Addressing Trust in AI Systems through Education: A Didactic Perspective

2026-09-02

AI Summary

本文通过提出ICE-T教育框架，旨在解决机器学习教育中的黑箱问题，以增强用户对AI系统的适当信任。

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.

Problem

• Machine Learning Education
• Opaque Black Box
• Calibrated Trust

Innovation

• ICE-T
• Intermodal Transfer
• Computational Thinking
• Explanatory Thinking
• Trust Calibration