Digital transformation in higher scientific education: The roles of virtual simulation, AI-assisted learning, and blended teaching
- 1School of Management, Guilin University of Aerospace Technology, Guilin, 541004, China
- 2School of Management, Guilin University of Aerospace Technology, Guilin, 541004, China; Corresponding author.
- 3Shaoxing Institute of Technology, Shaoxing, 312000, China
Abstract
The accelerating digitisation of higher education has prompted urgent questions about which technology-mediated pedagogical interventions most effectively improve student learning outcomes in scientific disciplines. Drawing on a cross-institutional survey of 1500 undergraduate and postgraduate students enrolled in STEM and scientific programmes across five institution types, this study investigates the individual and joint effects of three canonical digital transformation mechanisms: virtual simulation laboratories (VSL), artificial intelligence-assisted learning (AIAAL), and blended teaching models (BTM). Employing a mixed-methods empirical framework that combines structural equation modelling (SEM), multi-level fixed-effects regression, and bootstrap mediation analysis, we estimate standardised direct effects of 0.264, 0.218, and 0.192 for VSL, AIAAL, and BTM on academic achievement, respectively. Mediation analysis reveals that self-regulated learning (SRL), collaborative knowledge construction, and cognitive engagement jointly channel 55.5% of the VSL effect and 54.7% of the AIAAL effect through indirect pathways, underscoring the primacy of process-level learning mechanisms. Moderation analysis demonstrates that institutional readiness amplifies all three technology effects by 42-68%, while disciplinary context generates the sharpest differential: VSL effects in STEM disciplines are 110% larger than in non-STEM contexts, whereas AIAAL effects remain comparably robust across disciplines. Robustness tests using instrumental variables, propensity score matching, and difference-in-differences quasi-experimental designs confirm that results are not artefacts of selection bias or omitted variable contamination. Policy scenario projections to 2030 suggest that full digital integration could reduce inter-institutional achievement gaps by 74% relative to business-as-usual. The findings contribute a theoretically grounded and empirically validated framework for evidence-based digital transformation in scientific higher education.
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BibTeX
@article{long2026digital,
title = {{Digital transformation in higher scientific education: The roles of virtual simulation, AI-assisted learning, and blended teaching}},
author = {RuiQuan Long and XueHui Zhang and Zhao li},
journal = {Journal of Radiation Research and Applied Sciences},
year = {2026},
month = oct,
doi = {10.1016/j.jrras.2026.102737},
url = {https://doi.org/10.1016/j.jrras.2026.102737},
note = {CC BY},
}
RIS
TY - JOUR
TI - Digital transformation in higher scientific education: The roles of virtual simulation, AI-assisted learning, and blended teaching
AU - RuiQuan Long
AU - XueHui Zhang
AU - Zhao li
PY - 2026
DA - 2026/10/30
JO - Journal of Radiation Research and Applied Sciences
DO - 10.1016/j.jrras.2026.102737
UR - https://doi.org/10.1016/j.jrras.2026.102737
AB - The accelerating digitisation of higher education has prompted urgent questions about which technology-mediated pedagogical interventions most effectively improve student learning outcomes in scientific disciplines. Drawing on a cross-institutional survey of 1500 undergraduate and postgraduate students enrolled in STEM and scientific programmes across five institution types, this study investigates the individual and joint effects of three canonical digital transformation mechanisms: virtual simulation laboratories (VSL), artificial intelligence-assisted learning (AIAAL), and blended teaching models (BTM). Employing a mixed-methods empirical framework that combines structural equation modelling (SEM), multi-level fixed-effects regression, and bootstrap mediation analysis, we estimate standardised direct effects of 0.264, 0.218, and 0.192 for VSL, AIAAL, and BTM on academic achievement, respectively. Mediation analysis reveals that self-regulated learning (SRL), collaborative knowledge construction, and cognitive engagement jointly channel 55.5% of the VSL effect and 54.7% of the AIAAL effect through indirect pathways, underscoring the primacy of process-level learning mechanisms. Moderation analysis demonstrates that institutional readiness amplifies all three technology effects by 42-68%, while disciplinary context generates the sharpest differential: VSL effects in STEM disciplines are 110% larger than in non-STEM contexts, whereas AIAAL effects remain comparably robust across disciplines. Robustness tests using instrumental variables, propensity score matching, and difference-in-differences quasi-experimental designs confirm that results are not artefacts of selection bias or omitted variable contamination. Policy scenario projections to 2030 suggest that full digital integration could reduce inter-institutional achievement gaps by 74% relative to business-as-usual. The findings contribute a theoretically grounded and empirically validated framework for evidence-based digital transformation in scientific higher education.
SN - 2314-7164
SN - 1687-8507
ER -