AI-powered Synchronous Classrooms Bridging the Urban-rural Education Gap: A Multimodal Learning Analytics Approach

Sheng Shang

doi:10.47297/wspiedWSP2516-250019.20261008

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AI-powered Synchronous Classrooms Bridging the Urban-rural Education Gap: A Multimodal Learning Analytics Approach

Volume 10, Issue 8

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AI-powered Synchronous Classrooms Bridging the Urban-rural Education Gap: A Multimodal Learning Analytics Approach
AI-powered Synchronous Classrooms Bridging the Urban-rural Education Gap: A Multimodal Learning Analytics Approach
Xi'an Innovation College of Yan'an University, Xi'an, Shaanxi, 710100, China
Vol. 10, Issue 8, Pages: 91-95(2026)
Published：30 August 2026
DOI：10.47297/wspiedWSP2516-250019.20261008
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Sheng Shang.AI-powered Synchronous Classrooms Bridging the Urban-rural Education Gap: A Multimodal Learning Analytics Approach[J].Journal of International Education and Development,2026,10(08):91-95.
DOI：

Sheng Shang.AI-powered Synchronous Classrooms Bridging the Urban-rural Education Gap: A Multimodal Learning Analytics Approach[J].Journal of International Education and Development,2026,10(08):91-95. DOI： 10.47297/wspiedWSP2516-250019.20261008.

摘要

Abstract

Urban-rural gaps in digital pedagogical readiness among preservice teachers pose persistent challenges to achieving Sustainable Development Goal 4 (SDG4). Although AI-powered instructional systems are increasingly introduced in teacher education to support real-time feedback

personalised learning

and data-informed pedagogical decision-making

empirical evidence regarding preservice teachers' acceptance of such systems and their actual pedagogical impact remains limited. This study examines preservice EFL teachers' perceptions of an AI-powered synchronous classroom (AISC) learning environment

the effects of the AISC-multimodal learning analytics (MMLA) model on teaching quality and student learning outcomes during microteaching practice

and the role of AI-generated multimodal analytics in supporting pedagogical decision-making and professional competence. A quantitative quasi-experimental design involved 100 preservice EFL teachers from four teacher education institutes

assigned to an intervention group (n = 50) that used the AISC-MMLA system during synchronous microteaching or a control group (n = 50) that engaged in conventional synchronous microteaching without AI-supported analytics. Data were collected using a validated AISC Perception Scale and AI-generated multimodal indicators-speech analytics

attention-related metrics

and interaction logs-and analysed with independent-samples t-tests and multiple regression. The intervention group reported significantly more positive perceptions of the AISC environment (M = 4.28

SD = 0.51)

demonstrated higher teaching quality (β = 0.42

p < .001)

and achieved greater student learning gains (β = 0.37

p < .001). AI-generated multimodal analytics provided actionable real-time feedback that enhanced preservice teachers' technology-mediated pedagogical competence. The AISC model

strengthened by MMLA-driven insights

offers a scalable and data-informed pathway to improving teaching quality and enhancing student learning outcomes in underserved contexts

helping reduce pedagogical gaps between urban and rural training environments and equipping preservice teachers with the digital competencies required for future-ready classrooms.

关键词

Keywords

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