This study aimed to compare the effects of teacher feedback (TF) and AI-supported feedback (AIF) on academic achievement, perceived self-regulation, and feedback literacy among 42 sixth-grade students in a private school in Istanbul, Türkiye. Forty-two students were assigned to either a TF group (n=21), which received written feedback from the teacher, or an AIF group (n=21), which received AI-generated feedback through a Python-based natural language processing platform integrated with Cognitive Diagnostic Modelling. Both groups completed weekly quizzes over a four-week intervention period, aligned with English curriculum learning objectives. A 2 (time: pre-test vs. post-test) × 2 (group: TF vs. AIF) mixed-design multivariate analysis of variance (Mixed MANOVA) revealed significant improvements in all measured outcomes from pre-test to post-test (p<.001), with no significant differences between the TF and AIF groups or their interaction. These findings suggest that formative feedback enhances student outcomes regardless of delivery mode. The study underscores the potential of “AI + Teacher” collaborative models in middle school education, supporting essential skills development while addressing resource constraints for individualized feedback.

This study examines differences in students’ performance on familiar examination questions and questions that had not been previously encountered. A comparative document-based design was adopted. Data consisted of responses from 353 undergraduate students in an examination. Performance was analyzed using two examination questions covering topics that had been taught during lectures. The results showed a huge difference in students’ performance. Only 0.6% of students, compared to 40%, answered the question that had not been previously encountered correctly. A paired sample t-test revealed a statistically significant difference between the mean scores (p < 0.05). In addition, a weak positive correlation was observed between performance on the two items (r = 0.125). These findings suggest a strong influence of question familiarity on examination performance. It is recommended that educators employ instructional and assessment practices that enhance conceptual understanding and expose students to a wide range of problem types.

This opinion paper discusses artificial intelligence (AI) within the historical course of theories of learning and the intersections of pedagogy, technology, and policy. Building on the legacy of behaviorism, cognitivism, constructivism, and critical pedagogy, AI is interrogated not only as a technical tool but also as a transformative force that conceptualizes of educational thought. Starting with a vignette invites readers to analytically question whether AI represents a continuity of earlier patterns or a paradigmatic disagreement, and whether it positions learners as empowered mediators or as datafied objects within algorithmic systems. The study highlights that the future of learning depends on balancing two forces: the historical needs of education and the new technical possibilities created by AI. It argues that AI’s strengths—such as personalization, adaptability, and creativity—should be combined with education’s long-term commitments to equity, diversity, and democracy.The contribution lies in incorporating theoretical reflection with narrative pedagogy, offering a background for educators to involve with AI not merely as a technical phenomenon but as a socio-political and epistemological challenge restructuring education in the 21st century.