In this episode, we explore the fascinating world of artificial intelligence (AI) and its impact on academic writing. Our guest, Sandie Elsom, Lecturer in Technology Education and AI enthusiast, discusses the challenges of distinguishing AI-generated text from human writing and explores the implications for academic integrity. We'll uncover the linguistic features that often differentiate AI-generated text and examine the effectiveness of current detection tools. Join us as we navigate the complexities of AI in education and discuss strategies for encouraging academic honesty in the age of AI.

Berber Sardinha, T. (2024). AI-generated vs human-authored texts: A multidimensional comparison. Applied Corpus Linguistics, 4(1), 100083-. https://doi.org/10.1016/j.acorp.2023.100083

Liang, W., Izzo, Z., Zhang, Y., Lepp, H., Cao, H., Zhao, X., Chen, L., Ye, H., Liu, S., Huang, Z., McFarland, D. A., & Zou, J. Y. (2024). Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews. arXiv.Org. https://doi.org/10.48550/arxiv.2403.07183

Muñoz-Ortiz, A., Gómez-Rodríguez, C., & Vilares, D. (2023). Contrasting Linguistic Patterns in Human and LLM-Generated Text. arXiv.Org. https://doi.org/10.48550/arxiv.2308.09067

Turnitin AI Technical Staff. (2023). Turnitin’s AI writing detection model architecture and testing protocol. Turnitin. https://www.turnitin.com/

Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., Popoola, O., Šigut, P., & Waddington, L. (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19(1), 26–39. https://doi.org/10.1007/s40979-023-00146-z