TY  - GEN
TI  - Detecting Generative Artificial Intelligence Essays using Large Language Models: Machine and Deep Learning Approaches
KW  - Machine learning
KW  -  Deep learning
KW  -  Long ShortTerm Memory
KW  -  Support Vector Machine
KW  -  Educational Innovation
KW  - 
Generative artificial intelligence
KW  -  Higher education
SP  - 1
UR  - http://dx.doi.org/10.1109/icect61618.2024.10581394
Y1  - 2024/07/08/
PB  - IEEE
A1  - Tariq, Rasikh
A1  - Casillas-Muñoz, F
A1  - Ashraf, Waqar Muhammad
A1  - Ramírez-Montoya, Maria Soledad
CY  - Islamabad, Pakistan
N2  - The study focuses on discerning between human and AI-generated essays, highlighting the ethical implications of AI in academia. It employs various algorithms like logistic regression, Support Vector Machine (SVM), decision trees, random forests, KNN, and LSTM to develop models for essay classification. The TF-IDF technique (Term Frequency-Inverse Document Frequency) is applied to assess document word importance, with rigorous parameter tuning ensuring model accuracy. Findings revealed SVM's exceptional precision and recall, highlighting its robustness in accurately classifying essays, while decision trees offer simplicity but increased misclassification risk. KNN strikes a balance and random forests as well. LSTM excels in contextual understanding, albeit with higher computational demands. The research emphasizes the significance of algorithm selection in maintaining academic integrity and fostering genuine student creativity. SVM emerges as a robust and accurate choice for essay classification, ensuring fair assessment and upholding academic honesty.
EP  - 6
AV  - public
ID  - discovery10197150
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
ER  -