yet strong insistence on retaining human oversight to preserve ownership of interpretations. Efficiency, ownership, and trust emerge as the key factors shaping preferred levels of AI involvement. These insights matter for designing tools that support rather than sideline the interpretive core of qualitative research.

Core claim

Through interviews with 16 qualitative researchers, the study finds preferences for human-initiated AI coding over human-only or AI-initiated approaches. Researchers value efficiency gains from AI but require human control and transparency to maintain ownership and build trust, positioning AI as a supportive assistant in QDA workflows.

What carries the argument

Three degrees of delegation in QDA: human-only, human-initiated, and AI-initiated coding. This spectrum is used to surface how researchers weigh efficiency against ownership and trust.

If this is right

  • AI tools should include mechanisms for researchers to initiate and review AI suggestions to support ownership.
  • Transparency features about AI decision processes are needed to foster trust in automated coding.
  • Treating AI as an assistant rather than an autonomous agent can help keep human interpretation central and reduce analysis bias.
  • Designs that strengthen human-AI collaboration in QDA follow directly from prioritizing researcher control.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same efficiency-ownership tension may appear in other interpretive fields that adopt AI assistance, such as historical document analysis.
  • Real-world testing of human-initiated AI interfaces in ongoing QDA projects could refine the three delegation levels.
  • Wider use of assistant-style AI might gradually shift training and expectations around what counts as original qualitative insight.

Load-bearing premise

The preferences reported by the 16 interviewed qualitative researchers represent those of the wider community of qualitative researchers.

What would settle it

A follow-up study with a larger, more diverse sample of qualitative researchers that finds majority preference for either fully AI-initiated coding or complete avoidance of AI would contradict the reported balance.

Figures

Figures reproduced from arXiv: 2509.18297 by Anoushka Puranik, Ester Chen, Hidy Kong, Roshan L Peiris.

Figure 1
Figure 1. Figure 1: The main page of ChromaScribe, an AI-augmented QDA webtool prototype with a color-coded visualization panel on the left [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The theme selection page shows AI-generated themes. The user can select automated themes and/or add their own themes [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The visualization panel shows a timeline-based visualization of theme distributions. When a user selects a theme (such as [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
read the original abstract

Qualitative research offers deep insights into human experiences, but its processes, such as coding and thematic analysis, are time-intensive and laborious. Recent advancements in qualitative data analysis (QDA) tools have introduced AI capabilities, allowing researchers to handle large datasets and automate labor-intensive tasks. However, qualitative researchers have expressed concerns about AI's lack of contextual understanding and its potential to overshadow the collaborative and interpretive nature of their work. This study investigates researchers' preferences among three degrees of delegation of AI in QDA (human-only, human-initiated, and AI-initiated coding) and explores factors influencing these preferences. Through interviews with 16 qualitative researchers, we identified efficiency, ownership, and trust as essential factors in determining the desired degree of delegation. Our findings highlight researchers' openness to AI as a supportive tool while emphasizing the importance of human oversight and transparency in automation. Based on the results, we discuss three factors of trust in AI for QDA and potential ways to strengthen collaborative efforts in QDA and decrease bias during analysis.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper reports results from semi-structured interviews with 16 qualitative researchers examining their preferences across three levels of AI delegation in qualitative data analysis (human-only, human-initiated, and AI-initiated coding). It identifies efficiency, ownership, and trust as the primary factors shaping these preferences and argues that researchers remain open to AI as a supportive tool provided human oversight and transparency are maintained. The manuscript concludes with design implications for strengthening collaboration and reducing bias in AI-supported QDA.

Significance. If the thematic findings hold, the work supplies empirical grounding for HCI discussions on acceptable AI roles in interpretive research practices. It usefully surfaces the efficiency-ownership tension and proposes three trust factors, which could inform tool design. The study is a standard exploratory interview project with no circular derivations or invented parameters; its value lies in the direct participant-reported factors rather than in broad generalization.

major comments (2)
  1. [Abstract and Methods] Abstract and Methods: the abstract states that efficiency, ownership, and trust were identified as essential factors but supplies no information on the coding process, inter-rater reliability checks, or how saturation was assessed. Without these details the derivation of the three factors remains opaque and the load-bearing claim that they determine delegation preferences cannot be fully evaluated.
  2. [Discussion] Discussion section: the design recommendations for AI tools in QDA rest on the preferences reported by the 16 participants. The manuscript provides neither a participant table, disciplinary spread, recruitment criteria, nor evidence that variation across subfields (e.g., ethnography versus grounded theory) was captured; this weakens the step from observed factors to prescriptive guidance.
minor comments (2)
  1. The three delegation levels are introduced clearly in the abstract but would benefit from a concise table or diagram in the main text to help readers map participant quotes to each level.
  2. A short limitations paragraph explicitly addressing sample size and disciplinary coverage would strengthen the manuscript even if the authors do not claim broad generalizability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and indicate planned revisions to improve transparency and strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract and Methods] Abstract and Methods: the abstract states that efficiency, ownership, and trust were identified as essential factors but supplies no information on the coding process, inter-rater reliability checks, or how saturation was assessed. Without these details the derivation of the three factors remains opaque and the load-bearing claim that they determine delegation preferences cannot be fully evaluated.

    Authors: We agree that the abstract would benefit from greater methodological transparency. The full Methods section details our reflexive thematic analysis: the first author performed open coding on transcripts, followed by team discussions to develop and refine themes; saturation was evaluated iteratively by monitoring for the emergence of new codes until thematic stability was reached. Inter-rater reliability was not computed because the analysis was interpretive and primarily single-coder with collaborative refinement, which aligns with common practice in exploratory qualitative work. We will revise the abstract to concisely note the analysis approach and saturation assessment, thereby clarifying how the three factors were derived. revision: yes

  2. Referee: [Discussion] Discussion section: the design recommendations for AI tools in QDA rest on the preferences reported by the 16 participants. The manuscript provides neither a participant table, disciplinary spread, recruitment criteria, nor evidence that variation across subfields (e.g., ethnography versus grounded theory) was captured; this weakens the step from observed factors to prescriptive guidance.

    Authors: We acknowledge that a participant summary table and explicit discussion of sample diversity would better ground the design implications. The manuscript already describes recruitment via academic networks, professional organizations, and snowball sampling, targeting researchers with at least two years of qualitative experience. Participants spanned education, psychology, sociology, and health sciences and employed a range of approaches including phenomenology, grounded theory, and ethnography. We will add a demographics table and expand the Discussion to report how efficiency, ownership, and trust preferences were consistent across these subfields, thereby strengthening the link from findings to recommendations. revision: yes

Circularity Check

0 steps flagged

No circularity in empirical interview study

full rationale

This manuscript reports an empirical qualitative study based on interviews with 16 researchers. Its central claims about preferences for AI delegation levels and the importance of efficiency, ownership, and trust are presented as direct outcomes of thematic analysis of participant responses. No equations, fitted parameters, predictions derived from subsets of data, or self-referential derivations appear in the provided text. The derivation chain consists of data collection followed by interpretation, with no step that reduces by construction to its own inputs or relies on a load-bearing self-citation chain. The study is therefore self-contained against external benchmarks as a standard interview-based investigation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper is an empirical qualitative interview study. It rests on the domain assumption that self-reported preferences from a modest sample can identify broadly relevant design factors for AI tools. No free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Semi-structured interviews with qualitative researchers can surface reliable factors influencing AI delegation preferences in QDA.
    The entire set of findings depends on treating the 16 interviews as sufficient to identify efficiency, ownership, and trust as essential factors.

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