A Multilevel Dataset on Productive Cognitive Friction and Responsible AI-Augmented Business Judgment Under Two Active GenAI Conditions

Generative artificial intelligence (GenAI) can expand analytical support but may encourage premature acceptance and insufficient verification. This article presents a multilevel, multimethod dataset generated during a five-week study within ten-week undergraduate courses at a private university in Mexico. The study involved 120 students in 24 pre-existing teams across four sections and two disciplinary contexts. Teams were assigned within sections to two active GenAI conditions: strategic analysis and executive recommendation, or a productive-cognitive-friction condition adding structured uncertainty, simulation, role-based scenarios, stakeholder and ethical analysis, temporal-emotional reflection, and accountability safeguards. Of 118 consenting participants, 114 met the main quantitative-analysis criteria. The release includes de-identified pre/post responses to the 16-item responsible AI-augmented business judgment (RABJ) instrument; neutral individual performance independently assessed by two evaluators; original and neutral team rubric ratings; expert content-validity ratings; numeric qualitative codes; fidelity and assignment metadata; processed data; aggregate outputs; and reproducible notebooks. Technical validation covers integrity, missingness, reliability, interrater and intercoder agreement, content validity, exploratory structure, cluster-aware reproducibility, and disclosure risk. RABJ is a provisional multidimensional framework, not a validated four-factor construct. Direct identifiers, student-generated text, submitted work, and institutional linkage keys are excluded.