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 conceptualisations 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 thought processes, 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 conceptualisations, highlighting the potential value and limitations of each conceptualisation for education, as well as the perils of overemphasis on externalising human cognition as exemplified in today's hype surrounding generative AI tools. The paper concludes with an advocacy for a broader educational approach that includes educating people about AI and innovating educational systems to remain relevant in an AI enabled world.

翻译：本文提出了人工智能在学习和教育中作用的多维度视角，强调人工智能、分析与学习过程之间复杂的相互作用。在此，我挑战了当前普遍将人工智能窄化为随机工具（如生成式人工智能）的概念化观点，并论证了替代性人工智能概念化的重要性。我强调了人类智能与人工信息处理之间的差异、人工智能算法固有的认知多样性，并提出人工智能也可作为理解人类学习的工具。早期学习科学和人工智能教育研究曾将人工智能视为人类智能的类比，但这一视角已逐渐偏离，因此有必要重燃这种联系。本文提出了人工智能在教育中的三种独特概念化形式：人类认知的外化、通过内化人工智能模型来影响人类思维过程、以及通过紧密集成的人机系统扩展人类认知。本文通过当前研究和实践中的案例，检验了这三种概念化的实例，突出了每种概念化在教育中的潜在价值与局限性，并警示了过度强调外化人类认知的风险——这正是当前围绕生成式人工智能工具的热潮中表现出的问题。最后，本文倡导一种更广泛的教育方法，包括教育人们了解人工智能，以及创新教育系统以适应人工智能赋能的世界。