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Background: The integration of generative artificial intelligence (AI) technologies, such as GPT-3, Wordtune, and Jenni, into academic settings has revolutionized content creation, raising significant questions about authorship and originality. While AI offers benefits in efficiency and productivity, it presents substantial challenges to academic integrity. This paper examines these challenges and the need for new frameworks and policies to ensure ethical AI use. Materials and Methods: Our study aims to develop frameworks promoting ethical AI use and safeguarding academic work authenticity. We employed project-based learning (PBL) methodology to enhance student engagement and academic performance. A total of 179 students participated. PBL involves students in real-world projects, fostering critical thinking and research skills. Turnitin was used to evaluate the similarity percentage of submitted research papers, with a maximum allowable similarity of 20%. Results: Results showed that PBL students had higher academic performance (82.5 vs. 66.5) and lower similarity percentages (4.5% vs. 13%) compared to traditional literature review assessments. The use of Turnitin effectively identified AI-generated content, although it struggled with more sophisticated texts. Conclusion: Our findings highlight the effectiveness of PBL in promoting originality and reducing AI-generated plagiarism. Integrating these methodologies, along with ethical AI education, can help institutions maintain academic integrity while leveraging AI benefits. Future research should refine these strategies to adapt to the evolving educational landscape.
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Stored only when both papers are in the corpus — 19 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.