Background
Grading or marking student work and providing timely and accurate feedback is a time-consuming and monotonous, yet essential task for their learning.
Human grading of student work is far from perfect. Research n the USA based on 30 million records has shown that teachers who mark student work alphabetically by last name award lower grades towards the final letters of the alphabet, likely due to exhaustion after hours of grading. Research in Italy among 40.000 students in Northern Italy showed that girls get systematically higher grades than boys, probably because they exhibit fewer behavior issues that disrupt classes or irritate teachers.
However, recent advancements in artificial intelligence (AI) have made it possible to use large language models (LLMs) for grading written work, potentially saving teachers time and improving the accuracy and consistency of feedback.
Prompt Design Suggestions
When using LLMs for grading written work, it's important to keep in mind that prompt design is different than programming and requires a balance between precision and flexibility. Here are some suggestions for using LLMs for grading written work:
