Artificial Intelligence and Academic Integrity in Virtual Higher Education: A Descriptive-Comparative Study of Student and Faculty Perceptions in Ecuador

Abstract

The rapid adoption of generative artificial intelligence (GenAI) has created a practical problem for virtual higher education: universities must distinguish legitimate AI-supported learning from undisclosed delegation of academic work, while maintaining valid, fair, and privacy-sensitive assessment. This study compared student and faculty perceptions of AI use and academic integrity at the Technical University of Machala, Ecuador. A descriptive-comparative cross-sectional survey was administered to 1660 students and 34 faculty members during the second academic semester of 2024. The student questionnaire examined AI-use frequency, perceived academic benefit, readiness for non-assisted assessment, observation of dishonest online practices, perceived efficacy of virtual assessment, and attitudes toward proctoring. The faculty questionnaire examined suspected AI-generated submissions, responses to suspected use, perceived assessment efficacy, training, control tools, ethical judgments and proctoring. Findings indicate a transitional integrity landscape: students view AI mainly as a useful academic support, whereas faculty interpret it primarily through authorship, evidence and assessment-security concerns. Both groups report limitations in current virtual assessment, suggesting the need for AI-resilient assessment design, explicit disclosure rules, faculty development, student AI literacy, and proportional use of proctoring. The article argues against both blanket prohibition and permissive ambiguity, proposing a governance model grounded in transparent policy, authentic assessment, due process and human-centered AI literacy.