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Related Experiment Video

Updated: Jul 9, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Understanding secondary school students' intentions to learn artificial intelligence: a multigroup structural

Yuyu Gu1, Bingfu Xiong2, Ruxue Li2

  • 1Department of Education, Korea University, Seoul, Republic of Korea.

Frontiers in Psychology
|July 8, 2026
PubMed
Summary

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Student intention to learn artificial intelligence (AI) is driven by self-efficacy, attitudes toward AI, and perceived usefulness. Factors like grade level and location influence these motivations, guiding inclusive AI education strategies.

Area of Science:

  • Education
  • Artificial Intelligence
  • Psychology

Background:

  • Artificial intelligence (AI) is increasingly integrated into daily life, necessitating AI literacy as a core 21st-century skill.
  • While AI curricula are prioritized globally, research on student motivation for AI learning remains limited.
  • Understanding motivational factors is crucial for effective AI education implementation in secondary schools.

Purpose of the Study:

  • To investigate the motivational mechanisms influencing secondary students' intention to learn AI.
  • To extend the Theory of Planned Behavior by incorporating self-efficacy, attitude toward AI use, subjective norms, perceived usefulness, and AI literacy.
  • To examine the moderating roles of gender, grade level, school location, and extracurricular AI learning experience.

Main Methods:

Keywords:
AI learningK-12 educationintention to learnmultigroup analysistheory of planned behavior

Related Experiment Videos

Last Updated: Jul 9, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

  • Structural Equation Modeling (SEM) was employed to analyze data from 632 secondary school students in Zhejiang Province, China.
  • The study assessed the direct and indirect effects of psychological and contextual factors on AI learning intention.
  • Multigroup analysis was used to test the moderating effects of demographic and experiential variables.

Main Results:

  • Student intention to learn AI was significantly predicted by self-efficacy, attitudes toward AI use, and perceived usefulness.
  • Self-efficacy and perceived usefulness positively influenced attitudes toward AI use.
  • AI literacy and subjective norms indirectly impacted AI learning intention via self-efficacy and perceived usefulness, with no significant direct effect on attitudes.
  • Grade level, school location, and extracurricular AI experience moderated the relationships, while gender did not.

Conclusions:

  • Self-efficacy, attitude, and perceived usefulness are key drivers of AI learning intention among secondary students.
  • Educational interventions should focus on enhancing these factors to promote AI learning.
  • Tailoring AI learning programs based on grade level, school location, and prior experience is recommended for greater inclusivity and effectiveness.