AI-supported PBL students obtain significantly higher posttest computational thinking scores than traditional-lecture students when controlling pretest scores¶
Claim · All claims
Evidence · 1 study (2 entries) · causalr1–r2·q2·i3large
Currency · newest study 2026 · 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 i3 In a quasi-experiment with 103 first-year Nigerian undergraduates, the AI-PBL group showed a statistically significant main effect of instructional group on posttest computational thinking, F(1, 70) = 149.23, p < .001, partial η2 = .681, with adjusted means of 3.81 (AI-PBL) versus 2.40 (control). → Ayanwale 2026
q2 i? The computational thinking benefit of AI-PBL was statistically significant for both female students (F = 105.50, p < .001) and male students (F = 54.79, p < .001). → Ayanwale 2026 (2)
Evidence¶
Ayanwale 2026¶
Ayanwale, M. A., & Omeh, C. B. (2026). AI-supported problem-based learning for enhancing computational thinking skills in STEM education. Computers in Human Behavior: Artificial Humans 7 (2026) 100263. https://doi.org/10.1016/j.chbah.2026.100263
q2 · i3 · causal · r2
One-way ANCOVA in a quasi-experimental pre-test–post-test non-equivalent groups study of 103 first-year undergraduates (51 AI-PBL, 52 lecture control), controlling Pre_CT_Total and gender. The adjusted marginal mean for the experimental group (M = 3.81, SE = 0.08) exceeded the control group (M = 2.40, SE = 0.08), "partial η2 = .681".
"The results revealed a statistically significant main effect of instructional group, F (1, 70) = 149.23, p < .001, partial η2 = .681, indicating a large effect size"
Ayanwale 2026 (2)¶
Ayanwale, M. A., & Omeh, C. B. (2026). AI-supported problem-based learning for enhancing computational thinking skills in STEM education. Computers in Human Behavior: Artificial Humans 7 (2026) 100263. https://doi.org/10.1016/j.chbah.2026.100263
q2 · i? · causal · r1
Simple main effects follow-up from the same ANCOVA study, reported in the Results section, showing the instructional benefit was "consistent across genders"; no effect sizes are printed for these follow-up tests.
"Further analysis using simple main effects showed that this group difference was statistically significant for both female students, F = 105.50, p < .001, and male students, F = 54.79, p < .001"
Discussion¶
Related Claims¶
- AI-PBL students achieve high normalized learning gains (g = 0.73) in computational thinking and academic achievement while control students record low gains (g ≤ 0.20) — related
- AI-supported PBL students obtain significantly higher posttest academic achievement scores in robotics programming than traditional-lecture students when controlling pretest scores — related
- Gender does not significantly moderate the effect of AI-supported PBL on computational thinking or academic achievement — related
- Problem-based learning environments consistently improve students' critical thinking compared with lecture-based instruction — related
- Effectiveness of CT instruction varies with year level: first-year students benefited most under general or infusion approaches — related
- In an unscaffolded GenAI-permitted instructional design course, the control group outperformed the experimental group on posttest instructional design performance — related
- Effects of P(j)BL on CT are mixed across studies, with positive, null, and negative findings reported — related
- STEM students using real-time AI feedback achieved nearly double the mean gain score of students receiving conventional human-led instruction — related
- Structured AI-supported writing instruction produced a large posttest writing proficiency advantage over traditional instruction in Saudi EFL undergraduates — related
- Inquiry skills improved significantly only in the computer-based inquiry group, which outperformed the regular-classroom group at posttest — related
- More years of coding experience were associated with better performance in AI-supported PBL courses (exploratory) — related
- Student engagement is higher during PBL than during traditional instruction — related
- Tutorial group size moderates SDL gains in PBL: small and medium groups gained SDL, large groups declined — related
- The post-test advantage of generative AI-supported instruction persists after controlling for pre-test performance (ANCOVA) — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
ai-pbl-higher-posttest-computational-thinking |
title |
AI-supported PBL students obtain significantly higher posttest computational thinking scores than traditional-lecture students when controlling pretest scores |
description |
AI-supported PBL students obtain significantly higher posttest computational thinking scores than traditional-lecture students when controlling pretest scores |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
generated |
by process:wiki-ingest on 2026-10-12 |
trust |
unverified — no one has confirmed this page's content |
sources |
2 — ayanwale-2026, ayanwale-2026-2 |
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