In this episode of Ed-Technical, Libby and Owen explore why traditional AI detection tools are struggling in academic settings. As students adopt increasingly sophisticated methods to evade AI detection - like paraphrasing tools, hybrid writing, and sequential model use - detection accuracy drops and false positives rise. Libby and Owen look at the research showing why reliable detection with automated tools is so difficult, including why watermarking and statistical analysis often fail in real-world conditions.
The conversation shifts toward process-based and live assessments, such as keystroke tracking and oral exams, which offer more dependable ways to evaluate student work. They also discuss the institutional challenges that prevent widespread adoption of these methods, like resource constraints and student resistance. Ultimately, they ask how the conversation about detection could lead towards more meaningful assessment.
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Credits: Sarah Myles for production support; Josie Hills for graphic design; Anabel Altenburg for content production.
