Do AI Agents Moderate the Associations of Motivation and Learning-Strategy Effectiveness With Perceived Educational Outcomes? An Exploratory Structural Equation Modeling Study

Zhongqing Yang; Jiayue Lian; Ya Zhao

doi:10.47297/wspiedWSP2516-250048.20261006

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Do AI Agents Moderate the Associations of Motivation and Learning-Strategy Effectiveness With Perceived Educational Outcomes? An Exploratory Structural Equation Modeling Study

Volume 10, Issue 6

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Do AI Agents Moderate the Associations of Motivation and Learning-Strategy Effectiveness With Perceived Educational Outcomes? An Exploratory Structural Equation Modeling Study
Do AI Agents Moderate the Associations of Motivation and Learning-Strategy Effectiveness With Perceived Educational Outcomes? An Exploratory Structural Equation Modeling Study
Tourism College, Beijing Union University, Beijing, 100101, China
Vol. 10, Issue 6, Pages: 209-213(2026)
Online First：11 August 2026，

Published：30 June 2026
DOI：10.47297/wspiedWSP2516-250048.20261006
Accepted：

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Yang Zhongqing,Lian Jiayue,Zhao Ya.Do AI Agents Moderate the Associations of Motivation and Learning-Strategy Effectiveness With Perceived Educational Outcomes? An Exploratory Structural Equation Modeling Study[J].Journal of International Education and Development,2026,10(06):209-213.
DOI：

Yang Zhongqing,Lian Jiayue,Zhao Ya.Do AI Agents Moderate the Associations of Motivation and Learning-Strategy Effectiveness With Perceived Educational Outcomes? An Exploratory Structural Equation Modeling Study[J].Journal of International Education and Development,2026,10(06):209-213. DOI： 10.47297/wspiedWSP2516-250048.20261006.

摘要

Abstract

This study examined whether AI-agent use moderates the associations among learner motivation

learning-strategy effectiveness

intermediate learning processes

and perceived educational outcomes in higher education. Guided by self-determination theory

expectancy-value theory

and research on AI-supported self-regulated learning

an exploratory structural equation model was specified in which motivation and learning-strategy effectiveness were treated as antecedent constructs

cognitive engagement

emotional state

and learning behavior were treated as intermediate learning processes

and perceived educational outcomes were treated as the final outcome. Data were collected from 450 undergraduate students enrolled in AI-supported courses. The model tested direct associations from the antecedent constructs to the three intermediate processes

paths from the intermediate processes to perceived educational outcomes

and interaction terms representing AI-agent moderation. Learning-strategy effectiveness showed positive associations with the three intermediate processes

whereas motivation showed negative associations with those same processes in the present specification. The paths from the intermediate processes to perceived educational outcomes were not statistically significant

and the AI-agent interaction terms were likewise nonsignificant. Overall model fit was poor (CMIN/DF = 10.651

RMSEA = .125

CFI = .639

TLI = .621

NFI = .617)

so the model should be interpreted as exploratory rather than confirmatory. These findings suggest that AI tools should not be assumed to strengthen motivational or strategic learning pathways automatically. Instead

the educational value of AI support likely depends on how it is designed

integrated into instruction

and aligned with learners' regulatory needs.

关键词

Keywords

references

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