Yovav Eshet E113 Proceedings of the 21st Chais Conference for the Study of Innovation and Learning Technologies: Learning in the Digital Era I. Blau, A. Caspi, Y. Eshet-Alkalai, N. Geri, Y. Kalman, D. Olenik-Shemesh, Y. Sidi, & N. Brandel (Eds.), Ra'anana, Israel: The Open University of Israel Academic Integrity among Israeli Students During COVID-19: Lessons for the AI Crisis (Poster) Yovav Eshet Zefat Academic College yovave@zefat.ac.il יושרה אקדמית בישראל בתקופת הקורונה: לקחים עבור משבר הבינה המלאכותית )פוסטר( יובב עשת המכללה האקדמית צפת .ac.il zefat yovave@ Abstract This study integrates complementary subjective and objective empirical perspectives to examine academic integrity in Israeli higher education across different phases of the COVID-19 pandemic and to derive implications for the responsible use of generative artificial intelligence (AI). The research is grounded in the assumption that integrity-related behavior under technological disruption cannot be fully understood through a single methodological lens. Subjective self-reported data capture students’ motivations, perceptions, and ethical self-assessments, while objective machine-based analysis of academic assignments provides an external, behavior-focused measure of misconduct. Together, these approaches offer a more comprehensive account of academic integrity dynamics. The subjective component draws on survey data collected from 1,090 undergraduate students across five-time spans, from the pre-pandemic period through long post-pandemic stabilization. Guided by Self-Determination Theory and the Big Five personality framework, the surveys assessed intrinsic and extrinsic learning motivation, personality traits, and self-reported engagement in academic misconduct, including plagiarism and unauthorized collaboration. This perspective enables insight into how students interpret, rationalize, and regulate their academic behavior, particularly under conditions of uncertainty and stress. Complementing this self-reported perspective, the objective component analyzed plagiarism rates in 25,864 academic assignments collected from multiple Israeli higher education institutions across three periods: pre-pandemic, during the pandemic, and postpandemic. Automated plagiarism detection software was employed to identify longitudinal changes in misconduct patterns using consistent, machine-based criteria that are independent of students’ self-perceptions or reporting biases. Findings from both data sources converge in demonstrating a substantial increase in academic misconduct during crisis periods, with peak levels observed during the early stages of the pandemic. Subjective data indicate that extrinsic motivation is a strong predictor of self-reported misconduct,
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