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arXiv AI By Pierre Haritz, Hendrik Krone, Thomas Liebig

Addressing Trust in AI Systems through Education: A Didactic Perspective

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The paper introduces ICE-T, a didactic framework designed to improve trust in AI systems through education. It combines intermodal transfer, computational thinking, and explanatory thinking to address the opacity of machine learning tools. By linking these facets to research on algorithm aversion and AI literacy, the authors argue that trust calibration should be an explicit educational goal.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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