EduAI-NG: A FAIR Longitudinal Dataset of AI Readiness Among Nigerian Educators (EDAIL)

EduAI-NG is a FAIR-aligned longitudinal dataset derived from the EDAIL (Educators’ AI Literacy) programme in Nigeria, designed to assess and benchmark educator readiness for Artificial Intelligence (AI) integration in low-resource classroom environments. The dataset captures pre- and post-training responses from secondary school teachers across all six geopolitical zones of Nigeria. It comprises 2,239 pre-training responses, 1,068 post-training responses, and a matched cohort of 770 educators, enabling robust longitudinal analysis of training impact. The dataset includes 51–63 structured variables spanning four key competency domains: (1) AI foundational knowledge, (2) pedagogical integration of AI, (3) ethical and responsible AI awareness, and (4) deployment readiness. All responses are encoded using standardized Likert scales and have been cleaned, anonymized, and documented in accordance with FAIR data principles (Findable, Accessible, Interoperable, and Reusable). A key contribution of this dataset is the Teacher AI Deployment Readiness Index (TADRI-lite), a composite measure that quantifies multidimensional educator preparedness for AI-enabled teaching. EduAI-NG supports research in Artificial Intelligence in Education (AIED), educational data mining, responsible AI, and scalable AI deployment in the Global South. It is particularly suited for benchmarking, predictive modeling, and policy-driven AI capacity development. This dataset represents one of the first FAIR-aligned longitudinal benchmarks of educator AI readiness from an African context. Data was collected via electronic surveys (Google Forms), with voluntary participation and informed consent. All personally identifiable information has been removed prior to release. If you use this dataset, please cite: Olurinola, O., Folorunso, S., & Owor, P. (2026). EDAIL-EduAI-NG: Benchmarking Educator AI Readiness for Scalable Deployment in Low-Resource Classrooms. In the Proceedings of the IndabaX Nigeria 2026 Conference, PMLR 319