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Evaluating Generative AI Tool Adoption and Its Effects on Academic Performance |
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PP: 769-781 |
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doi:10.18576/amis/190404
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Author(s) |
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Evon Abu-Taieh,
Mohammed E. Daghbosheh,
Rami Almatarneh,
Suha Afaneh,
Ala’aldin Alrowwad,
Issam AlHadid,
Rami S. Alkhawaldeh,
Sufian Khwaldeh,
Hamed S. Albdour,
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Abstract |
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| There is an explosion of Generative AI tools used by students. The key topic is what factors influence the use of students’ Gen AI tools and how they affect academic achievement in higher education. This study explores the factors influencing students’ intention to use Generative AI tools and their impact on academic achievement. The research model was validated using survey data from 398 students in a bilingual higher education setting in Jordan. Structural Equation Modeling (SEM) analysis was performed using Amos 20 to test the research hypotheses. Further the authors test the study using 8 machine learning models (decision tree, SVM, Random Forest, Neural network, Linear Regression, kNN, Gradient boosting, and AdaBoost). The empirical results are offered. several key findings. First, the ease of use and compatibility both positively influence the attitude towards using Generative AI tools. Facilitating conditions positively influence perceived behavioral control. Attitude, subjective norms, and perceived behavioral control positively influence behavioral intention to use Gen AI tools. Finally, behavioral intention positively influences academic achievement.
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