Szczegóły publikacji
Opis bibliograficzny
Applying deep learning as an academic management tool to detect ChatGPT plagiarized content similarities / Pascal Muam MAH, Tomasz PEŁECH-PILICHOWSKI, Iwona SKALNA // W: EdMedia + Innovate Learning [Dokument elektroniczny] : World Conference on Educational Media & Technology : May 19–23, 2025, Barcelona, Spain. — Wersja do Windows. — Dane tekstowe. — Waynesville : Association for the Advancement of Computing in Education (AACE), [2025]. — e-ISBN: 978-1-939797-83-4. — S. 678–689. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://www.learntechlib.org/primary/p/226204/ [2025-07-29]. — Bibliogr. s. 686–688, Abstr. — Dostęp po zalogowaniu
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 161462 |
|---|---|
| Data dodania do BaDAP | 2025-08-08 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Konferencja | World Conference on Educational Multimedia, Hypermedia and Telecommunications 2025 |
Abstract
This study investigates the use of deep learning techniques as an academic management tool to detect content similarities in class projects and scientific papers. Utilizing cosine similarity, the research identifies patterns in content, including material generated by ChatGPT 4o. The study also incorporates Abrahamson's Diffusion of Innovation Theory (ADIT) to assess the adoption levels of Chatbots and ChatGPT in natural language processing (NLP) and artificial intelligence (AI) as tools for academic oversight. A key focus is on the ability of Chatbots and ChatGPT to act as both creators and detectors of plagiarized content. The research explores how deep learning can classify innovative knowledge, simulate content similarities, and assess research authenticity—critical for managing academic submissions. Findings show that content analyzed using cosine similarity exhibits orthogonal vector correlations, implying independent similarities rather than direct copying. The study concludes that Chatbots and ChatGPT are in the early and late majority stages of technological adoption. It highlights their dual role in both generating and identifying potentially plagiarized content, emphasizing the importance of using these tools to preserve academic integrity. By recognizing the growing prevalence of AI-generated plagiarism, the study calls for stricter detection methods and supports responsible AI usage in academic environments.
