Over the past two decades, the landscape of educational technology has undergone a profound transformation, moving from rudimentary computer-assisted instruction toward sophisticated, adaptive, and now increasingly autonomous intelligent systems. Early personalization efforts, enabled by adaptive learning algorithms, aimed to tailor instructional content to individual learners¿ pace, prior knowledge, and preferences (Holmes et al., 2019). However, advances in machine learning, natural language processing, and cognitive modeling have expanded these systems¿ capabilities to the point where they can make autonomous pedagogical decisions¿recommending learning paths, assessing comprehension in real time, and adjusting not only the pace but also the epistemological framing of content (Luckin, 2024).
