Evidence map›Paper›PMID 41262539›Full record

ArticleFrontiers in research metrics and analytics2025

Navigating the meta-crisis of generativity: adapting qualitative research quality criteria in the era of generative AI.

Niroj Dahal, Md Kamrul Hasan, Amine Ounissi, Md Nurul Haque, Hiralal Kapar

Abstract read
In one paragraph

Article in Frontiers in research metrics and analytics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Niroj DahalKathmandu University School of Education, Lalitpur, Nepal.
Md Kamrul HasanDepartment of English, United International University, Dhaka, Bangladesh.
Amine OunissiCentre for Foundation and Continuing Education, University Malaysia Terengganu, Kuala Nerus Terengganu, Malaysia.
Md Nurul HaqueDepartment of English and Modern Languages, International University of Business Agriculture and Technology, Dhaka, Bangladesh.
Hiralal KaparKathmandu University School of Education, Lalitpur, Nepal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Integrating generative AI (GenAI) in qualitative research offers innovation but intensifies core epistemological, ontological, and ethical challenges. This article conceptualizes the meta-crisis of generativity-a convergence of Denzin and Lincoln's three crises: representation (blurring human/AI authorship), legitimation (questioning trust in AI-generated claims), and praxis (ambiguity in non-human participation). We examine how human-GenAI collaboration challenges researchers' voice, knowledge validity, and ethical agency across research paradigms. To navigate this, we propose strategic approaches: preserving positionality via voice annotation and reflexive bracketing (representation); ensuring trustworthiness through algorithmic audits and adapted validity checklists (legitimation); and redefining agency via participatory transparency and posthuman ethics (praxis). Synthesizing these, we expand qualitative rigor criteria-such as credibility and reflexivity-into collaborative frameworks that emphasize algorithmic accountability. The meta-crisis is thus an invitation to reanimate the critical ethos of qualitative research through interdisciplinary collaboration, balancing the potential of GenAI with ethical accountability while preserving humanistic foundations.

Indexed as

algorithmic biascrisis of legitimationcrisis of praxiscrisis of representationgenerative AIposthuman ethicsqualitative research

Identifiers

PMID41262539
PMCPMC12623375

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.