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 thinking supported by a process model. Connecting the framework to the empirical literature on algo...
| Authors | Pierre Haritz, Hendrik Krone, Thomas Liebig |
|---|---|
| Primary category | cs.CY |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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