The proliferation of AI has resulted in a demand for tools to detect and identify AI-generated content. However, it’s important that we evaluate these tools to better understand their impact on students and the ethical concerns that surround their use. To learn more about AI detection tools, take a look at a CSU Dominguez HIlls research guide on AI Detectors.
Please note that this list of AI detection tools is not exhaustive. Also, these detection tools are not tools that we recommend, but simply tools that are available.
They should not be used as the sole means of detecting AI use, but instead as one method among other methods recommended by the research below.
Quinn, Brian, and Erin Burns. "Artificial Intelligence Tools for Detection, Research and Writing." Texas Tech University Libraries, 19 Aug. 2024, guides.library.ttu.edu/artificialintelligencetools/detection. Accessed 21 Aug. 2024.
Again, consider the sample used to train an AI detector. If the sample is over- or under-representative of certain styles of writing, such as writing by second-language learners or writers with strong regional dialects, the detector may overly classify those writers as AI.
When uploading student work to AI detectors, you can't always be certain how their work is going to be stored or used by the detector. Be aware that uploading student assignments with personally identifiable information may be a violation of FERPA.
Consider why you're using an AI detector. Are you only checking papers that you already suspect of using AI-generated content? How do your personal and implicit biases play into decisions around the use and application of AI detectors? Rather, consider designing your assignments to be resistant to AI or incorporate explicit uses of AI to encourage responsible use.
Here are just a few articles to get you started if you want to explore literature on AI detection tools.