The AI Triplet: Computational, Conceptual, and Mathematical Knowledge in AI Education
This work addresses the need for systematic AI education frameworks to enhance learning and broaden participation, though it is incremental as it adapts an existing concept from chemistry education.
The paper tackles the problem of understanding AI expertise by proposing the 'AI triplet' framework, which integrates computational, conceptual, and mathematical knowledge, and applies it to topics like tree search and gradient descent to suggest educational improvements.
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.