Some scholars argue that humans dominate three main predicates: physical strength, cognition, and metacognition. Regarding physical strength, nearly three centuries ago, with the advent of James Watt's steam engine, machines began to surpass humans with significant advantages. In terms of cognition, the victory of Deep Blue/IBM over Garry Kasparov in 1997, as well as that of AlphaZero/Google over Stockfish 8 in 2017, are emblematic events attesting that, in terms of simple cognition, machines, like physical strength, reaffirmed the trend of surpassing humans in this cognitive field as well. What remains for humans, as the final frontier, is metacognition, potentially serving as a differentiated competitive advantage

The relationship between metacognition and DeepSeek models represents a compelling and yet underexplored area of research. Metacognition refers to a system's capacity to monitor and comprehend its own cognitive processes, which includes the active regulation and adjustment of its procedures. In the DeepSeek-R1 and DeepSeek-R1-Zero, it is evident that the interactions between the system's monitoring and control processes are both present and crucial for achieving the coherent and often surprising levels of reasoning that define these models. Consequently, DeepSeek paves a new avenue for reasoning in Artificial Intelligence (AI) by prioritizing reinforcement learning (RL) over the more conventional approach of supervised fine-tuning (SFT). This study aims to analyse the implications of such innovations for machines' abilities to simulate behaviours based on self-reflection and to act accordingly. We will explore the extent to which these elements can be associated with metacognition, a trait traditionally considered to be uniquely human. Furthermore, from an educational standpoint, the significance of the relationship between advancements in DeepSeek and metacognition is highlighted, particularly in relation to the importance of prioritizing metacognitive approaches in education.