Commentaries on “Generative grounded theory ( GGT ): Inductive theory building in the age of generative AI ”
TL;DR
Commentaries of this Methods Dialogue acknowledge that GGT could extend the reach of inductive inquiry and facilitate automated theorizing, but it is a method that demands critical judgment, transparent record‐keeping, and human control.
Abstract
The rapid growth of generative AI has generated excitement about its potential and concern about its effects on psychological research. Schmitt, Hao, Pham, and Hofstetter (2026) offer Generative Grounded Theory (GGT) as a seven‐step framework for incorporating generative artificial intelligence into inductive theory building. It uses AI to support corpus formation, data structuring, coding, conceptual clustering, abstraction, theoretical integration, and the assessment of saturation, while reserving interpretive authority and theoretical responsibility for the researcher. These Commentaries of this Methods Dialogue respond to the accepted, revised version of the lead article by Schmitt et al. (2026), which incorporated feedback from open, collaborative reviews by established researchers. Following acceptance, the review teams provided four independent assessments of the value of GGT. Tomaino proposes that GGT is particularly suited for experimental researchers who are generating rich conversational data through AI‐mediated studies, as GGT can possibly reveal mechanisms, generate new research questions, and unearth competing explanations. Schweidel emphasizes the researcher's indispensable role as a “cognitive operator.” He acknowledges that GGT's traceability facilitates replication, digital‐twin exploration, and coordination across qualitative–quantitative researchers, but only if researchers resist sycophancy, cognitive offloading, and the temptation to delegate the entire process to an autonomous agent. Dolbec, Fischer, and Smith expand the focus on the responsibility of the GGT researcher, arguing that the ease of AI‐assisted analysis may create a “fallacy of facility” in which plausible output is mistaken for expertise. They propose that methodological simulacrums can produce polished but weakly grounded research unless investigators actively harness substantial qualitative method expertise to control AI's distorting capability. Finally, Puntoni and Schillewaert shift attention upstream from analysis to data collection. They argue that using AI moderation may relax the long‐standing separation between qualitative depth interviews and standardized variable assessment. If so, AI‐adaptive interviews could integrate sampling and analysis by offering a “living” grounded theory, while raising new questions about saturation and reproducibility. Together, these commentaries acknowledge that GGT could extend the reach of inductive inquiry and facilitate automated theorizing, but it is a method that demands critical judgment, transparent record‐keeping, and human control.