Faculty Readiness for AI-Supported Teaching and Scalable Online Program Delivery in Higher Education: The EPIQ-AI Framework for Epistemic Integrity

Background: Higher education institutions are expanding online delivery and integrating generative artificial intelligence (GenAI), yet faculty readiness remains uneven, raising concerns about assessment validity, academic integrity, institutional legitimacy, and the quality of scalable online provision. Objective: This study develops the EPIQ-AI Readiness Framework to explain how institutions can align faculty capacity, governance, and quality assurance for AI-supported teaching and online program delivery. Methods: Using an integrative secondary evidence synthesis, the study triangulates recent official statistics, large-scale faculty and institutional surveys, peer-reviewed studies, and policy frameworks published between 2020 and 2025. The analysis is organized across four readiness domains: epistemic, pedagogical, institutional, and quality-and-compliance readiness. Results: The evidence converges on four main findings. First, faculty adoption of AI is increasingly widespread, but confidence, pedagogical clarity, and depth of use remain limited. Second, institutional ambitions for online scale and AI integration are advancing faster than policy maturity, professional development, and support capacity. Third, assessment has become the central pressure point, with growing evidence that detection-centered academic integrity regimes are unreliable, potentially biased, and insufficient for high-stakes decisions. Fourth, faculty readiness is best understood not as an individual skills deficit but as a sociotechnical alignment problem shaped by governance, incentives, workload, literacy, course design support, and equity-sensitive implementation. Conclusions: The EPIQ-AI framework reframes readiness as a multidimensional condition for credible AI-enabled and online higher education. It offers a theoretically grounded and operationally actionable model for institutions seeking to strengthen AI literacy, redesign assessment, improve governance, and sustain epistemic integrity while advancing scalable, policy-compliant online delivery.