Education systems have entered a profound period of transformation driven by the integration of artificial intelligence (AI) and intelligent technologies. AI-supported personalized learning environments possess the potential to delivercustomized instructional, feedback, and developmental experiences tailored to individual students’ learning paces, modalities,and discrete needs. However, the widespread deployment of these tools without rigorous analyses of their efficacy, accuracy,and efficiency carries the risk of negating their anticipated pedagogical benefits. This research examines the cognitive, affective,and behavioral dimensions of high school students’ experiences within AI-driven learning ecosystems. Employing a convergentmixed-methods design, quantitative data were gathered via a validated 20-item instrument from 593 secondary students (grades9–12) at a Turkish Anatolian High School, supplemented by qualitative open-ended experiential evaluations. The quantitativeresults revealed moderate levels of perceived utility, with critical structural trends: while 46.21% reported benefits in understanding complex concepts, a substantial segment ( 31–35%) remained undecided across metrics such as GPA impact andgeneral engagement. Qualitative thematic analysis identified prominent concerns regarding AI hallucinations, data privacy,and academic integrity. The findings yield actionable, data-driven insights for educators, researchers, and EdTech developersto facilitate the systematic and ethically sound integration of AI tools at the secondary education level.
Mustafa Enes Kayacı (Mon,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: