Beyond Personalization: Embracing Democratic Learning Within Artificially Intelligent Systems

Abstract

This essay explains how, from the theoretical perspective of Basil Bernstein's three “conditions for democracy,” the current pedagogy of artificially intelligent personalized learning seems inadequate. Building on Bernstein's comprehensive work and more recent research concerned with personalized education, Natalia Kucirkova and Sandra Leaton Gray suggest three principles for advancing personalized education and artificial intelligence (AI). They argue that if AI is to reach its full potential in terms of promoting children's identity as democratic citizens, its pedagogy must go beyond monitoring the technological progression of personalized provision of knowledge. It needs to pay more careful attention to the democratic impact of data‐driven systems. Kucirkova and Leaton Gray propose a framework to distinguish the value of personalized learning in relation to pluralization and to guide educational researchers and practitioners in its application to socially just classrooms.

Utilizing AI for Physics Problem Solving: A Literature Review and ChatGPT Experience

“…While ChatGPT enhances certain aspects of traditional teaching, the lecturer emphasized that it cannot completely supplant human interaction and personalized guidance, which are irreplaceable components of effective teaching. ChatGPT was a valuable tool for supplementing instruction and providing additional resources (Harry, 2023;Song, 2024;, but it could not replicate the mentorship, emotional support, and nuanced feedback that a human educator offers (Kucirkova & Leaton-Gray, 2023). Overall, it suggested the importance of integrating AI tools like ChatGPT into the broader educational framework rather than viewing them as replacements for traditional teaching methods.…”