An Exploratory Analysis of ChatGPT and Gemini Responses on False Positives in AI-Generated Text Detection
DOI:
https://doi.org/10.58524/oler.v6i3.1284Keywords:
Artificial intelligence, AI detection systems, ChatGPT, False positives, GeminiAbstract
The increasing use of AI tools has raised concerns about the mislabeling of human-written texts as AI-generated (i.e., false positives), potentially leading to unfair academic decisions in education settings. This study aimed to explore AI-based explanations for this misclassification and develop strategies that L2 teachers can use to reduce them. Adopting a qualitative framework, a textual corpus was created from responses by ChatGPT and Gemini to two prompts about mislabeling texts as AI-generated. The corpus comprised 4 responses totaling 2024 words. It was thematically analyzed. The findings showed that, according to ChatGPT and Gemini, there are five overarching themes regarding possible reasons: linguistic monotony, conventional writing patterns, bias related to non-native English writers, overly polished writing, and limitations of AI detection systems. According to the findings, ChatGPT and Gemini recommended three strategies for L2 teachers to minimize false positives: trace evidence of authors’ writing processes, customize their writing assessments, and use verification techniques in decision-making. Overall, this study contributes to the literature by providing a structured synthesis of AI-generated explanations of false positives and identifying pedagogically relevant strategies for L2 writing assessment, while emphasizing the importance of critically interpreting AI detection results.
References
Alam, M. S., Asmawi, A., Haque, M. H., Patwary, M. N., Ullah, M. M., & Fatema, S. (2024). Distinguishing between student-authored and ChatGPT-generated texts: A preliminary exploration of human evaluation techniques. Iraqi Journal for Computer Science and Mathematics, 5(3), 287–304. https://doi.org/10.52866/ijcsm.2024.05.03.016
Alexander, K., Savvidou, C., & Alexander, C. (2023). Who wrote this essay? Detecting AI-generated writing in second language education in higher education. Teaching English with Technology, 23(2), 25–43. https://doi.org/10.56297/BUKA4060/XHLD5365
Alfarraj, Y. F., & Wardat, Y. (2024). Exploring the impact of ChatGPT on scientific research: Assessing strengths, weaknesses, opportunities, and threats. Education as Change, 28(1), 1–27. https://doi.org/10.25159/1947-9417/16006
Alkaissi, H., & McFarlane, S. I. (2023). Artificial hallucinations in ChatGPT: Implications in scientific writing. Cureus, 15(2), e35179. https://doi.org/10.7759/cureus.35179
AlSagri, H. S., Farhat, F., Sohail, S. S., & Saudagar, A. K. J. (2025). ChatGPT or Gemini: Who makes the better scientific writing assistant? Journal of Academic Ethics, 23(3), 1121–1135. https://doi.org/10.1007/s10805-024-09549-0
Barone, S. M., & Cargile, C. (2020). L1 and L2 writing differences: From understanding to practice. English Teaching Forum, 58(3), 10–19.
Barrot, J. S. (2023). Using ChatGPT for second language writing: Pitfalls and potentials. Assessing Writing, 57, 100745. https://doi.org/10.1016/j.asw.2023.100745
Bendo, M. C. D. (2026). False positives in AI writing detection: A small-scale empirical study using authentic Filipino student essays. ASEAN Journal of Open and Distance Learning, 18(1), 12–19. https://doi.org/10.64233/VYVI9613
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Cheng, A., Lin, Y., Reedy, G., Joseph, C., Wirkowski, S., Mallette, V., Nagesh, V., Krieser, D., & Calhoun, A. (2025). Ability of AI detection tools and humans to accurately identify different forms of AI-generated written content. Advances in Simulation, 10, Article 66. https://doi.org/10.1186/s41077-025-00396-6
Cotton, D. R., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228-239. https://doi.org/10.1080/14703297.2023.2190148
Dao, N. A. D., Nguyen, T. M., Tran, T. T., & Phan, A. (2025). Educational administrators versus teachers’ perspectives on the use of AI in language learning: A qualitative study. Computer-Assisted Language Learning Electronic Journal (CALL-EJ), 26(4), 418–442.
Derakhshan, A. (2025). Tracking the effects of Gemini as a GenAI tool on L2 learners’ writing proficiency and anxiety: Latent growth curve modeling approach. The Asia-Pacific Education Researcher. https://doi.org/10.1007/s40299-025-01042-5
Dincer, A. & Yeşilyurt, S. (2025). Dealing with bias and hallucinations in AI tools. In İ. Çakır & A. Bakla (Eds.), AI in scholarly research and publishing: Opportunities, challenges and dilemmas (pp. 75–95). Anı Publishing.
Fleckenstein, J., Meyer, J., Jansen, T., Keller, S. D., Köller, O., & Möller, J. (2024). Do teachers spot AI? Evaluating the detectability of AI-generated texts among student essays. Computers and Education: Artificial Intelligence, 6, 100209. https://doi.org/10.1016/j.caeai.2024.100209
Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2023). Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers. NPJ Digital Medicine, 6(1), 75. https://doi.org/10.1038/s41746-023-00819-6
Georgiou, G. P. (2026). Key features to distinguish between human- and AI-generated texts: Perspectives from university professors. AI in Education, 2(1), 2. https://doi.org/10.3390/aieduc2010002
Giray, L. (2024). The problem with false positives: AI detection unfairly accuses scholars of AI plagiarism. The Serials Librarian, 85(5-6), 181–189. https://doi.org/10.1080/0361526X.2024.2433256
Giray, L., Roe, J., & Diesta Espiritu, J. (2026). AI writing detectors are ineffective, unreliable, and harmful. English Teaching: Practice & Critique, 1–17. https://doi.org/10.1108/ETPC-07-2025-0155
Graham, S., & Sandmel, K. (2011). The process writing approach: A meta-analysis. The Journal of Educational Research, 104(6), 396–407. https://doi.org/10.1080/00220671.2010.488703
Grammarly. (2026, March 30). How do AI detectors work? Methods and limitations. Grammarly.
Griche, M. S., & Bennis, M. (2026). Generative artificial intelligence integration in foreign language education in higher education: A systematic literature review. Technology in Language Teaching & Learning, 8, 102827. https://doi.org/10.29140/tltl.2026.102827
Gritsai, G., Voznyuk, A., Grabovoy, A., & Chekhovich, Y. (2024). Are AI detectors good enough? A survey on quality of datasets with machine-generated texts.
Hadra, M., Cambridge, K., & Mesbah, M. (2026). Evaluating the accuracy and reliability of AI content detectors in academic contexts. International Journal for Educational Integrity, 22, Article 4. https://doi.org/10.1007/s40979-026-00213-1
Ibrahim, K. (2023). Using AI-based detectors to control AI-assisted plagiarism in ESL writing: “The Terminator Versus the Machines”. Language Testing in Asia, 13(1), 46. https://doi.org/10.1186/s40468-023-00260-2
Kurt, G., & Kurt, Y. (2024). Enhancing L2 writing skills: ChatGPT as an automated feedback tool. Journal of Information Technology Education, 23, Article 24. https://doi.org/10.28945/5370
Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7), Article 100779. https://doi.org/10.1016/j.patter.2023.100779
Liu, Y., & Wang, H. (2024). Who on Earth is using generative AI? (Policy Research Working Paper No. 10870). World Bank Group Digital Development Global Practice.
Liu, Y., Huang, J., & Wang, H. (2025). Who on Earth is using generative AI? Global trends and shifts in 2025 (Policy Research Working Paper No. 11231). World Bank Group Digital Transformation Vertical. https://doi.org/10.1596/1813-9450-11231
Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410
Makiev, K. G., Asimakidou, M., Vasios, I. S., Keskinis, A., Petkidis, G., Tilkeridis, K., … & Iliopoulos, E. (2023). A study on distinguishing ChatGPT-generated and human-written orthopaedic abstracts by reviewers: Decoding the discrepancies. Cureus, 15(11), e49166. https://doi.org/10.7759/cureus.49166
Ncube, P. D., Dzvapatsva, G. P., Matobobo, C., & Ranga, M. M. (2026). Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity. AI and Ethics, 6(1), 68. https://doi.org/10.1007/s43681-025-00871-w
Nguyen, L., & Barrot, J. S. (2024). Detecting and assessing AI-generated and human-produced texts: The case of second language writing teachers. Assessing Writing, 62, 100899. https://doi.org/10.1016/j.asw.2024.100899
Onozawa, C. (2010). A study of the process writing approach: A suggestion for an eclectic writing approach. 共愛学園前橋国際大学論集, 10, 153–163.
OpenAI. (2026, May 11). How ChatGPT adoption broadened in early 2026. OpenAI.
Perkins, M. (2023). Academic integrity considerations of AI large language models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching & Learning Practice, 20(2). https://doi.org/10.53761/1.20.02.07
Pramjeeth, S., & Ramgovind, P. (2025). Reconceptualizing assessment design in the age of AI: Is it genuine or faux? New Directions for Teaching and Learning, 2025(182), 29–40. https://doi.org/10.1002/tl.20625
Reid, E. (2026, May 19). A new era for AI search. Google.
Saha, S., & Feizi, S. (2025). Almost ai, almost human: The challenge of detecting ai-polished writing. In Findings of the Association for Computational Linguistics: ACL 2025 (pp. 25414–25431). https://doi.org/10.18653/v1/2025.findings-acl.1303
Stokel-Walker, C. (2023). ChatGPT listed as author on research papers: Many scientists disapprove. Nature, 613, 620–621. https://doi.org/10.1038/d41586-023-00107-z.
Tran, T. T. T. (2025). Enhancing EFL writing revision practices: The impact of AI-and teacher-generated feedback and their sequences. Education Sciences, 15(2), 232. https://doi.org/10.3390/educsci15020232
Van Vlasselaer, M., Van Droogenbroeck, F., & Spruyt, B. (2026). Who wrote this? Evaluating the reliability of AI detection tools in higher education. International Journal for Educational Integrity, 22, Article 16. https://doi.org/10.1007/s40979-026-00226-w
Waked, A. N., Abdelsalam, H., Ashraf, M. W., Pilotti, M., & El Alaoui, K. (2024). The impact of falsely detecting AI-generated text on academic assessment. In M. Shelley & O. T. Ozturk (Eds.), Proceedings of ICRES 2024 – International Conference on Research in Education and Science (pp. 573-586), Antalya, Türkiye. ISTES.
Wang, C., & Wang, Z. (2025). Investigating L2 writers’ critical AI literacy in AI-assisted writing: An APSE model. Journal of Second Language Writing, 67, Article 101187. https://doi.org/10.1016/j.jslw.2025.101187
Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., Popoola, O., ... & Waddington, L. (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19(1), 26. https://doi.org/10.1007/s40979-023-00146-z
Yan, D. (2024). Comparing individual vs. collaborative processing of ChatGPT-generated feedback: Effects on L2 writing task improvement and learning. Language Learning & Technology, 28(1), 1–19. https://doi.org/10.64152/10125/73597
Zainurrahman, Emilia, E., & Rodliyah, R. S. (2026). A genre-based approach with GenAI feedback to teaching written exposition in a tertiary EFL context in Indonesia. TESL-EJ, 30(1), 1–33. https://doi.org/10.55593/ej.30117a8
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