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.

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，着重探讨了人工智能、数据分析与学习过程之间复杂的相互作用。在此，我对当前将人工智能狭隘概念化为随机工具（如生成式人工智能所例示）的主流观点提出质疑，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，并指出人工智能亦可作为理解人类学习的工具。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，但已偏离这一视角，这促使我们需要重新激活这种联系。本文提出了教育中人工智能的三种独特概念化：人类认知的外化、内化人工智能模型以影响人类心智模型，以及通过紧密集成的人机系统扩展人类认知。通过审视当前研究与实践中作为这三种概念化实例的案例，本文阐述了每种概念化对教育的潜在价值与局限，以及过度强调外化人类认知的风险。本文认为，人工智能模型可作为思考学习的有效对象，尽管学习的某些方面或许只能通过亲历学习过程的缓慢体验而获得，无法完全用可被预测性破解的人工智能模型来解释。最后，本文主张采用更广泛的教育人工智能研究路径，其不仅应关注教育中人工智能解决方案的设计与开发，还应包含人工智能普及教育以及创新教育系统，使其在人工智能无处不在的世界中保持其现实相关性。