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Avoidance Learning and Learned Helplessness

Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Related Experiment Videos

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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
algorithm attitudeartificial intelligenceartificial neural networkhigher educationlearning anxietystructural equation modeling

Related Experiment Videos

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.