AI-Driven Digital Learning Recognition: Evaluating User Perceptions of Beedik Implementation

This study explores user perceptions of a digital system for AI-based learning recognition called Beedik. As artificial intelligence (AI) transformations in the education system increasingly take hold, understanding how learners interact with the recognition platform becomes crucial to ensuring the system's usability, trustworthiness, and pedagogical value. Using a quantitative survey method, data were collected from 134 users and analyzed through descriptive statistics and PLS SEM. Using a descriptive quantitative approach, perceptions were measured across the dimensions of Access, Clarity, Reliability, Relevance, and Satisfaction. Measurement model testing demonstrated the instrument's reliability and validity (all Cronbach's alpha, rho_A, and Composite Reliability ≥ 0.70; AVE ≥ 0.50), confirming that the indicators consistently reflect the constructs. In the structural model, Reliability and Access had the strongest and most significant influence on satisfaction, but Clarity and Relevance showed a positive but not yet significant direction. These findings emphasize the development priority of system reliability and ease of access, followed by improving clarity and relevance. Implicatively, the study contributes to the literature on educational technology acceptance and supports the lifelong education agenda through systematically designed digital transformation.