THE IMPACT OF AI-POWERED LEARNING PLATFORMS AND PERSONALIZED LEARNING RECOMMENDATIONS ON SKILL DEVELOPMENT

Abstract

This study describes research that was conducted to investigate the relationships of AI-Powered Learning Platforms (APLP), Personalized Learning Recommendations (PLR), and educational skill development (SD). One hundred fifty engineering students at Mehran University (Pakistan) were surveyed in order to collect the data for a validated 12-item Likert-scale questionnaire. It was found that the questionnaire demonstrated high internal consistency, with a Cronbach’s α = .888. Using Pearson's correlation, it was revealed that there is a strong positive relationship between APLP-SD (*r* = .675, *p* < .001), PLR-SD (*r* = .733, *p* < .001), and APLP-PLR (*r* = .779, *p* < .001). To further investigate this issue, linear regression was carried out, and the results confirm that APLP (β = .265, *p* = .003) and PLR (β = .527, *p* < .001) together are significant predictors of SD and they account for 56.5% of the variance (Adj. R² = .559, F (2,147) = 95.495, *p* < .001). However, the fact that PLR has almost twice as much impact as APLP on skill development is also proven by the findings. The discovery of this study reaffirms that AI-controlled adaptive platforms and custom recommendations greatly increase skill acquisition through the provision of learner-centric pathways. This piece of technology does not escape from the scrutiny of ethical issues, such as customer bias and data privacy but, having been introduced in a strategic way, it does support the user’s engagement and brings improvements in in-house competence. The next research attempts should concentrate on the study of the long-term outcomes and the possibility of mixing hybrid models, which helps "competency tunnel vision" to be weakened.