However, as I’ve explored in previous posts on the capabilities and limitations of GenAI, I firmly believe that this technology is fundamentally unsuited for high-stakes student assessment. At its core, GenAI generates probabilistic outputs based on patterns in training data, lacking true understanding and the ability to make qualitative judgments. This leads to inconsistency and bias in grading, raising serious concerns about fairness and reliability.
The use of AI in grading also raises a host of ethical and equity issues. As I wrote in “Generative AI doesn’t ‘democratize creativity’“, the notion that AI levels the playing field is often an illusion. In reality, relying on AI for grading may exacerbate existing inequities and privilege certain groups of students over others.
In this post, I’ll go deeper into the reasons why I believe GenAI should not be used for grading, drawing on recent experiments and real-world examples. I’ll also explore the potential risks and unintended consequences of AI-powered assessment. By the end, I hope to convince you that, despite the temptation, GenAI is a dead-end when it comes to evaluating student work.
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For more widespread adoption of VR in education, the cost of VR headsets need to come down significantly.