Trustworthy AI Literacy in Primary Education: A Human-Centred Learning and Measurement Design

Abstract

Generative foundation models can produce plausible outputs without guaranteeing factual or relational correctness. Primary-school learners therefore need accessible ways to translate trustworthy-AI principles into evidence-based judgements and responsible use decisions. This conceptual design paper presents a human-centred learning and measurement design within the Austrian TRUST-AI4Schools project. Human-centredness is operationalised through learner agency, age-appropriate accessibility, evidence ownership, and human authority over consequential use. Using a design-science-informed process, the paper describes prior reference construction: learners establish and validate an inspectable reference before encountering a generated transformation, then compare selected relations and justify whether the output should be used, revised, further checked or withheld for a stated purpose. The design addresses selected aspects of trustworthy AI literacy through verification, provenance awareness and human-controlled use decisions. An author-generated image illustrates the task context. Classroom feasibility and learning effects remain to be evaluated. A proposed augmented reality extension will investigate whether spatially linked evidence supports independent judgement without replacing it with interface feedback.

Keywords: Trustworthy AI, Foundation models, AI literacy, Primary education, Augmented reality

DOI: 10.54941/ahfe1008233