THE IMPORTANCE OF INSTITUTIONAL AI DIRECTIONS: AN EXPLORATORY FACTORY ANALYSIS (EFA) IN IDENTIFYING FACTORS OF AI IN CLASSROOM CONTEXT AND THEIR RELATIONSHIP TO ACADEMIC INTEGRITY

Abstract

This study investigates institutional directions for integrating Artificial Intelligence (AI) in higher education classrooms and their relationship to academic integrity. Given the rapid adoption of AI technologies in education and concomitant concerns about ethical use and academic honesty, the research aims to empirically identify and validate latent factors that influence AI implementation and integrity outcomes. Utilizing an exploratory quantitative design, a self-administered questionnaire was distributed to 751 undergraduate students at the City College of Calamba. The instrument, developed based on literature review and pilot-tested for reliability, comprised 12 Likert-scale items per latent variable assessing AI usage practices, challenges, awareness, perceived potential, and academic integrity principles. Exploratory Factor Analysis (EFA) revealed five distinct factors—AI Usage Practices, AI-Related Challenges and Issues, Awareness and Ethical Considerations, Perceived Potential of AI, and Academic Integrity—that collectively accounted for approximately 62% of total variance. Factor loadings ranged notably from 0.60 to 0.85, confirming the construct validity and internal consistency of the measurement model. Students generally expressed agreement (grand mean = 3.25) with responsible AI use strategies, highlighting a mature stance on maintaining human interaction and academic honesty while mitigating reliance on AI. Moderate inter-factor correlations (0.35 to 0.58) underscored the interactive influence among these latent constructs. The findings emphasize the importance of clear institutional AI guidelines, professional development focused on ethical AI use, and inclusive engagement of educational personnel beyond teaching roles. The study addresses gaps in longitudinal data by recommending further research on evolving perceptions and effective pedagogical models for AI-enhanced learning that uphold integrity. This work contributes to understanding the multifaceted dynamics of AI adoption in education, offering actionable recommendations to foster sustainable, ethical, and inclusive AI integration that safeguards academic standards.