This study aims to (1) examine the impact of AI-driven learning tools (AI-LTs) on educational sustainability (EDS) and (2) investigate the mediating role of students’ engagement (SE) and the moderating effect of digital resilience (DR) in this relationship. Based on sociotechnical systems theory (STS), self-determination theory (SDT), and resilience theory, and (3) developing a multidimensional framework to explore how technological, psychological, and contextual factors interact to shape sustainable learning outcomes. Data were gathered from 387 university students in Saudi universities using a standardized questionnaire and subsequently analyzed utilizing SPSS version 28 and PROCESS Macro Version 4.0. The study performed multiple regression and moderated mediation to evaluate the proposed relationships. The results confirmed that AI-LTs significantly enhance educational sustainability. Based on the findings, AI-LTs significantly improve the long-term viability of education, particularly when it is tailored to individual students, encourages active participation, and is logical from a pedagogical perspective. Student engagement was found to influence the relationship, suggesting that when AI tools are utilized effectively, they foster a sustained commitment to education and improved learning outcomes. Furthermore, digital resilience has a significant influence on the connection between AI-LT–EDS, indicating that students who exhibited improved adaptability to digital challenges reaped considerable benefits. The research enhances the existing literature by integrating three complementary frameworks—STS, SDT, and resilience theory—to provide a comprehensive understanding of AI’s role in sustainable education. Practically, the study underscored the importance of AI integration strategies that improve digital resilience, student engagement, and structural imbalance. The results demonstrated that AI usage necessitates significant institutional support and improved technology to establish educational environments that are adaptable, resilient, and easily accessible to students.

Preparing graduates to navigate increasingly uncertain labor markets is an important challenge for higher education and the broader pursuit of sustainable graduate employability. Although higher education institutions invest substantially in developing Career Competencies (CC), comparatively less is known about how these competencies are associated with graduates’ Job Market Readiness (JMR), the role of Career Self-Management (CSM) in this relationship, and whether perceived Labor Market Uncertainty (LMU) conditions the strength of this association. Drawing on Human Capital Theory, Career Construction Theory, and Conservation of Resources Theory, this study develops and empirically examines an integrated framework linking developmental resources, self-regulatory career behavior, employment readiness, and perceived labor market conditions. Data were collected from 398 final-year university students and recent graduates in Saudi Arabia using a non-probability, multi-institutional purposive sampling approach and analyzed using IBM SPSS Statistics 27, AMOS 27, and Hayes’ PROCESS macro. The findings show that CC was positively associated with both CSM and JMR, while CSM was also positively associated with JMR. The analysis further identified a significant indirect statistical association between CC and JMR through CSM, along with a remaining significant direct association. In addition, perceived LMU significantly moderated the CC–JMR relationship, such that the positive association was stronger at higher levels of perceived uncertainty. These findings contribute to graduate employability research by showing that the employment relevance of Career Competencies is associated not only with the resources graduates possess but also with their Career Self-Management and the labor market conditions they perceive. Therefore, sustainable graduate employability is treated as a broader theoretical implication of these relationships rather than an outcome directly measured by this study. These findings provide a basis for future longitudinal research examining whether these associations contribute to more sustainable education-to-work transitions over time.