Instruction in STEAM AI literacy is predominantly technology-enhanced (82.1% of studies) rather than unplugged (17.9%)¶

Claim · All claims
Evidence · 1 study · review r2 · q3
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i? 82.1% of reviewed studies relied on technology-enhanced activities, while only 17.9% employed unplugged approaches such as card-based simulations or role-playing tasks. → Niri 2026

Evidence¶

Niri 2026¶

Niri, G., Chiu, T. K. F., Ombid, A. M. O., Ybañez, D. L. J. B., Dennerlein, S. M., Zhou, X., & Lavicza, Z. (2026). STEAM education for AI literacy: a systematic literature review. International Journal of STEM Education. https://doi.org/10.1186/s40594-026-00629-8

q3 · i? · review · r2

Analysis of instructional modalities across the reviewed studies (RQ1, Fig. 5). The review reports the printed proportions and suggests unplugged methods "remain valuable for younger learners and introductory contexts", but most research emphasizes tool-supported experiences.

"82.1% of studies relied on technology-enhanced activities, incorporating digital tools, platforms, or programming environments. In contrast, only 17.9% of the studies employed unplugged approaches, such as card-based simulations or role-playing tasks"

Discussion¶

Related Claims¶

• STEAM AI literacy studies rely mainly on mixed (41.0%) and qualitative (35.9%) methods, with large-scale quantitative work limited (23.1%) — related
• Technology disciplines, especially Computer Science and Data Science, lead contributions to AI literacy across TIECD dimensions, while Design (AILE 5.1) is underrepresented across all STEAM clusters — related
• Thematic analysis identified three outcomes: AI concepts became explainable and learnable, participants gained enhanced understanding of classification processes, and participants valued the practical applicability of unplugged approaches — related
• Existing CS and AI unplugged activity collections contain few resources specifically about language models or text generation, leaving a gap LLMs Unplugged fills — related
• An online PD program using unplugged explainable machine-learning activities increased K–12 educators' AI self-efficacy from below-moderate to above-moderate levels — related