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Modeling learning engagement in AI-supported education: a crossover enabler-inhibitor framework.
Saleh Alwahaishi1,2, Waqas Ahmed2
1Department of Information Systems and Operations Management, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.
Frontiers in Psychology
|June 26, 2026
Summary
Learning motivation, not just reduced fatigue, is key for sustained engagement in AI education. Designing AI systems to support motivation is crucial for effective learning.
Area of Science:
- Educational Technology
- Artificial Intelligence
- Psychology
Background:
- Artificial intelligence (AI) is transforming education, impacting learner interaction, feedback, and motivation.
- Psychological factors influencing engagement in AI-powered learning environments require further investigation.
Purpose of the Study:
- To develop and test a crossover model explaining how enabling beliefs and emotional strain influence learning engagement in AI environments.
- To examine the roles of performance expectancy, technology self-efficacy, feedback overload, and AI learning anxiety as predictors of engagement, mediated by learning motivation and AI fatigue.
Main Methods:
- Structural equation modeling was used to analyze data from 251 learners using AI-driven educational tools.
- The study employed a crossover model integrating self-determination, cognitive load, and social cognitive theories.
Main Results:
- Learning motivation emerged as the strongest predictor of behavioral engagement.
- Performance expectancy positively influenced motivation, whereas feedback overload negatively impacted it.
- Technology self-efficacy did not significantly affect motivation or fatigue; fatigue did not directly reduce engagement.
Conclusions:
- Sustained learning engagement in AI environments is primarily driven by motivation, not solely by the absence of depletion.
- Inhibitory factors should be viewed as threats to motivation. AI system design should prioritize preserving psychological energy, minimizing cognitive load, and reinforcing learning purpose.
- Effective AI systems require careful engineering of the emotional architecture, including motivation-aware features and adaptive controls, to sustain learner engagement.
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