Full classrooms, tight deadlines, ever-increasing assessment demands? You’re not alone in feeling the pressure, which is why some EdTech companies are doing their utmost to sell AI as a cure-all for automated assessments. Just have it scan student responses, and you’ll have instant, fair feedback. At least, that’s the promise.
The truth? It’s murkier. You can use AI scoring to get through your grading workload, but without the right processes in place, you can’t be 100 percent sure that it’s getting it right. In this article, we’ll walk you through the essential elements of a trustworthy AI assessment tool.
Key Takeaways
- In AI scoring, grades are assigned by artificial intelligence. This works best with multiple-choice tests.
- Because AI can guess or “hallucinate,” it isn’t a cure-all for assessment problems.
- To get the benefits of AI scoring while minimizing the risks, use human-in-the-loop processes and choose a system that can explain its answers.
AI Scoring: The Good, the Bad, and the Ugly
AI scoring is the use of machines to automate grading. AI is, of course, a misnomer. There is nothing “intelligent” about large language models (LLMs); rather than thinking and reasoning, they are simply good at manipulating text—as some of your students have no doubt discovered. Yet even though they can produce mediocre essays, they have some serious flaws.
When it comes to AI scoring, there are a few things you have to keep in mind about the way these machines are structured.
The good
The models marketed as AI are built by feeding large quantities of text, images, and video into computer models that pick up patterns. They then calculate the probability that one sequence will follow the next. After processing millions of books, they calculate that there’s a high probability that the word “merry” will be followed by the word “Christmas,” and so on.
Because they’re so good at pattern recognition, AI scoring tools can be quite helpful when it comes to spotting spelling and grammar errors or identifying incorrect multiple-choice answers. They can also spot non-sequiturs in student essays and identify poorly formatted citations. However, many higher-level assessment tasks remain out of reach.
The bad
The errors AI tools often make reflect the difference between calculating probabilities and true reasoning. For instance, just this morning, I asked ChatGPT to guide me through installing a crossbar in an already assembled IKEA chest. I wanted to avoid taking the chest apart just to install a single component. Luckily, ChatGPT told me it had access to the manual and made a brilliant suggestion—just insert the wooden pegs into the holes on the outside of the chest.
The problem, of course, is that there were no holes on the outside of the chest. ChatGPT simply assumed they were there because IKEA generally makes it easy to screw things in by putting pre-drilled holes all over the place.
In academics, students often guess their way to the right answer like this. But assessment professionals need to be able to confidently verify their responses. That means LLMs that look for patterns and probabilities rather than verifiable facts are too unreliable to score assessments entirely on their own.
The ugly
Assessment experts also have to worry about biased AI. For example, a trio of professors from Stanford and Dartmouth universities found in May 2025, both Republicans and Democrats think LLMs have a left-leaning slant. This study is one of many to confirm left-wing bias in popular LLMs.
Most popular LLMs were developed in California’s Bay Area, one of the most politically progressive regions in the world. Perhaps it’s not surprising that LLMs reflect the political bias of their makers.
However, if assessors outsource scoring entirely to LLMs that prioritize one worldview over another, they jeopardize the fairness of their assessment system.
Key Lessons for Assessment Leaders
To take advantage of AI’s strengths while minimizing the risks, you need an assessment system built with the capabilities and shortcomings of LLMs in mind. Here are some essential things to look for as you choose an AI scoring system for your school.
1. No black boxes
Since LLMs are based on patterns, their outputs aren’t fixed or absolutely predictable. While this is acceptable for everyday use, it’s a big problem for high-stakes assessment scoring. You need a transparent AI scoring system that can provide a rubric-style breakdown of which parts of a given answer led to a grade, and which led to deductions.
2. Collaboration is key
In your school system—whether you’re at a small private school or a large public school district—you’ll have assessors with varied expertise and experience. Ideally, you want a scoring system that matches assessor expertise to item type so the human scorer can understand the AI scoring recommendation.
3. Human in the loop
From #2, it follows that you’ll need a human in the loop of your assessment process. By using AI grading tools that augment human scoring by highlighting potential issues—but leave the ultimate authority to the assessment expert—you can ensure that you’re not guessing your way to student grades.
4. AI ethics training
Don’t just throw your assessors into the deep end—equip them with the AI ethics training they need to spot issues. It doesn’t take long to educate scorers about the likely shortcomings of their AI assistants, such as left-leaning bias. Create a rubric they can follow to second-guess AI outputs, and you’ll have taken a big step towards winning their trust.
5. Manual scoring
High-stakes assessments will likely contain sections that can be automatically scored, such as multiple-choice questions, as well as open-ended or subjective questions that typically need a human scorer. That means your assessment tool needs to enable a mix of automated and manual scoring so you can adopt an item-appropriate workflow.
6. Open standards
When assessment platforms follow open standards, they can be seamlessly integrated with other EdTech tools and customized to ensure compliance with new regulations. This makes open source AI scoring tools such as TAO resilient in the face of changing EdTech needs.
The Bottom Line
AI is here to stay—and so are budget shortfalls, time pressures, and high expectations. If you’re set on using AI to increase the efficiency of your grading workflow, just make sure you’re choosing a system that is transparent, fair, and resilient, without replacing human judgment. By using AI scoring as a tool, you can help your assessors move faster without blindly trusting a black box.
To learn more about AI in education, check out these helpful blogs:
See Trustworthy AI Scoring For Yourself
If you want to see what trust in AI scoring actually looks like instead of taking it on faith, a demo is the simplest way to test your assumptions. You’ll see how TAO pairs automated scoring with a human in the loop so teachers keep real authority over judgments that matter.
TAO’s platform doesn’t treat AI as a black box—it gives you room to review, adjust, and collaborate with the system rather than surrendering your instincts to it. A walkthrough will show you the workflows, checks, and shared controls of the TAO platform. Schedule a demo today.
