AI Literacy and Cognitive Evolution in Generation Z: A Synthesis of Empirical Evidence and Workshop-Based Insights
DOI:
https://doi.org/10.65150/EP-jsshrs/V2E4/2026-05Keywords:
Artificial Intelligence Literacy; Generation Z; Cognitive Dependency; Digital Ethics; AI Workshop; Higher Education; Generative AI; Professional Readiness; PRISMAAbstract
Generative artificial intelligence has transformed from a niche research tool into an ambient feature of daily life for Generation Z (Gen Z), reshaping how this cohort learns, thinks, and prepares for professional futures. This manuscript synthesizes evidence from a systematic literature review (SLR) of 65 peer-reviewed sources spanning 2016 to 2026 with empirical findings from a cross-sectional descriptive-correlational study of 41 high school students at SMK Pembangunan JAYA YAKAPI, Indonesia. Following the PRISMA 2020 protocol, the SLR draws on Scopus, CRIS Reference, and Google Scholar databases. The empirical study assessed students' AI literacy across three subscales—AI understanding and usage, critical and ethical AI literacy, and career and future readiness—and examined their responses to a structured AI literacy workshop. Preliminary Cronbach's alpha values of 0.886 for the AI literacy scale and 0.940 for the workshop response scale indicate strong instrument reliability. A Spearman rank correlation of approximately 0.60 (p < 0.001) between AI literacy and workshop response scores was found among matched respondents, indicating a moderately strong positive association. The synthesis reveals a persistent paradox: while the majority of Gen Z students regularly use AI tools, fewer than 25% can demonstrate foundational knowledge of how these systems operate or govern user data. The AI Literacy is proposed as a practical and theoretically grounded curriculum framework integrating functional, critical, and ethics literacy dimensions. The evidence collectively positions AI ethics literacy as the most consequential lever available to educators seeking to protect intellectual autonomy in an AI-saturated learning environment.
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Copyright (c) 2026 Eka Sudarmaji, Herlan ., M. Rubiul Yatim, Satria Yunas, Roy Prakoso, Rika Kaniati, Octavia Tyara Ramadh, Sabrina Nurulfarah Juwanto (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
