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A Double-Edged Algorithm Attitude: How Appreciation and Aversion Shape Students' AI Learning Anxiety in Higher
Zhaolin Lu1, Jiayuan Guo1, Tian Yuan1
1School of Design and Arts, Beijing Institute of Technology, No. 5 South Street, Zhongguancun, Haidian District, Beijing 100081, China.
Behavioral Sciences (Basel, Switzerland)
|June 26, 2026
Summary
Student anxiety using artificial intelligence (AI) in higher education is linked to both algorithm aversion and appreciation. Understanding these attitudes is key to creating supportive AI learning environments.
Area of Science:
- Educational Technology
- Human-Computer Interaction
- Psychology of Technology
Background:
- Artificial intelligence (AI) integration in higher education is increasing.
- Students often experience anxiety when learning to use AI tools.
- Understanding student attitudes towards AI is crucial for effective learning.
Purpose of the Study:
- To investigate how performance expectations, perceived explainability, and perceived ethical risks influence algorithm aversion and appreciation.
- To examine the impact of these algorithm attitudes on artificial intelligence learning anxiety.
- To provide insights for developing supportive AI learning environments in education.
Main Methods:
- A hybrid partial least squares structural equation modeling-artificial neural network (PLS-SEM-ANN) approach was utilized.
- Survey data from 409 university students were analyzed.
- The study modeled the relationships between performance expectations, explainability, ethical risks, algorithm attitudes, and AI learning anxiety.
Main Results:
- Both algorithm aversion and algorithm appreciation significantly increase AI learning anxiety, with aversion having a stronger effect.
- Perceived ethical risk strongly predicts algorithm aversion but not appreciation.
- Performance expectations and perceived explainability enhance algorithm appreciation and also have weaker positive effects on algorithm aversion.
Conclusions:
- In educational settings, higher performance value and explainability can increase student pressure and anxiety.
- Algorithm aversion is a stronger driver of AI learning anxiety than algorithm appreciation.
- Findings offer guidance for designing educational strategies to mitigate AI learning anxiety and foster positive attitudes.
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