Comparing Human and Machine Cognition: A Scoping Review with Implications for AI in Education

As artificial intelligence systems become more integrated into society, comprehending their cognitiveprocesses and how these processes contrast with human cognition has emerged as an important concernfor educators. Motivated by Thinking Machines Lab, the paper provides a conceptual review thatexamines the principles of machine "thinking," linking them to current theories of human cognition fromeducation and cognitive science. Despite progress in neural networks and deep learning that allowscomputers to replicate some elements of memory, attention, and reasoning, there are still deficiencies inour comprehension of their functioning and the adaptation of their capabilities to meet diverse humanneeds. This research examines the difference between public comprehension of AI systems and the fastprogression of technology, highlighting challenges related to interpretability, generalizability, and usercentriccustomization. It argues that bridging these gaps is essential for educators and students toeffectively and ethically use AI in education. Received: 11 December 2025 / Accepted: 30 January 2026 / Published: March 2026