eprintid: 10197150 rev_number: 8 eprint_status: archive userid: 699 dir: disk0/10/19/71/50 datestamp: 2024-09-18 10:14:04 lastmod: 2024-09-18 10:18:04 status_changed: 2024-09-18 10:14:04 type: proceedings_section metadata_visibility: show sword_depositor: 699 creators_name: Tariq, Rasikh creators_name: Casillas-Muñoz, F creators_name: Ashraf, Waqar Muhammad creators_name: Ramírez-Montoya, Maria Soledad title: Detecting Generative Artificial Intelligence Essays using Large Language Models: Machine and Deep Learning Approaches ispublished: pub divisions: UCL divisions: B04 divisions: F43 keywords: Machine learning, Deep learning, Long ShortTerm Memory, Support Vector Machine, Educational Innovation, Generative artificial intelligence, Higher education note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. abstract: 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. date: 2024-07-08 date_type: published publisher: IEEE official_url: http://dx.doi.org/10.1109/icect61618.2024.10581394 oa_status: green full_text_type: other language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2298735 doi: 10.1109/ICECT61618.2024.10581394 isbn_13: 979-8-3503-4971-9 lyricists_name: Ashraf, Waqar lyricists_id: WMAAS21 actors_name: Ashraf, Waqar actors_id: WMAAS21 actors_role: owner full_text_status: public pres_type: paper publication: Proceedings - 2024 International Conference on Engineering and Computing, ICECT 2024 place_of_pub: Islamabad, Pakistan pagerange: 1-6 event_title: 2024 International Conference on Engineering & Computing Technologies (ICECT) event_dates: 23 May 2024 - 23 May 2024 book_title: 2024 International Conference on Engineering & Computing Technologies (ICECT) citation: Tariq, Rasikh; Casillas-Muñoz, F; Ashraf, Waqar Muhammad; Ramírez-Montoya, Maria Soledad; (2024) Detecting Generative Artificial Intelligence Essays using Large Language Models: Machine and Deep Learning Approaches. In: 2024 International Conference on Engineering & Computing Technologies (ICECT). (pp. pp. 1-6). IEEE: Islamabad, Pakistan. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10197150/1/9.%20IEEE%203rd%20International%20Conference%20on%20Engineering%20%26%20Computing%20Technologies%20%28ICECT%202024%29.pdf