AI Literacy Assessment Revisited: A Task-Oriented Approach Aligned with Real-world Occupations

TL;DR

This paper introduces a task-oriented AI literacy assessment aligned with real-world job skills, emphasizing practical competencies over technical knowledge, demonstrated through a US Navy robotics training program.

Contribution

It develops a novel AI literacy assessment focused on practical, work-related skills and validates its effectiveness in a professional training context.

Findings

Scenario-based assessment outperformed traditional tests in measuring applied AI literacy.

Practical, contextualized tasks better evaluate AI skills relevant to real-world work.

Emphasizing practical skills improves AI literacy assessment for non-technical workers.

Abstract

As artificial intelligence (AI) systems become ubiquitous in professional contexts, there is an urgent need to equip workers, often with backgrounds outside of STEM, with the skills to use these tools effectively as well as responsibly, that is, to be AI literate. However, prevailing definitions and therefore assessments of AI literacy often emphasize foundational technical knowledge, such as programming, mathematics, and statistics, over practical knowledge such as interpreting model outputs, selecting tools, or identifying ethical concerns. This leaves a noticeable gap in assessing someone's AI literacy for real-world job use. We propose a work-task-oriented assessment model for AI literacy which is grounded in the competencies required for effective use of AI tools in professional settings. We describe the development of a novel AI literacy assessment instrument, and accompanying formative assessments, in the context of a US Navy robotics training program. The program included training in robotics and AI literacy, as well as a competition with practical tasks and a multiple choice scenario task meant to simulate use of AI in a job setting. We found that, as a measure of applied AI literacy, the competition's scenario task outperformed the tests we adopted from past research or developed ourselves. We argue that when training people for AI-related work, educators should consider evaluating them with instruments that emphasize highly contextualized practical skills rather than abstract technical knowledge, especially when preparing workers without technical backgrounds for AI-integrated roles.