Re-Evaluating Components of Classical Educational Theories in AI-Enhanced Learning: An Empirical Study on Student Engagement

The primary goal of this research was to empirically identify and validate the factors influencing student engagement in a learning environment where AI-based chat tools, such as ChatGPT or other large language models (LLMs), are intensively integrated into the curriculum and teaching–learning proce...

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Bibliographic Details
Published in:Education sciences 2024-09, Vol.14 (9), p.974
Main Authors: Bognár, László, Ágoston, György, Bacsa-Bán, Anetta, Fauszt, Tibor, Gubán, Gyula, Joós, Antal, Juhász, Levente Zsolt, Kocsó, Edina, Kovács, Endre, Maczó, Edit, Mihálovicsné Kollár, Anita Irén, Strauber, Györgyi
Format: Article
Language:English
Subjects:
Analysis
Behavior
Cognition & reasoning
Cognitive ability
confirmatory factor analysis
Discriminant analysis
Education
Educational Practices
Educational Theories
EFA
explanatory factor analysis
Goal setting
higher education
Influence
Learner Engagement
Learning
Learning Processes
Likert scale
Methods
Motivation
Quantitative psychology
Questionnaires
Ryan, R.M
Self-efficacy
structural equation modeling
Student participation
university students
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Summary:The primary goal of this research was to empirically identify and validate the factors influencing student engagement in a learning environment where AI-based chat tools, such as ChatGPT or other large language models (LLMs), are intensively integrated into the curriculum and teaching–learning process. Traditional educational theories provide a robust framework for understanding diverse dimensions of student engagement, but the integration of AI-based tools offers new personalized learning experiences, immediate feedback, and resource accessibility that necessitate a contemporary exploration of these foundational concepts. Exploratory Factor Analysis (EFA) was utilized to uncover the underlying factor structure within a large set of variables, and Confirmatory Factor Analysis (CFA) was employed to verify the factor structure identified by EFA. Four new factors have been identified: “Academic Self-Efficacy and Preparedness”, “Autonomy and Resource Utilization”, “Interest and Engagement”, and “Self-Regulation and Goal Setting.” Based on these factors, a new engagement measuring scale has been developed to comprehensively assess student engagement in AI-enhanced learning environments.
ISSN:2227-7102
2227-7102
DOI:10.3390/educsci14090974