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 conceptualisation of AI as tools, as exemplified in generative AI tools, and argue for the importance of alternative conceptualisations of AI for achieving human-AI hybrid intelligence. I highlight the differences between human intelligence and artificial information processing, the importance of hybrid human-AI systems to extend human cognition, and posit that AI can also serve as an instrument for understanding human learning. Early learning sciences and AI in Education research (AIED), 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 conceptualisations of AI: the externalization of human cognition, the internalization of AI models to influence human mental models, and the extension of human cognition via tightly coupled human-AI hybrid intelligence systems. Examples from current research and practice are examined as instances of the three conceptualisations in education, highlighting the potential value and limitations of each conceptualisation for education, as well as the perils of overemphasis on externalising human cognition. The paper concludes with advocacy for a broader approach to AIED that goes beyond considerations on the design and development of AI, but also includes educating people about AI and innovating educational systems to remain relevant in an AI-ubiquitous 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，强调人工智能、分析技术与学习过程之间错综复杂的相互作用。在此，我质疑当前将人工智能狭隘地概念化为工具的主流观点（以生成式人工智能工具为例），并论证了实现人机混合智能所需替代性概念框架的重要性。我着重阐述了人类智能与人工信息处理之间的本质差异，强调了人机混合系统在扩展人类认知方面的重要性，并提出人工智能亦可作为理解人类学习机制的研究工具。早期学习科学与教育人工智能研究曾将人工智能视作人类智能的类比模型，但当前研究已偏离这一视角，亟需重新建立这种联系。本文提出三种独特的人工智能概念框架：人类认知的外化、影响人类心智模型的人工智能模型内化，以及通过紧密耦合的人机混合智能系统实现人类认知的延伸。通过检视当前研究与实践中的案例，本文阐释了这三种概念框架在教育领域的具体表现，揭示了各自在教育应用中的潜在价值与局限，特别是过度强调人类认知外化可能带来的风险。最后，本文主张拓展教育人工智能的研究范式，其不仅应关注人工智能的设计与开发，更需包含人工智能普及教育，并通过教育系统创新，使其在人工智能无处不在的时代保持持续相关性。