Date
Publisher
arXiv
Artificial intelligence (AI) literacy is increasingly recognized as a foundational competency for all university graduates. Yet students' engagement with AI tools often clusters at two extremes: avoidance driven by fear, mistrust, ethical concern, or lack of access, and uncritical reliance that produces fluent output while masking misunderstanding. Existing AI literacy frameworks provide valuable competency definitions, but most offer limited guidance for diagnosing where learners begin and how they progress toward responsible, critical engagement. This paper proposes a five-stage AI Literacy Continuum: 0) Not Yet Engaged, 1) Uncritical Use, 2) Informed Use, 3) Critical Evaluation, and 4) Improvement --that describes developmental orientations toward AI use in higher education. The continuum complements dimensional frameworks by providing educators with a practical diagnostic and instructional pathway aligned with international frameworks, including UNESCO and OECD. We present a design-based implementation case from North Carolina State University, where credit-bearing courses and intensive hands-on workshops engaged more than 330 participants between Fall 2024 and Spring 2026. Because the implementation did not use a validated pre/post instrument or comparison group, we frame the findings as observational and practice-based: participants exhibited behaviors consistent with movement from non-engagement or uncritical use toward informed engagement, while sustained and discipline-embedded experiences produced stronger evidence of critical evaluation and improvement-oriented practice. We discuss curricular pathways, opportunity considerations, assessment strategies, and argue that AI literacy should be understood not as tool adoption alone but as a developmental capacity to understand, evaluate, and responsibly apply AI systems in disciplinary and societal contexts.
Study at a glance
- Question: How can a developmental continuum help educators diagnose, instruct, and assess students' AI literacy progression?
- Setting and data: North Carolina State University, Fall 2024 to Spring 2026, over 330 participants across courses and workshops.
- Method: Design-based research with observational practice-based data from surveys, classroom observations, and student artifacts.
Why it matters
Students' engagement with AI tools often polarizes between avoidance and uncritical reliance. Existing frameworks catalog competencies but provide limited guidance for diagnosing where learners begin and how they progress toward responsible, critical engagement.
Key findings
- Brief one- to two-day training workshops enabled participants to demonstrate behaviors consistent with movement from non-engagement or uncritical use toward informed engagement.
- Sustained engagement through credit-bearing courses and repeated workshops supported stronger participant progression toward critical evaluation and improvement-oriented practice.
- High school students, undergraduates, graduate students, and international visitors demonstrated consistent patterns of progression across the developmental stages.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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