Evidence map›Paper›PMID 42890195›Full record

ArticleFrontiers in psychology2026

On AI-augmented transformatizing in practice: protocols, quality criteria, and reporting standards for Human-AI Augmented Mixed Methods Research.

Anthony J Onwuegbuzie

Abstract read
In one paragraph

Article in Frontiers in psychology, 2026. 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

1 author.

Anthony J OnwuegbuzieDepartment of Educational Leadership and Management, University of Johannesburg, Johannesburg, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is increasingly reshaping qualitative research, quantitative research, and mixed methods research, but practical guidance for conducting, evaluating, and reporting human-AI analytic collaboration remains underdeveloped. Building on AI-augmented transformatizing, a representational-recursive framework that conceptualizes AI as a participant in analytic re-representation rather than merely as a technical tool, this article advances a practice-oriented framework for Human-AI Augmented Mixed Methods Research. The article offers three integrated contributions. First, it proposes the

Indexed as

AI-augmented transformatizingArtificial intelligenceHuman-AI Augmented Mixed Methods ResearchHuman-AI collaborationmethodological governanceMixed Methods Researchrecursive analysisreporting standards

Identifiers

PMID42890195
PMCPMC13672510

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.