Dataset for an Explanatory Mixed-Method Study of Faculty Readiness, Enactment, Satisfaction, and Structural Mediation in an Institutional AI Credentialing Initiative

This dataset accompanies an explanatory mixed-method study examining faculty readiness, instructional enactment depth, professional satisfaction, and structural mediation within a university-wide artificial intelligence credentialing initiative in higher education. The quantitative component includes anonymized survey data measuring (1) faculty readiness dimensions (digital artificial intelligence literacy, pedagogical belief alignment, and metacognitive orientation), (2) enacted instructional transformation levels categorized into surface integration, structural redesign, and transformative collaboration, and (3) multidimensional professional satisfaction indicators. Composite scores and item-level responses are included where permitted by institutional ethics guidelines. The qualitative component includes anonymized excerpts from open-ended survey responses, focus group discussion transcripts, and structured observational notes derived from instructional video artifacts. Qualitative data were de-identified to remove personal, institutional, and program-specific identifiers. All participants provided informed consent, and data were anonymized prior to repository submission. No raw video files are included; instead, coded observational summaries are provided to protect participant confidentiality. This dataset is intended to support transparency, secondary analysis, and replication studies related to artificial intelligence-enabled instructional reform and faculty development in higher education contexts.