GenAI-assisted education produces significantly higher learning motivation than non-GenAI approaches (g = 0.81)¶

Claim · All claims
Evidence · 2 studies · 2 quant-synthesis r2–r3 · q3–q4 · i2–i3
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i3 Pooled effect sizes show GenAI-assisted education significantly improves learning motivation relative to non-GenAI approaches, with a large effect. → Ying Dong 2026

q4 i2 Students using GenAI showed more learning motivation than students in comparison conditions, with a pooled Hedges' g of 0.764. → Fang L 2026

q4 i? Between-study heterogeneity was very high (I2 = 93.7%), so the pooled effect should be read as a positive average effect rather than a stable effect across all educational contexts. → Fang L 2026

Evidence¶

Ying Dong 2026¶

Ying Dong. (2026). Generative AI technologies and educational outcomes: a comprehensive meta-analysis comparing traditional and AI-driven approaches. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-06903-y

q3 · i3 · quant-synthesis · r2

Random-effects meta-analysis of learning-motivation comparisons among the included studies. The pooled estimate was "g = 0.81; 95%CI = 0.20 ~ 1.41, large effect", reported alongside the achievement, thinking, and writing outcomes in Fig. 3.

"learning motivation (g = 0.81; 95%CI = 0.20 ~ 1.41, large effect)"

Fang L 2026¶

Fang L, Li Z, Xiong B and Lu Z (2026). Effects of generative artificial intelligence on students' learning motivation: evidence from a meta-analysis. Front. Psychol. 17:1832451. https://doi.org/10.3389/fpsyg.2026.1832451

q4 · i2 · quant-synthesis · r3

Random-effects meta-analysis of 56 effect sizes from 42 controlled experimental and quasi-experimental studies of 6,059 participants (2023–2025). The pooled effect was "Hedge's g = 0.764" with a 95% CI of [0.566, 0.962]; heterogeneity was I2 = 93.7%, Tau2 = 0.515, Q[55] = 866.132, p < 0.001.

"As Hedge's g = 0.764, 95% CI =[0.566,0.962],95% PI=[−0.689,2.217]; z=7.571,p <0.001, studentsusingGenAIshowedmorelearningmotivationthanother students."

Studies bearing on this claim¶

Does learning with generative AI raise learners' motivation compared with learning without it, and where? This table gathers the studies behind this claim and the 14 narrower claims that bear on it: 10 distinct studies (5 causal, 2 quant-synthesis, 1 review, 1 qualitative, 1 associational), q2–q4, 4 of 10 with an effect size coded. One row per study, codes as each entry records them; no effect is averaged and no verdict is drawn. Built by scripts/synthesize_claim.py from eval/synthesis/; a row that looks wrong is fixed on the claim it comes from.

Discussion¶

Learner Variables¶

• Motivation — outcome: instruction changes it

Related Claims¶

• GenAI-assisted education produces significantly higher academic achievement than non-GenAI-assisted approaches (g = 0.40) — related
• GenAI-assisted education produces significantly higher higher-order thinking abilities than non-GenAI approaches (g = 0.72) — related
• Effect sizes across GenAI studies are highly heterogeneous, requiring random-effects pooling — related
• Motivational orientation is strongly associated with the role students assign to GenAI: intrinsically motivated students most often position it as an instructor, extrinsically motivated students as a replacement tool — related
• GenAI effects on externally assessed learning are highly heterogeneous and conditional on prior knowledge, scaffolding, verification, and task design, with pooled effects unstable under publication-bias correction — related
• GenAI-related misconduct allegations rose from 11.9% of all misconduct cases in 2023 to 36.9% in 2025 against a stable total caseload — related
• Gen-AI has a moderate positive overall effect on students' higher-order thinking (g = 0.609) across 29 experimental and quasi-experimental studies — related
• AI-assisted pair programming significantly increased undergraduate intrinsic motivation compared to individual programming, with outcomes comparable to human–human pair programming — a narrower finding that bears on this claim
• ChatGPT use shows a large positive pooled effect on students' learning performance (g = 0.867) across 44 experimental and quasi-experimental studies — related
• Student-centered, problem-driven learning has a small, positive, heterogeneous effect on students' motivation compared to teacher-centered instruction (d = 0.498) — related
• Daily AI use duration shows a weak positive correlation with academic motivation among university students — related
• Exam environment critically moderates GenAI learning effects: significant large gains when GenAI is permitted during assessment (g = 0.76) versus non-significant slightly negative effects when it is not (g = -0.06). — related
• The LLM-based journaling assistant shows no significant effect on students' intrinsic motivation for creating a learning journal — related