ESP-4502

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2026-02-25

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Vol. 11 No. 2 (2026): Publishing

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Copyright (c) 2026 Zhen Yang, Yong Zhang*, Yusri Kamin, Yun Liu

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How to Cite

Zhen Yang, Yong Zhang, Yusri Kamin, & Yun Liu. (2026). AI-Supported Learning Environments Shape Learning Ability in Science and Vocational Education: The Role of Psychological Trust and Motivation. Environment and Social Psychology, 11(2), ESP-4502. https://doi.org/10.59429/esp.v11i2.4502

AI-Supported Learning Environments Shape Learning Ability in Science and Vocational Education: The Role of Psychological Trust and Motivation

Zhen Yang

Faculty of Educational Sciences and Technology, Universiti Teknologi Malaysia,81300,Johor Bahru,Malaysia, yangzhen@graduate.utm.my

Yong Zhang

Faculty of Economics and Management, Jiaying University, Meizhou 514015, Guangdong, China, yongzhang4935@jyu.edu.cn

Yusri Kamin

Faculty of Educational Sciences and Technology, Universiti Teknologi Malaysia,81300,Johor Bahru,Malaysia, p-yusri@utm.my

Yun Liu

Faculty of Economics and Management, Jiaying University, Meizhou 514015, Guangdong, China, Liuyun6618@126.com


DOI: https://doi.org/10.59429/esp.v11i2.4502


Keywords: AI-supported learning; Pedagogical Filial Trust; Cultural calibration; Gaokao pressure; Multi-level SEM; Collectivist education

Abstract

The educational environment in China is experiencing profound change due to the incorporation of artificial intelligence, which was triggered by Education Modernization 2035 agenda and the AI+ Education Action Plan. Despite high levels of AI acceptance among Chinese students, psychological trust levels are weak, and this presents a paradox of students having an algorithmic authority anxiety when AI prescriptions differ from the instruction of the instructor. In this work, the Cultural-Calibrated Dual-Pathway Integration Model (C-DPIM) is proposed to examine the influence of psychological trust and motivation on learning proficiency in the AI-assisted learning setting in the realms of Chinese science and vocational education. It was a nationally stratified longitudinal cohort study that followed 2,847 students in 28 institutions in 12 provinces in the academic year between September 2024 and June 2025. The model was assessed using multi-level structural equation modeling, latent growth curve analysis and policy simulations. The findings suggest that AI transparency is an important predictor of Pedagogical Filial Trust (PFT) (β = 0.35, p < .001), and AI personalization is a predictor of socio-instrumental motivation in vocational education (β = 0.26, p < .001). Trust exclusivity is found in science students, as 62% of variance is attributed to PFT pathways, and motivation primacy is observed in vocational students, as 68% variance is attributed to motivation pathways. The effect of the Gaokao Corrosion shows that trust in the science students in the high-pressure dropped by 53.5%. The collectivist orientation increases the collectivistic orientation increased by 1.5 times but at the same time, it increases the trust fragility by 14 times in case of errors made by AI in the public eye. Simulations of the policy imply that an institutional co-signature would improve the learning proficiency with a standard deviation of 0.55 in the vocational settings. This study provides culturally recalibrated models of streamlining AI application in collectivist education systems across the globe.

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