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The Complexities of AI Grading Systems: A Discussion on Bias and Critical Thinking
A recent study has highlighted the challenges faced by artificial intelligence (AI) grading systems, which are increasingly being used to evaluate student performance. The study found that these systems often struggle to identify and reward critical thinking skills, instead prioritizing grammatical correctness and adherence to rules. This raises important questions about the nature of education and the role of technology in assessing student learning.
One of the key issues with AI grading systems is their tendency to be biased against certain types of students. In particular, black students have been found to perform poorly under these systems, despite demonstrating excellent critical thinking skills. This bias is often attributed to cultural differences, with some researchers suggesting that the algorithms used by these systems are less effective at identifying and rewarding culturally diverse forms of communication. On the other hand, Chinese students who may not excel in grammar or vocabulary have been found to perform well under these systems, possibly due to their ability to generate "word salads" that conform to the rules.
This raises important questions about the nature of intelligence and the types of skills that are valued by educators. Are we teaching our children to think critically, or simply to follow rules and use proper grammar? The answer is not clear-cut, but it is evident that traditional methods of grading student work are no longer sufficient in the digital age.
The problem with AI grading systems goes beyond issues of bias and cultural sensitivity. They also fail to account for the complex and nuanced nature of human thought. In particular, they often struggle to identify and reward critical thinking skills, instead prioritizing more superficial aspects of student performance. This can lead to a situation in which students are able to generate well-structured and grammatically correct essays that are entirely wrong, simply by following the rules.
This is not an ideal scenario for education, as it suggests that students may be able to fool AI systems into thinking they have learned something, even if they haven't. It also raises important questions about the role of human educators in this process. Are we relying too heavily on technology to assess student learning, rather than engaging with them directly and providing feedback? The answer is not clear, but it is evident that traditional methods of grading student work are no longer sufficient.
One possible solution to this problem is to go back to basics and reevaluate our approach to education. Rather than relying on AI systems to grade student work, perhaps we should focus on developing our own critical thinking skills. This could involve incorporating more hands-on and experiential learning into our curriculum, as well as encouraging students to think creatively and critically.
The problem is that this approach may not be feasible in the current educational landscape, where technology is increasingly playing a major role in assessment and evaluation. Many educators and policymakers are already struggling to keep up with the demands of digital education, and it's unclear whether we can simply "go back" to more traditional methods of teaching and learning.
Furthermore, there is also a social aspect to this issue as our current grading system which relies heavily on algorithms doesn't teach us how to communicate effectively. We are teaching kids that we is correct but you know what people talk about in real life. They don't say "we" they say "I".
Another issue with these systems is the way it makes teachers grade work which is extremely challenging and can be very discouraging for many educators who value student performance over algorithmic results.
The algorithm itself is not racist, but it stumbles across words it doesn’t know. It's like a word salad you're talking about so if we say this is the correct use of the word cram, uhland excellent plus ten points I just literally like that. So it's interesting now but to go back to grading by hand I don't think we could ever do that. I know it's just too much work. It's too much work and nobody wants to do it.
But, if we look at it in a different way, it's not about the student or the teacher. The algorithm is simply following the rules. We have created this system where we expect AI systems to be able to identify violence against animals but can't make moral judgments like humans do. So what would happen when they see that the robot was made of plastic and was just a toy, but it's being treated as if it was alive.
And also, how about nature videos? If a pack of hyenas is pulling down a water buffalo or wildebeest, would it get flagged because AI can't tell the difference between a robot and an animal. It's like having two cats fighting on a YouTube channel - would that get flagged as cruelty? We all love watching cat fights but if we use this algorithm to flag it, then what about other videos of animals in conflict.
The lack of distinction between these different types of content is telling. The AI system cannot differentiate between violence against animals and animal-related content. This highlights a broader problem - that our current approach to education relies too heavily on technology, and not enough on human judgment and critical thinking skills.
In the end, it's clear that traditional methods of grading student work are no longer sufficient in the digital age. We need to find new ways of evaluating student learning, ones that take into account the complexities of human thought and the nuances of cultural communication. By reevaluating our approach to education and incorporating more hands-on and experiential learning, we can create a more effective and equitable system for assessing student performance.
However, this may require a fundamental shift in how we think about education and technology. It's time to rethink the role of AI systems in our educational landscape and prioritize human judgment and critical thinking skills over algorithmic results.