AI-STEM research concentrated in higher education, technology and science content, lecture-based instruction, and face-to-face contexts¶
Claim · All claims
Evidence · 1 study · reviewr2·q2
Currency · newest study 2022 · not yet checked against its sources · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? Most AI techniques except educational robots were applied in higher education with medium and large scale learners; lecturing was the most frequent instructional strategy; computer system was the most frequent medium; and face-to-face was the dominant environment. → Weiqi Xu and Fan Ouyang 2022
Evidence¶
Weiqi Xu and Fan Ouyang 2022¶
Weiqi Xu and Fan Ouyang. (2022). The application of AI technologies in STEM education: a systematic review from 2011 to 2021. International Journal of STEM Education. https://doi.org/10.1186/s40594-022-00377-5
q2 · i? · review · r2
The review's distribution analysis of 63 studies found 43 of 59 level-reporting studies focused on higher education, with medium scale (N = 24) and large scale (N = 21) samples outnumbering small scale (N = 10). Instructors most frequently used lecturing (N = 27), computer system resource was the most used medium (N = 28), and face-to-face environment dominated (N = 33 of 51).
"Third, a majority of AI techniques (except educational robots) were applied in higher education with medium and large scale of learners. The most fre- quently used AI in higher education were learning predic- tion and ITSs."
Discussion¶
Related Claims¶
- Research on generative AI in secondary STEM education is concentrated in the Global North, with 73% of affiliations and the United States accounting for 30% of publications
- AI-motivation research is geographically and educationally concentrated: over half of reviewed studies were conducted in China and most in higher education
- RL-based educational applications concentrate on STEM subjects (n = 39 of 62 papers specifying a subject) and on college/higher-education learners (n = 30), with 36 papers not specifying a target group — a narrower finding that bears on this claim
- The evidence base is dominated by US studies and quantitative designs, with sample sizes ranging from one teacher to 53 schools and 1,323 teachers — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
ai-stem-distribution-higher-ed-lecture-face-to-face |
title |
AI-STEM research concentrated in higher education, technology and science content, lecture-based instruction, and face-to-face contexts |
description |
AI-STEM research concentrated in higher education, technology and science content, lecture-based instruction, and face-to-face contexts |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2022 |
generated |
by process:wiki-ingest on 2026-10-16 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — weiqi-xu-and-fan-ouyang-2022 |
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