Module 4 Readings and links
Bloxham, S., den-Outer, B., Hudson, J., & Price, M. (2015). Let’s stop the pretence of consistent marking: exploring the multiple limitations of assessment criteria. Assessment & Evaluation in Higher Education, 1–16. https://doi.org/10.1080/02602938.2015.1024607 Links to an external site.
Edwards, B. (2023). Why AI detectors think the US Constitution was written by AI. Arstechnica. https://arstechnica.com/information-technology/2023/07/why-ai-detectors-think-the-us-constitution-was-written-by-ai/ Links to an external site.
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/https://doi.org/10.1016/j.caeai.2024.100209 Links to an external site.
Forsyth, R. (2022). Confident assessment in higher education. Sage.
Liang, W., Izzo, Z., Zhang, Y., Lepp, H., Cao, H., Zhao, X., Chen, L., Ye, H., Liu, S., & Huang, Z. (2024). Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews. arXiv preprint arXiv:2403.07183.
Liu, J. Q. J., Hui, K. T. K., Al Zoubi, F., Zhou, Z. Z. X., Samartzis, D., Yu, C. C. H., Chang, J. R., & Wong, A. Y. L. (2024). The great detectives: humans versus AI detectors in catching large language model-generated medical writing. International Journal for Educational Integrity, 20(1), 8. https://doi.org/10.1007/s40979-024-00155-6 Links to an external site.
Perkins, M., Furze, L., Roe, J., & MacVaugh, J. (2024). The Artificial Intelligence Assessment Scale (AIAS): A framework for ethical integration of generative AI in educational assessment. Journal of University Teaching and Learning Practice, 21(06). Updated version at https://leonfurze.com/2024/08/28/updating-the-ai-assessment-scale/ Links to an external site.
Perkins, M., Roe, J., Vu, B. H., Postma, D., Hickerson, D., McGaughran, J., & Khuat, H. Q. (2024). GenAI Detection Tools, Adversarial Techniques and Implications for Inclusivity in Higher Education. arXiv preprint arXiv:2403.19148. https://arxiv.org/abs/2403.19148?s=03
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. https://doi.org/10.1007/s40979-023-00146-z