Issue:
Volume 1, Issue 3, September 2026
Pages:
117-129
Received:
3 August 2026
Accepted:
17 August 2026
Published:
9 November 2026
DOI:
10.11648/j.sdai.20260103.12
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Views:
Abstract: Generative artificial intelligence (GenAI) can produce polished mathematical solutions, executable code, persuasive media, and professional recommendations, yet fluency does not establish correctness, provenance, security, or contextual fitness. Existing AI-literacy frameworks appropriately describe broad competencies, while emerging studies in mathematics, programming, and health professions education show the value of domain-specific validation. What remains underdeveloped is a portable instructional process that makes verification, uncertainty, and reliance observable across disciplines. This conceptual article introduces TRACE-AI: Translate the task and stakes; Represent the problem, assumptions, and constraints; Audit the AI's reasoning and output; Corroborate pivotal claims through independent evidence, tests, or tools; Estimate uncertainty and warranted reliance; Act proportionately by accepting, revising, rejecting, or escalating; and Internalize learning through reflection and unaided transfer. The framework was developed through a theory-informed critical synthesis of AI literacy, mathematical problem solving, computational thinking and debugging, epistemic cognition, self-regulated learning, and trust calibration. Each stage produces an assessable artefact, and a risk gate increases verification and human oversight as uncertainty, irreversibility, or potential harm rises. Worked applications in mathematics, programming/cybersecurity, and health-professional reasoning demonstrate invariant functions with discipline-specific standards of proof. An assessment architecture distinguishes final-answer quality from process quality, appropriate reliance, confidence calibration, verification quality, and delayed unaided transfer. Six propositions define a research programme for comparative and randomized evaluation. TRACE-AI is not presented as an effectiveness-tested intervention; it is a transdisciplinary curriculum and research architecture requiring contextual adaptation and empirical validation. Its contribution is to move GenAI education beyond prompt production toward disciplined human judgment about when AI output deserves trust.
Abstract: Generative artificial intelligence (GenAI) can produce polished mathematical solutions, executable code, persuasive media, and professional recommendations, yet fluency does not establish correctness, provenance, security, or contextual fitness. Existing AI-literacy frameworks appropriately describe broad competencies, while emerging studies in mat...
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