Toward a Coherent AI Literacy Pathway in Technology Education: Bibliometric Synthesis and Cross-Sectional Assessment

Abstract

Rapid advances in artificial intelligence (AI) are reshaping curricula and work, yet technology and engineering education lack a coherent, critical AI literacy pathway. In this study, we (1) mapped dominant themes and intellectual bases and (2) compared AI literacy between secondary technical students and pre-service technology and engineering teachers to inform curriculum design. Moreover, we conducted a Web of Science bibliometric analysis (2015–2025) and derived a four-pillar framework (Foundational Knowledge, Critical Appraisal, Participatory Design, and Pedagogical Integration) of themes consolidated around GenAI/LLMs and ethics, with strong growth (1259 documents, 587 sources). Phase 2 was a cross-sectional field study (n = 145; secondary n = 77, higher education n = 68) using the AI literacy test. ANOVA showed higher total scores for pre-service teachers than secondary technical students (p = 0.02) and a sex effect favoring males (p = 0.01), with no interaction. MANCOVA found no multivariate group differences across 14 competencies, but univariate advantages for pre-service technology teachers were found in understanding intelligence (p = 0.002) and programmability (p = 0.045); critical AI literacy composites did not differ by group, while males outperformed females in interdisciplinarity and ethics. We conclude that structured, performance-based curricula aligned to the framework—emphasizing data practices, ethics/governance, and human–AI design—are needed in both sectors, alongside measures to close gender gaps.