AI-Assisted Grading: A Double-Edged Sword for Equity in Academia

Let’s imagine a scenario: A university professor finds themselves overwhelmed, teaching four courses and managing 200 students in a single semester. Struggling to keep up with the workload, they turn to AI for assistance. The professor begins using an AI system to grade most assignments, significantly reducing their workload.

This hypothetical situation presents both opportunities and challenges:

On one hand, it offers relief to an overworked educator, potentially allowing more time for lesson planning, student interaction, and professional development. On the other hand, it raises serious concerns about the fairness and accuracy of grading when an algorithm, rather than a human, is making decisions that impact students’ academic records.

This scenario prompts a critical question: If an algorithm decides a student’s grade, who ensures fairness?

The Efficiency Argument

When compared to all the other responsibilities a professor has, grading is often considered one of the more tedious tasks. Ask any professor buried under a pile of assignments, and they’ll probably tell you they’d (or their TAs would) welcome a little AI assistance. If done well, AI can analyze structure, coherence, grammar, and even argument strength rapidly, reducing the turnaround time for feedback and freeing educators to focus on more meaningful interactions with students.

We might also argue that AI has the potential to smooth out human inconsistencies that can be a result of fatigue-driven errors, grading burnout, shifts in judgment based on the time of day or mood, and accidental comparisons.

Yet, this seemingly “too good to be true” academic future comes with hidden risks, requiring us to address the ethical and legal challenges. One major concern being algorithmic bias.

The Bias Beneath the Algorithm

AI doesn’t operate in a vacuum; it learns from existing data. And spoiler alert, academia has a long history of biases baked into grading practices. If an AI is trained on a limited or biased dataset, it might reinforce existing inequities disadvantaging certain groups of students.

For example, an AI trained primarily on Western academic writing may struggle to fairly evaluate essays that incorporate storytelling, personal narratives, or rhetorical styles more common in non-Western traditions. This bias is further evident in AI detectors, which frequently misclassify essays written by non-native speakers as discovered by a study published in Patterns, placing these students at an unfair disadvantage before their work is even evaluated.

Finding a Balance

So, do we abandon AI-assisted grading altogether? Not necessarily. The key is in responsible implementation. Here’s what needs to happen:

1. Human-AI Grading Partnership: AI can provide an initial objective assessment, flagging areas for further review. Professors should have the final say, using AI suggestions as a starting point rather than a verdict.
2. Student Involvement in AI Policies: Students should be told if AI is being used to grade their work. If students don’t know what the AI is looking for, how can they improve? Institutions should create clear AI grading policies with student input, ensuring fairness and accountability.
3. Diverse Training Data: AI models should be trained on a wide range of writing samples from students across socioeconomic and linguistic backgrounds to prevent biases that disproportionately impact certain groups.
4. Regular Audits for Bias: AI systems should be continuously evaluated to ensure they are not unfairly disadvantaging certain student groups. Just as a professor’s grading style can be reassessed, so should an AI’s.

The Future of Fair Grading

Ultimately, AI in grading should be a tool, not a replacement for human judgment. Faculty must understand AI’s limitations, maintain meaningful conversation with students and leverage AI to enhance rather than dictate the grading process. As my colleague puts it, “AI helps, but it doesn’t replace the important and sometimes hard conversations I have with students about their work.” That’s the balance we need.

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