ChatGPT-Assisted Learning Effectiveness and Academic Achievement: A Mechanism-Based Model in Higher Education

This study examines the impact of ChatGPT-assisted learning on the academic achievement of hospitality and tourism students in Egyptian public universities, with particular emphasis on the mediating roles of perceived usefulness and self-regulated learning. Drawing conceptually on the Technology Acceptance Model (TAM), the study adopts a contextualized framework that emphasizes perceived usefulness while incorporating ChatGPT-assisted learning effectiveness as a learning-oriented driver within generative AI-supported educational environments. A quantitative research design was employed using an online survey administered to students who actively used ChatGPT for academic purposes. A total of 689 valid responses were collected from nine public universities and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed hypotheses. The findings indicate that ChatGPT-Assisted Learning Effectiveness (CALE) has a statistically significant and positive direct effect on academic achievement (AA; β = 0.386, T = 3.946, p < 0.001, 95% CI = 0.192–0.561) and strongly predicts perceived usefulness (β = 0.673, T = 9.274, p < 0.001, 95% CI = 0.581–0.742) and self-regulated learning (β = 0.707, T = 10.734, p < 0.001, 95% CI = 0.621–0.779). In turn, PU (β = 0.281, T = 3.854, p < 0.001, 95% CI = 0.142–0.417) and SRL (β = 0.220, T = 2.418, p = 0.016, 95% CI = 0.041–0.356) significantly enhance academic achievement. Mediation analyses further confirm that PU (β = 0.189, T = 2.366, p = 0.018, 95% CI = 0.031–0.284) and SRL (β = 0.156, T = 3.699, p < 0.001, 95% CI = 0.102–0.301) partially mediate the relationship between CALE and academic achievement. These findings offer important theoretical insights by contextualizing TAM’s performance-related logic within generative AI-driven learning environments and refining its application to academic outcome settings, while highlighting self-regulated learning as a critical explanatory mechanism. From a practical perspective, the study provides valuable implications for educators and policymakers by emphasizing the need to promote students’ perceived usefulness of ChatGPT and foster learner autonomy, positioning generative AI as a powerful pedagogical support tool for enhancing academic success in hospitality and tourism education.

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Published
2026-03-21
DOI
https://doi.org/10.3390/info17030303
Primary Topic
Artificial Intelligence in Healthcare and Education
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article

ChatGPT-Assisted Learning Effectiveness and Academic Achievement: A Mechanism-Based Model in Higher Education

Bassam Samir Al-Romeedy, Ahmed Mohamed Hasanein
Information
Artificial Intelligence in Healthcare and Education
article

ChatGPT-Assisted Learning Effectiveness and Academic Achievement: A Mechanism-Based Model in Higher Education

Bassam Samir Al-Romeedy, Ahmed Mohamed Hasanein
article en

Abstract

This study examines the impact of ChatGPT-assisted learning on the academic achievement of hospitality and tourism students in Egyptian public universities, with particular emphasis on the mediating roles of perceived usefulness and self-regulated learning. Drawing conceptually on the Technology Acceptance Model (TAM), the study adopts a contextualized framework that emphasizes perceived usefulness while incorporating ChatGPT-assisted learning effectiveness as a learning-oriented driver within generative AI-supported educational environments. A quantitative research design was employed using an online survey administered to students who actively used ChatGPT for academic purposes. A total of 689 valid responses were collected from nine public universities and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed hypotheses. The findings indicate that ChatGPT-Assisted Learning Effectiveness (CALE) has a statistically significant and positive direct effect on academic achievement (AA; β = 0.386, T = 3.946, p < 0.001, 95% CI = 0.192–0.561) and strongly predicts perceived usefulness (β = 0.673, T = 9.274, p < 0.001, 95% CI = 0.581–0.742) and self-regulated learning (β = 0.707, T = 10.734, p < 0.001, 95% CI = 0.621–0.779). In turn, PU (β = 0.281, T = 3.854, p < 0.001, 95% CI = 0.142–0.417) and SRL (β = 0.220, T = 2.418, p = 0.016, 95% CI = 0.041–0.356) significantly enhance academic achievement. Mediation analyses further confirm that PU (β = 0.189, T = 2.366, p = 0.018, 95% CI = 0.031–0.284) and SRL (β = 0.156, T = 3.699, p < 0.001, 95% CI = 0.102–0.301) partially mediate the relationship between CALE and academic achievement. These findings offer important theoretical insights by contextualizing TAM’s performance-related logic within generative AI-driven learning environments and refining its application to academic outcome settings, while highlighting self-regulated learning as a critical explanatory mechanism. From a practical perspective, the study provides valuable implications for educators and policymakers by emphasizing the need to promote students’ perceived usefulness of ChatGPT and foster learner autonomy, positioning generative AI as a powerful pedagogical support tool for enhancing academic success in hospitality and tourism education.

InformationVol. 17(3)
University of Sadat City (EG), King Faisal University (SA)
Decent work and economic growth
Openalex Percentile: Top 14%
Artificial Intelligence in Healthcare and Education
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