The AI Triplet: Computational, Conceptual, and Mathematical Knowledge in AI Education

TL;DR

This paper introduces the 'AI triplet' concept, integrating computational, conceptual, and mathematical knowledge as foundational for AI education, and explores its potential to improve teaching and learning strategies.

Contribution

It establishes a theoretical framework for the AI triplet and demonstrates its application to AI topics like tree search and gradient descent.

Findings

Proposes the AI triplet as a foundational framework for AI education

Shows how the triplet maps onto key AI topics

Suggests hypotheses for educational impact

Abstract

Efforts to enhance education and broaden participation in AI will benefit from a systematic understanding of the competencies underlying AI expertise. In this paper, we observe that AI expertise requires integrating computational, conceptual, and mathematical knowledge and representations. We call this the ``AI triplet,'' similar in spirit to the ``chemistry triplet'' that has heavily influenced the past four decades of chemistry education research. We describe a theoretical foundation for this triplet and show how it maps onto two sample AI topics: tree search and gradient descent. Finally, just as the chemistry triplet has impacted chemistry education in concrete ways, we suggest two initial hypotheses for how the AI triplet might impact AI education: 1) how we can help AI students gain proficiency in moving between the corners of the triplet; and 2) how all corners of the AI triplet highlight the need for supporting students' spatial cognitive skills.