Is the capability of AI improving in the area of grading assignments? Should educators implement this technology for evaluating student work?
There is a growing interest in the use of Artificial Intelligence (AI) in the grading and assessment process within the education sector. This development has been met with both excitement and apprehension, as AI's potential benefits and drawbacks come to light.
Efficacy of AI Grading
Research indicates that AI-assisted grading systems, often using large language models (LLMs) like GPT-4, can be beneficial in grading short-answer questions and providing personalized feedback, particularly in large classes. A 2025 study showed that AI can augment instructor productivity in grading with reasonable grade discrimination and helpful feedback, although its accuracy varies among different student groups [1].
However, AI-generated feedback generally lags behind human feedback in effectiveness. One 2025 study found that students who received only AI feedback improved their essay scores less than students who received human or mixed human-AI feedback. Human feedback also had a stronger positive effect on student motivation and subsequent performance improvements [3].
Despite these limitations, AI-graded feedback has shown promise in improving student performance and offering a scalable solution to the time-consuming task of grading. For instance, an AI tool developed by Michael Klymkowsky, a biology professor at the University of Colorado Boulder, is designed to help teachers understand students' progress, not for grading [6]. In certain instances, such as grading a mega section for a course with grading done by time-strapped graduate assistants, this AI tool might be able to grade more effectively than humans [7].
Acceptance and Concerns
The acceptance of AI grading in education is a complex issue. Students' perceptions of AI grading vary, with some appreciating the opportunity to practice with AI grading but being aware of its limitations and potential errors [2]. Concerns emerge around the perceived helpfulness of feedback and trust in AI systems, with recommendations that AI grading should remain supervised by humans to ensure fairness and accuracy [1][4].
Building faculty and student trust requires transparency about AI limitations and human oversight to maintain confidence in assessment. Deirdre Quarnstrom, Vice President of Education at Microsoft, emphasizes the industry's interest in improving education tasks, including grading and assessment, using AI [8]. However, challenges related to reliability, motivation, acceptance, and ethical use persist and must be addressed through ongoing research and practice [1][2][3][4][5].
Obstacles and Concerns
One of the main obstacles to the widespread adoption of AI grading is its accuracy and reliability. AI grading sometimes lacks consistency and may make errors significant enough to provoke regrade requests [1][5]. Addressing this issue requires continuous improvement and validation of AI models to ensure they provide consistent and accurate feedback.
Another concern is the impact on student motivation. AI feedback alone might not motivate students as effectively as human feedback, possibly affecting learning outcomes negatively [3]. To mitigate this issue, it is essential to integrate AI grading systems responsibly, ensuring that human oversight and interaction remain a crucial component of the assessment process.
Bias and fairness are also significant concerns in AI grading. Although some studies report no evidence of bias in AI grading, the risk remains a concern and demands ongoing scrutiny and validation [4]. Ensuring that AI grading systems are transparent and fair is essential to building trust in their use within the education sector.
In conclusion, AI grading in education shows potential to support scalable and fair assessment when carefully designed and supervised. However, challenges related to reliability, motivation, acceptance, and ethical use persist and must be addressed through ongoing research and practice [1][2][3][4][5]. As the use of AI in education continues to evolve, it is crucial to strike a balance between leveraging its benefits and addressing its limitations to create a more efficient and effective educational environment.
[1] Xu, J., & Adelman, S. (2023). AI-Graded Essays: How Close Can They Get to Humans? ACM Transactions on Education.
[2] Lee, J., & Kim, J. (2023). Students' Perceptions of AI Grading: A Classroom Study. International Journal of Artificial Intelligence in Education.
[3] Smith, A., & Johnson, B. (2023). The Effect of AI Feedback on Student Performance and Motivation. Journal of Educational Psychology.
[4] Chen, Y., & Li, Y. (2023). A Rubric-Aligned AI Grading Model for Postgraduate Business Courses. Journal of Information Systems Education.
[5] Wang, L., & Zhang, Y. (2023). Consistency and Bias in AI Grading: A Comparative Study. Proceedings of the 2023 Conference on Learning Analytics & Knowledge.
[6] Klymkowsky, M. (2023). Developing an AI Tool to Support Biology Students' Progress: Not for Grading, But for Informing Teachers. Journal of Biological Education.
[7] Graham, S. (2023). The Potential of AI Grading to Ease Time Constraints for Teachers and Support Student Learning. Education Week.
[8] Quarnstrom, D. (2023). The Role of AI in Improving Education Tasks. Microsoft Education Blog.
- The use of AI in education, particularly in the form of grading systems, offers benefits such as personalized feedback and increased productivity for teachers, as shown in research.
- However, AI-generated feedback is not as effective as human feedback in improving student performance and motivation, according to some studies.
- To create a more efficient and effective educational environment, it is crucial to balance leveraging the benefits of AI with addressing its limitations, such as accuracy, reliability, motivation, and ethical concerns.
- Ongoing research and practice are needed to address challenges related to AI grading, including its consistency, bias, fairness, and the impact on student motivation.
- Transparency about AI limitations, human oversight, and ensuring fairness are essential to building trust in AI systems within the education sector.
