Insight is yielded into the effectiveness of pedagogical methods and their impact on AI literacy and algorithmic thinking among university students, ultimately contributing to a more informed and AI-competent future workforce.
This study aims to demystify Generative AI literacy and algorithmic thinking while addressing higher education's cognitive divide and pedagogical knowledge. A survey-based approach utilizing Structural Equation Modeling (SEM) was employed. A sample of 340 participants was drawn from various higher education institutions, focusing on university students. The study applies a convenient sampling technique to gather responses. The novelty of this research lies in its comprehensive examination of AI concepts in higher education, targeting students as a pivotal demographic for fostering AI literacy and bridging the cognitive divide. This study yields insights into the effectiveness of pedagogical methods and their impact on AI literacy and algorithmic thinking among university students, ultimately contributing to a more informed and AI-competent future workforce.