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Artificial intelligence (AI) has become ubiquitous in recent decades, but using AI in education is not a recent idea. Since the emergence of AI, there has been an interest in the relationship between AI and education, not only looking at AI as an applied tool to advance education but also investigating its value as an analogy to human intelligence. In this paper, we build on AI’s power to act as an analogy to human intelligence to conceptualize it as a tool for metacognitive reflection. This idea itself is actually several decades old but it has been largely ignored. Given the recent wave of interest in AI literacy, we position the value that AI can bring in helping children reflect on their own thinking and learning as another rationale for teaching AI, in addition to economic, social, and ethical considerations. Moreover, we propose four different approaches to AI that can serve as metacognitive tools: (1) viewing computation as AI, (2) mainstream data-driven AI, (3) large language models, and (4) computational models of human cognition. We contrast the potential of each strand to promote metacognition and provide some toy problems where AI can foster students’ learning about their own learning.
Ojeda-Ramírez et al. (Sat,) studied this question.