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与教育中作用的多维视角，重点阐述了人工智能、分析技术与学习过程之间错综复杂的相互作用。在此，我挑战了将人工智能窄化为随机工具（如生成式AI所体现的）的主流观念，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、AI算法中固有的认知多样性，并指出AI亦可作为理解人类学习的工具。早期学习科学及教育领域的人工智能研究曾将AI视为人类智能的类比，但已偏离这一视角，因而亟需重新建立这种联系。本文提出了人工智能在教育中的三种独特概念化：人类认知的外化、AI模型的内化以影响人类思维过程，以及通过紧密整合的人机系统扩展人类认知。通过审视当前研究与实践中的案例作为这三种概念化的实例，本文揭示了每种概念化对教育的潜在价值与局限性，并警示了过度强调人类认知外化的风险——正如当下围绕生成式AI工具的炒作所体现的那样。最后，本文倡导一种更广泛的教育方法，包括向公众普及AI知识以及创新教育体系，使其在AI赋能的世界中保持相关性。