In SEM, all three self-efficacy dimensions are positively associated with AI use, with Excellence strongest (β = .47), followed by Communication (β = .31) and Attention (β = .22); the model explains 43% of variance¶

Claim · All claims
Evidence · 1 study (2 entries) · associational r1 · q2 · i3 large
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 Excellence showed the strongest association with AI use (β = .47, p < .001), followed by Communication (β = .31, p < .01) and Attention (β = .22, p < .05), with bootstrap CIs excluding zero. → Suriá-Martínez R 2026 q2 i? The model explained 43% of the variance in AI use (R2 = .43). → Suriá-Martínez R 2026 (2)

Evidence¶

Suriá-Martínez R 2026¶

Suriá-Martínez R, García-Castillo F, López-Sánchez C and García del Castillo JA (2026). Between perceived competence and artificial intelligence: academic self-efficacy profiles in university students with motor disabilities. Front. Educ. 11:1956192. https://doi.org/10.3389/feduc.2026.1956192

q2 · i3 · associational · r1

Structural equation model estimated by maximum likelihood with 1,000 bootstrap resamples (N = 102); 95% bias-corrected CIs remained above zero for all three dimensions. The quote fragment "β = .47, p < .001" grounds the Excellence path; fit indices were RMSEA = .065, CFI = .94, TLI = .92, SRMR = .067.

"Excellence showed the strongest association (β = .47, p < .001), followed by Communication (β = .31, p < .01) and Attention (β = .22, p < .05)."

Suriá-Martínez R 2026 (2)¶

q2 · i? · associational · r1

Variance-explained estimate from the same SEM (N = 102). The quote fragment "R2 = .43" is reported as a model-level variance estimate, not an effect size for a single path.

"The model explained 43% of the variance in AI use (R2 = .43), indicating that the three academic self-efficacy dimensions were jointly associated with 43% of the observed variance in AI use within the sample."

Discussion¶

Self-efficacy, reflective thinking, and help-seeking were associated with topic understanding at the cohort level (r = 0.427, 0.369, 0.168; d = 0.479) — related
Teaching self-efficacy, professional identity, and AI literacy are each significantly associated with AI teaching innovation behavior, with additional links among the mediators — related
Pedagogical AI competence is positively associated with teachers' AI use — related
Three academic self-efficacy profiles (low, moderate, high) are identified among university students with motor disabilities using Latent Profile Analysis — related
Six predictor constructs jointly explain 44.0% of the variance in ethical GenAI use among university students, with all structural paths positive and significant — related
Thoughtless use of generative AI significantly and negatively predicts both self-efficacy and motivation — related

Page metadata

Field	Value
`type`	`claim`
`id`	`sem-self-efficacy-dimensions-ai-use-associations`
`title`	`In SEM, all three self-efficacy dimensions are positively associated with AI use, with Excellence strongest (β = .47), followed by Communication (β = .31) and Attention (β = .22); the model explains 43% of variance`
`description`	`In SEM, all three self-efficacy dimensions are positively associated with AI use, with Excellence strongest (β = .47), followed by Communication (β = .31) and Attention (β = .22); the model explains 43% of variance`
`status`	`review`
`evidence_strength`	`moderate`
`newest_evidence`	`2026`
`generated`	by `process:wiki-ingest` on `2026-10-11`
`trust`	unverified — no one has confirmed this page's content
`sources`	2 — `suriá-martínez-r-2026`, `suriá-martínez-r-2026-2`

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