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TRUVACE RECORD VERSION
record: TRV-2026-1950
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-19T22:03:46.634761Z
status: published
lens: g_space
sector: health
headline: Learning Adaptation in Artificial Intelligence-Enabled Nursing Education: A Network Analysis of Artificial Intelligence Literacy, Self-Regulated Learning and Academic Resilience
dek: Aims To examine the interconnections among artificial intelligence literacy, self-regulated learning strategies, and academic resilience in undergraduate nursing students, and to conduct an exploratory comparison of network characteristics between students with and without clinical practice experience. Design A cross-sectional survey. Methods Using convenience sampling, 1784 undergraduate nursing students were recruited from five universities in Hunan Province, China, between March and May 2026. Participants com…
gain_title: Learning Adaptation in Artificial Intelligence-Enabled Nursing Education: A Network Analysis of Artificial Intelligence Literacy, Self-Regulated Learning and Academic Resilience: Bootstrap procedures assessed network accuracy and stability, and the Network Comparison Test was used to conduct an exploratory comparison between students with and without clinical practice experience.
problem_title: (none)
trace_subject: (none)
gain_reading: Learning Adaptation in Artificial Intelligence-Enabled Nursing Education: A Network Analysis of Artificial Intelligence Literacy, Self-Regulated Learning and Academic Resilience: Bootstrap procedures assessed network accuracy and stability, and the Network Comparison Test was used to conduct an exploratory comparison between students with and without clinical practice experience.
gain_evidence: (none)
problem_reading: (none)
problem_evidence: (none)
quick_read: Aims To examine the interconnections among artificial intelligence literacy, self-regulated learning strategies, and academic resilience in undergraduate nursing students, and to conduct an exploratory comparison of network characteristics between students with and without clinical practice experience. Design A cross-sectional survey.

Results The 10-node network contained 35 non-zero edges and had a mean predictability of 62.6%. Artificial intelligence evaluation showed the highest node strength, followed jointly by monitoring learning strategies and cognitive maturity.
limitation: 
tag: Evidence-backed gain
key_points: Aims To examine the interconnections among artificial intelligence literacy, self-regulated learning strategies, and academic resilience in undergraduate nursing students, and to conduct an exploratory comparison of network characteristics between students with and without clinical practice experience. | Design A cross-sectional survey. | Methods Using convenience sampling, 1784 undergraduate nursing students were recruited from five universities in Hunan Province, China, between March and May 2026.
rundown: Aims To examine the interconnections among artificial intelligence literacy, self-regulated learning strategies, and academic resilience in undergraduate nursing students, and to conduct an exploratory comparison of network characteristics between students with and without clinical practice experience. Design A cross-sectional survey.

Methods Using convenience sampling, 1784 undergraduate nursing students were recruited from five universities in Hunan Province, China, between March and May 2026. Participants completed a demographic questionnaire and three validated scales.
sources:
- peer_reviewed | Journal of Advanced Nursing | https://doi.org/10.1111/jan.70784 | 2026-10-15
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