資料來源與延伸閱讀 Kobak, D., González-Márquez, R., Horvát, E.-Á., & Lause, J. (2025). Delving into LLM-assisted writing in biomedical publications through excess vocabulary. Science Advances, 11(27), eadt3813.https://doi.org/10.1126/sciadv.adt3813Kirchner, J. H., Ahmad, L., Aaronson, S., & Leike, J. (2023, January 31). New AI classifier for indicating AI-written text. OpenAI.https://openai.com/index/new-ai-classifier-for-indicating-ai-written-text/Zu, J., Fauss, M., & Li, C. (2025). Effects of generation model on detecting AI-generated essays in a writing test. In J. Wilson, C. Ormerod, & M. Beiting Parrish (Eds.), Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Coordinated session papers (pp. 92–98). National Council on Measurement in Education.https://aclanthology.org/2025.aimecon-sessions.11/Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7), 100779.https://doi.org/10.1016/j.patter.2023.100779Al Ali, A., Helcl, J., & Libovický, J. (2026). Different time, different language: Revisiting the bias against non-native speakers in GPT detectors. In S. Baez Santamaria, S. A. Somayajula, & A. Yamaguchi (Eds.), Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics: Volume 4, Student Research Workshop (pp. 277–291). Association for Computational Linguistics.https://doi.org/10.18653/v1/2026.eacl-srw.20