We answer the present paper’s title in the negative. We begin by introducing and characterizing “real learning” (\(\mathcal {RL}\)) in the formal sciences, a phenomenon that has been firmly in place in homes and schools since at least Euclid. The defense of our negative answer pivots on an integration of reductio and proof by cases, and constitutes a general method for showing that any contemporary form of machine learning (ML) isn’t real learning. Along the way, we canvass the many different conceptions of “learning” in not only AI, but psychology and its allied disciplines; none of these conceptions (with one exception arising from the view of cognitive development espoused by Piaget), aligns with real learning. We explain in this context by four steps how to broadly characterize and arrive at a focus on \(\mathcal {RL}\).