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

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 AI-Augmented Transformatizing Protocol , a recursive workflow for preparing representations, for assigning human and AI analytic roles, for generating AI-assisted transformations, for conducting human critique, for refining findings, and for constructing meta-inferences. Second, it introduces quality criteria for assessing methodological rigor in human-AI augmented analysis, including representational fidelity, prompt traceability, recursive transparency, human oversight integrity, algorithmic reflexivity, convergence-divergence interrogation, meta-inferential robustness, and ethical accountability. Third, it presents reporting standards designed to help authors disclose AI systems, model versions, prompting procedures, analytic iterations, researcher interventions, validation/legitimation strategies, ethical safeguards, and limitations. These proposed methodological components are conceptual and practice-oriented rather than empirically validated; accordingly, the worked example illustrates their operational logic, whereas future research is needed to establish their empirical performance, including the validity and reliability of the HAAM-QAM. Rather than treating AI use as an optional efficiency enhancement, the framework positions human-AI collaboration as a structured methodological practice requiring explicit design, documentation, reflexivity, and accountability. By translating the theory of AI-augmented transformatizing into protocols, quality criteria, and reporting standards, this article provides methodologists, researchers, authors, reviewers, editors, graduate educators, mentors, and the like with practical guidance for conducting transparent, rigorous, and ethically responsible AI-augmented mixed methods research in psychology and related fields. In so doing, it extends current reporting guidelines and quality frameworks by addressing the distinctive representational, recursive, and ethical demands introduced when AI participates directly in analysis.