Addressing Trust in AI Systems through Education: A Didactic Perspective

Abstract: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 algorithm aversion, AI literacy, and mental model formation, and to systematic reviews of the K-12 ML activity landscape, we argue that the three facets supply the cognitive mechanisms that the trust calibration literature identifies as drivers of appropriate reliance: representational richness, graduated process control, and the capacity to contextualize errors. On this basis, we propose that trust calibration be treated as an explicit educational objective, with ICE-T as a principled and scalable means of achieving it.

Computers and Society (cs.CY); Artificial Intelligence (cs.AI)

arXiv:2609.02453 [cs.CY]

(or arXiv:2609.02453v1 [cs.CY] for this version)

https://doi.org/10.48550/arXiv.2609.02453 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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