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¶
Claim · All claims
Evidence · 1 study · reviewr3·q2
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person · recheck by 2030: baselines and conditions change
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? Math (n = 27) and language learning (n = 11) are the most popular learning topics, and nearly half of papers specifying a subject target STEM education (n = 39). → Anna Riedmann 2025
q2 i? Most learning environments target college or higher education learners (n = 30), while nearly half of reviewed publications did not specify an educational target group (n = 36). → Anna Riedmann 2025
Evidence¶
Anna Riedmann 2025¶
Anna Riedmann, Philipp Schaper, Birgit Lugrin. (2025). Reinforcement Learning in Education: A Systematic Literature Review. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-025-00494-6
q2 · i? · review · r3
Descriptive analysis of educational contexts across the 89 included papers (Table 1 shows target-group distribution across subjects). The review reports "Most learning environments target college or high education learners (n = 30)" and that 36 papers did not specify a target group.
"Most popular learning topics are math (n = 27) and language learning (n = 11). We also noted physics (n = 3), biology and geology (n = 3), and computer-related topics (n = 6) in more than one publication."
Discussion¶
Related Claims¶
- RL-based educational evaluations split across live evaluation (n = 41), real interaction data (n = 23), and simulated users (n = 24) — related
- Content-related adaptation (n = 53 papers) outnumbers guidance-related adaptation (n = 36) in RL-based educational applications — related
- GBA is predominantly applied in physics education (4 studies), with about 19% of studies not specifying a subject field — related
- Data practice emphasis varies across science disciplines, with environmental science and biology dominating the literature — 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
- 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 — related
- AI-STEM research concentrated in higher education, technology and science content, lecture-based instruction, and face-to-face contexts — a broader claim this one bears on
- 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 |
rl-education-stem-higher-ed-concentration |
title |
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 |
description |
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 |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2025 |
recheck_by |
2030 |
generated |
by process:wiki-ingest on 2026-10-12 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — anna-riedmann-2025 |
Frontmatter is the page's machine-readable record — see the Schema & Guide.