The Interplay of Learning, Analytics, and Artificial Intelligence in Education: A Vision for Hybrid Intelligence

TL;DR

This paper advocates for a broader understanding of AI in education, emphasizing hybrid human-AI systems and conceptualizations beyond tools, to enhance learning and adapt to an AI-ubiquitous future.

Contribution

It introduces three novel conceptualizations of AI in education—externalization, internalization, and extension of human cognition—and discusses their implications.

Findings

Hybrid human-AI systems can extend human cognition.

Current AI conceptualizations may limit educational innovation.

A broader approach to AI in education can better prepare learners for an AI-rich world.

Abstract

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.