Claims

Learner acceptance of AI simulations is generally high, with System Usability Scale scores of 61.6 to 78.56 and 72%-93% willingness to recommend

Claim · All claims
Evidence · 1 study · quant-synthesis r2 · q2
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 i? Across 8 studies, learners expressed generally high acceptance: SUS scores ranged from 61.6 to 78.56 and 72%-93% were willing to recommend AI simulations. → Jiang Hongzhan 2026

Evidence

Jiang Hongzhan 2026

Jiang Hongzhan, Wang Ziyan, Shen Wanting, Meng Meiqi, Yang Dan, Li Xuejing, Hao Yufang. (2026). AI-Powered Simulation for Nursing Education: Mixed Methods Systematic Review. Journal of Medical Internet Research. https://doi.org/10.2196/95167

q2 · i? · quant-synthesis · r2

Synthesis of acceptance evidence from 8 studies using standardized instruments (SUS in 3 studies, TAM in 2, implementation measures exceeding 4.6/7) found "System Usability Scale scores ranged from 61.6 to 78.56" and 72%-93% willingness to recommend.

"System Usability Scale scores ranged from 61.6 to 78.56, indicating moderate to good usability."

Discussion

Page metadata
Field Value
type claim
id ai-simulation-high-learner-acceptance
title Learner acceptance of AI simulations is generally high, with System Usability Scale scores of 61.6 to 78.56 and 72%-93% willingness to recommend
description Learner acceptance of AI simulations is generally high, with System Usability Scale scores of 61.6 to 78.56 and 72%-93% willingness to recommend
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 1 — jiang-hongzhan-2026

Frontmatter is the page's machine-readable record — see the Schema & Guide.