SOURCE-LINKED INTELLIGENCE
Addressing Trust in AI Systems through Education: A Didactic Perspective
Machine learning (ML) education faces two persistent and connected obstacles: many educational tools present ML as an opaque black box, which leaves learners with a superficial understanding, and this same opacity prevents users from forming the calibrated trust that appropriate reliance on AI systems requires. We present ICE-T, a didactic framework that integrates three mutually reinforcing facets: intermodal transfer grounded in Bruner's enactive, iconic, and symbolic modes of representation, computational thinking operationalized through the Use-Modify-Create progression, and explanatory th
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-02T11:20:14.000Z
First collected: 2026-09-21T05:32:15.665Z. This is not the publication date.