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

arXiv: https://arxiv.org/abs/2403.16081 · HTML: https://mlsanity.com/paper/2403.16081/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 61 references that are in the map.
- First posted on arXiv 24 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [Ask Not What AI Can Do, But What AI Should Do: Towards a Framework of Task Delegability](https://mlsanity.com/paper/1902.03245/) (2019)
- [Predicting challenge moments from students' discourse: A comparison of GPT-4 to two traditional natural language processing approaches](https://mlsanity.com/paper/2401.01692/) (2024)
- [An Artificial Intelligence driven Learning Analytics Method to Examine the Collaborative Problem solving Process from a Complex Adaptive Systems Perspective](https://mlsanity.com/paper/2210.16059/) (2022)
- [Impact of combining human and analytics feedback on students' engagement with, and performance in, reflective writing tasks](https://mlsanity.com/paper/2211.08222/) (2022)

## Built on by (most cited first, 9 of 9)

- [Evolution of AI in Education: Agentic Workflows](https://mlsanity.com/paper/2504.20082/) (2025)
- [Generative AI and Agency in Education: A Critical Scoping Review and Thematic Analysis](https://mlsanity.com/paper/2411.00631/) (2024)
- [Rethinking the Potential of Multimodality in Collaborative Problem Solving Diagnosis with Large Language Models](https://mlsanity.com/paper/2504.15093/) (2025)
- [Evaluating Trust in AI, Human, and Co-produced Feedback Among Undergraduate Students](https://mlsanity.com/paper/2504.10961/) (2025)
- [What Students Ask, How a Generative AI Assistant Responds: Exploring Higher Education Students' Dialogues on Learning Analytics Feedback](https://mlsanity.com/paper/2601.04919/) (2026)
- [Exploring Human-AI Complementarity in CPS Diagnosis Using Unimodal and Multimodal BERT Models](https://mlsanity.com/paper/2507.14579/) (2025)
- [Towards Synergistic Teacher-AI Interactions with Generative Artificial Intelligence](https://mlsanity.com/paper/2511.19580/) (2025)
- [Developing a General Personal Tutor for Education](https://mlsanity.com/paper/2512.04869/) (2025)
- [AI-Assisted Code Review as a Scaffold for Code Quality and Self-Regulated Learning: An Experience Report](https://mlsanity.com/paper/2604.23251/) (2026)

## Abstract (arXiv)

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.