Faculty and Administrator Perceptions of AI-Driven Assessment Analytics in Meeting Accreditation Standards in Healthcare Management Education

Abstract: The use of artificial intelligence (AI) in higher education assessment has been widely promoted as a mechanism for strengthening accreditation compliance and advancing continuous quality improvement (CQI); however, empirical evidence explaining how faculty and administrator perceptions translate into these outcomes remains limited. Guided by an integrated Technology Acceptance Model–Human-Centered AI–Continuous Quality Improvement (TAM–HCAI–CQI) framework, this study examined the relationships among perceived AI-driven assessment analytics, assessment confidence, perceived accreditation readiness, and CQI capacity in healthcare management education. Using a quantitative, cross-sectional perception-based design, survey data were collected from 64 participants (45 faculty members and 19 academic administrators) engaged in assessment and accreditation activities. Descriptive analyses indicated moderately positive perceptions of AI-driven assessment analytics (M = 3.61, SD = 0.67), with no statistically significant differences between faculty and administrators. Regression analyses demonstrated that perceived AI-driven assessment analytics significantly predicted accreditation readiness (β = .52, p < .001, R² = .27) and CQI capacity (β = .57, p < .001, R² = .32). Mediation analyses using bootstrapping revealed that assessment confidence partially mediated the relationship between AI perceptions and accreditation readiness (indirect effect β = .32, 95% CI [.18, .50]) and fully mediated the relationship between AI perceptions and CQI capacity (indirect effect β = .34, 95% CI [.20, .54]). Role-based analyses further indicated convergence between faculty and administrator perceptions across all study variables. Collectively, these findings demonstrate that the contribution of AI-driven assessment analytics to accreditation readiness and continuous improvement operates primarily through faculty and administrator confidence in assessment evidence, rather than through technology alone. The study explains how institutions move from compliance-oriented assessment practices toward sustained continuous quality improvement, highlighting the importance of human-centered implementation strategies that align AI-supported analytics with accreditation standards. Implications are discussed for institutional practice, accreditation policy, and future research on AI-enabled assessment and quality assurance in healthcare management education. Keywords: artificial intelligence; assessment analytics; accreditation readiness; continuous quality improvement; assessment confidence; faculty perceptions; administrator perceptions; healthcare management education. Title: Faculty and Administrator Perceptions of AI-Driven Assessment Analytics in Meeting Accreditation Standards in Healthcare Management Education Author: Dr. David Bull International Journal of Computer Science and Information Technology Research ISSN 2348-1196 (print), ISSN 2348-120X (online) Vol. 14, Issue 1, January 2026 - March 2026 Page No: 66-87 Research Publish Journals Website: www.researchpublish.com Published Date: 02-February-2026 DOI: https://doi.org/10.5281/zenodo.18457736 Paper Download Link (Source) https://www.researchpublish.com/papers/faculty-and-administrator-perceptions-of-ai-driven-assessment-analytics-in-meeting-accreditation-standards-in-healthcare-management-education