arXiv AI By Rubens Lacerda Queiroz, Cabral Lima, Fabio Ferrentini Sampaio, Priscila Machado Vieira Lima

How do machines learn? Evaluating the AIcon2abs method

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arXiv:2401. 07386v5 Announce Type: cross Abstract: This study expands on previous work that introduced the AIcon2abs method (AI from Concrete to Abstract: Demystifying Artificial Intelligence to the general public), an innovative approach designed to increase public understanding of machine learning (ML) across diverse age groups, including K-12 students, and aims to evaluate its effectiveness.

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arXiv AI
Sep 3

Addressing Trust in AI Systems through Education: A Didactic Perspective

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.

By Pierre Haritz, Hendrik Krone, Thomas Liebig