Lam, Wai ChengWai ChengLam2025-12-152025-12-152025-08https://dspace.usj.edu.mo/handle/123456789/6749This study employed a cross-sectional survey design to examine generational differences and adult learning principles influencing employee engagement in AI learning within Macau’s workforce. Data were collected from 309 full-time employees across diverse industries utilizing validated psychometric instruments assessing key learner variables, included Self-Directedness, Need to Know, Readiness to Learn, Prior Experience, and Motivation, and Perceived Organizational Support (POS). Analytical methods included ANOVA and multiple regression analyses focusing on moderation effects of POS on work engagement outcomes. Results demonstrated that Millennials (ages 20-28) exhibited significantly higher cognitive (M=3.85), emotional (M=3.85), and physical (M=3.74) work engagement compared to Generation X and Baby Boomers (e.g., cognitive engagement: F(2,306)=12.88, p<.001). Millennials also scored notably higher on adult learning variables, particularly in Self-Directedness (β = .47, p < .001), Need to Know (β = .39, p < .001), and Motivation (β = .44, p < .001). Need to Know and Self-Directedness emerged as robust positive predictors of engagement across generational cohorts. Perceived Organizational Support not only directly enhanced work engagement (e.g., emotional engagement for Millennials: β = .89, p < .001) but also significantly moderated the effects of learner variables, especially for older employees facing greater challenges in digital adaptation. These findings underscore the critical importance of tailored adult learning strategies that leverage intrinsic learner motivations and organizational support to foster sustained engagement across generations. Organizations are encouraged to design AI training initiatives that accommodate distinct generational needs, blending autonomy-supportive approaches with structured guidance and robust organizational backing. A key limitation of this study is its reliance on self-reported engagement measures, which may not fully capture actual behavioral changes in workplace AI adoption. Future research should incorporate objective performance metrics to validate and extend these findings.enWORK ENGAGEMENTGENERATIONAL DIFFERENCEADULT LEARNINGLEARNING AIMACAO WORKFORCEtext::thesis::master thesis