This paper presents a multi-dimensional view of AI's role in learning and education, emphasizing the intricate interplay between AI, analytics, and the learning processes. Here, I challenge the prevalent narrow conceptualization of AI as stochastic tools, as exemplified in generative AI, and argue for the importance of alternative conceptualizations of AI. I highlight the differences between human intelligence and artificial information processing, the cognitive diversity inherent in AI algorithms, and posit that AI can also serve as an instrument for understanding human learning. Early learning sciences and AI in Education research, which saw AI as an analogy for human intelligence, have diverged from this perspective, prompting a need to rekindle this connection. The paper presents three unique conceptualizations of AI in education: the externalization of human cognition, the internalization of AI models to influence human mental models, and the extension of human cognition via tightly integrated human-AI systems. Examples from current research and practice are examined as instances of the three conceptualizations, highlighting the potential value and limitations of each conceptualization for education, as well as the perils of overemphasis on externalizing human cognition. It is argued that AI models can be useful as objects to think about learning, even though some aspects of learning might just come through the slow experience of living those learning moments and cannot be fully explained with AI models to be hacked with predictions. The paper concludes with advocacy for a broader approach to AI in Education that goes beyond considerations on the design and development of AI solutions in education, but also includes educating people about AI and innovating educational systems to remain relevant in an AI-ubiquitous world.

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