AI chatbots: A disguised enemy for academic integrity?

The widespread popularity of ChatGPT and other AI chatbots has sparked debate within the scientific community, particularly regarding their impact on academic integrity among students. While several studies have examined AI's role in education, a significant gap remains concerning how AI chatbot usage affects students’ perceptions of academic integrity. This study aims to address this gap through rigorous quantitative techniques to explore the dynamics of student interactions with AI chatbots and assess whether this engagement diminishes academic integrity in higher education. Using a non-experimental design, the research investigates the causal relationship between AI chatbot usage and academic integrity, focusing on eight latent variables identified in the literature. A stratified sampling technique was employed to collect a representative sample of 594 participants via a 5-point Likert scale survey from four Southern Asian countries. The dataset underwent extensive statistical analysis using Structural Equation Modeling (SEM) techniques. The findings establish significant links between motivations for using AI chatbots and a decline in academic integrity. The study identifies a behavioral link between academic integrity and pedagogical limitations, highlighting traditional classroom-based pedagogy as the most impactful factor influencing students’ motivation to engage with AI chatbots. This research not only quantitatively addresses ethical concerns related to AI in academia but also offers insights into user behavior by assigning distinct weights to post-usage behavioral factors, differentiating it from earlier studies that treated these factors equally.

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Publication Details

Journal
International Journal of Educational Research Open
Published
2024-10-31
DOI
https://doi.org/10.1016/j.ijedro.2024.100396
Citations
14
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
1.68
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article

AI chatbots: A disguised enemy for academic integrity?

Shafiqul Hoque, Bellal Hossain, Sayma Kabir, Ahnaf Chowdhury Niloy et al.
14 citations
International Journal of Educational Research Open
Artificial Intelligence in Healthcare and Education
1.68
article

AI chatbots: A disguised enemy for academic integrity?

Shafiqul Hoque, Bellal Hossain, Sayma Kabir, Ahnaf Chowdhury Niloy, Fabiha Bushra, Md. Khairul Islam, Md. Azizur Rahman, Samiya Islam, Naznin Sultana, Reaz Hafiz, Farhana Gulmeher
article en
14 citations

Abstract

The widespread popularity of ChatGPT and other AI chatbots has sparked debate within the scientific community, particularly regarding their impact on academic integrity among students. While several studies have examined AI's role in education, a significant gap remains concerning how AI chatbot usage affects students’ perceptions of academic integrity. This study aims to address this gap through rigorous quantitative techniques to explore the dynamics of student interactions with AI chatbots and assess whether this engagement diminishes academic integrity in higher education. Using a non-experimental design, the research investigates the causal relationship between AI chatbot usage and academic integrity, focusing on eight latent variables identified in the literature. A stratified sampling technique was employed to collect a representative sample of 594 participants via a 5-point Likert scale survey from four Southern Asian countries. The dataset underwent extensive statistical analysis using Structural Equation Modeling (SEM) techniques. The findings establish significant links between motivations for using AI chatbots and a decline in academic integrity. The study identifies a behavioral link between academic integrity and pedagogical limitations, highlighting traditional classroom-based pedagogy as the most impactful factor influencing students’ motivation to engage with AI chatbots. This research not only quantitatively addresses ethical concerns related to AI in academia but also offers insights into user behavior by assigning distinct weights to post-usage behavioral factors, differentiating it from earlier studies that treated these factors equally.

International Journal of Educational Research OpenVol. 7
East West University (BD)
Reduced inequalities
Openalex Percentile: Top 15%
Artificial Intelligence in Healthcare and Education
1.68
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