Grading is a common challenge for STEM teachers at secondary and higher education levels, especially when evaluating complex assignments designed to support student mastery of their subject. However, AI looks set to be of assistance.
Answer complexity, time-intensive manual grading, and the interpretative nature of many STEM assessments can strain teacher time, and risks impacting both grading equity and the quality of student feedback.
With often large cohorts of learners, how can STEM teachers ensure effective, equitable grading that supports individual student’s growth and development? AI could hold the answer.
As artificial intelligence advances, it offers educators an additional tool to develop more efficient and impactful grading practices, enhancing their ability to support student learning.
AI’s capabilities in visual recognition, language processing, and data analysis help STEM teachers evaluate complex assignments more efficiently and accurately, while preserving the critical judgment and oversight needed for fair assessment.
In this article, we explore how AI is transforming STEM grading, the specific functionality to be aware of, and ways to implement these technologies ethically to support student learning and fairness in assessment.
What are the challenges of grading complex problem types in STEM?
STEM teachers use comprehensive assessment methods to evaluate student knowledge, comprehension, and subject mastery – from multiple-choice exams to open-ended problem-solving assessments. While some STEM assessments can be graded quickly and efficiently with letter-based grading, others are considerably more nuanced.
Complex responses – like equations, graphs, coding, flowcharts, chemical notation, etc – take significant time and manual effort to interpret and evaluate thoroughly. This is further complicated by the fact that solutions in STEM may have multiple valid pathways, where even incorrect answers can demonstrate a solid grasp of the right process (subjective vs objective assessment).
Scherer and Tiemann (2012) highlight this challenge in their study on developing a computer-based assessment for complex problem-solving in chemistry. They note that due to the "high complexity of computer-based assessments, it might be difficult for test takers to find an optimal or correct solution". This complexity extends to the grading process as well, with the authors pointing out that "the amount of collected data could become problematic within the evaluation process".
Students need constructive feedback that recognizes their understanding of the topic despite arriving at the wrong conclusion. In these circumstances, binary ‘right/wrong’ grading fails to account for students’ progress and can discourage the sustained effort and student engagement needed to master challenging topics.
The challenges of manual grading are further highlighted in a recent study on grading of physics exams. The researchers note that grading complex STEM problems involves assessing multiple cognitive processes and abilities, including "information retrieval, problem representation, model building, strategic behavior, knowledge application, and the phases of scientific inquiry such as planning an experiment and evaluating the results". This complexity makes thorough evaluation time-intensive and prone to inconsistencies.
However, faced with large volumes of (often) handwritten assessments, STEM teachers can struggle to provide timely actionable feedback. As well as being excessively time-consuming, the manual grading process can also lead to inconsistencies and misinterpretations of student understanding. Fortunately, the advent of AI-assisted grading solutions offers a promising way forward.
How can AI-assisted grading efficiently evaluate complex STEM assignments?
Fortunately, the advent of AI-assisted grading solutions offers a promising way forward.
Research has shown that AI can achieve high accuracy in grading STEM assessments. For instance, a study on an AI-based automated grading system for diabetic retinopathy demonstrated overall accuracy of 0.965 and an AUC of 0.980 for referable cases. While this example is from medical imaging, it illustrates the potential of AI in complex STEM grading tasks.
AI-assisted grading technology has developed rapidly in recent years, offering everything from fully automated grading systems, to simple tools to streamline human grading processes.
- Automated grading systems use data- and natural language processing to grade multiple-choice and essay-style questions
- Visual recognition software interprets and assesses visual components like diagrams, flowcharts, and graphs, as well as solving and evaluating mathematical equations
- Customizable rubrics allow educators to create more nuanced evaluation criteria to assess student knowledge, such as process and creativity
- Learning analytics identify trends in student performance data to recommend tailored feedback and instruction to suit individual learners’ needs
For complex assignments, STEM teachers can call on AI to accelerate grading for large classrooms and provide more impactful personalized feedback.
- Mathematics: AI can read and verify equations are correct, check both the methodology and the answers, to provide feedback on errors in understanding or calculations
- Chemistry: AI can analyze equations for proper balancing, identify products of reactions, and evaluate the use of correct notation and terminology
- Engineering: AI can evaluate engineering diagrams – such as CAD drawings – for adherence to design specifications and engineering principles, plus clarity and technical accuracy
- Coding: AI systems assess code based on functionality and efficiency, running tests to determine if the code executes as intended, and providing feedback
The benefits of integrating AI into STEM grading practices
One of the main benefits of AI in STEM grading is that AI can recognize and evaluate various formats – including diagrams, code, and proofs – instantly and accurately. But it’s what that frees you to do that’s equally important.
AI releases educators from more burdensome manual processes to focus on adding value in ways that only human input can – such as personalized mentorship, having deeper discussions around concepts, and encouraging critical thinking skills.
Accelerated assessment and timely feedback
AI streamlines the process of assessing complex assignments, significantly reducing grading time. This means you spend less time grading and more time providing meaningful feedback to support your students’ learning. Faster grading means quicker feedback, which helps students stay focused and continue their learning journey with fewer delays.
Enhanced accuracy and consistency
AI offers a consistent and reliable approach to grading, as it maintains accuracy without the influence of fatigue or fluctuating perceptions over time. By providing more accurate and consistent evaluation, AI helps mitigate biases that can undermine traditional grading practices (see below), creating a more equitable assessment environment.
Student insights – and the time to action them
AI-powered analytics can spot trends in student performance. This helps you pinpoint opportunities at an individual and whole class level. And because AI has eliminated much of the more time-consuming manual work associated with grading, you have more time to act on those insights and improve student outcomes.
Why is human oversight still essential in AI-assisted grading?
While AI can accelerate assessment processes, it can’t replace interpersonal relationships between educators and students, understand the context of individual student progress, or personally support students to achieve their full potential. But it can make more time for you – as educators – to do that.
Plus, AI isn’t infallible. While AI can accelerate the grading process, human oversight is still needed to review and verify AI-assisted grades. For example, a student may provide an unconventional route to a solution that falls outside AI’s understanding. In these circumstances, AI may incorrectly grade it as wrong.
Educators, who are familiar with their students’ abilities and thinking styles, can intervene by revisiting these grades to ensure that students receive fair evaluations that accurately reflect their knowledge and skills.
It’s about finding the right balance between what AI and humans do best.
Barriers to AI adoption in STEM education
While there are clear benefits of AI-assisted grading in STEM, some institutions remain resistant to adopting these technologies, which is entirely understandable.
AI is still relatively new, and many educators may not yet understand how these tools work or how they can benefit both instructors and students. Much of the conversation surrounding AI tends to emphasize its limitations:
- It is often viewed as a blunt tool in a nuanced learning landscape
- There are concerns about inherent biases in AI systems
- Educators may worry overreliance on AI could dehumanize grading practices
However, this perspective overlooks the potential of AI to enhance educational outcomes when introduced in a responsible and intentional way.
By leaning into the strengths of current AI functionalities – such as pattern recognition and Optical Character Recognition (OCR) – educators automate routine elements of STEM grading, while maintaining the human elements that add most value to students.
Read more on educational technology and its impact on the learning landscape.
Best practices for STEM teachers implementing AI-assisted grading
Explore AI for different assignment types
AI isn’t a single tool – it comprises diverse functionality for different use cases. AI can be used for diverse assignment types including problem sets, quizzes, projects, bubble sheet exams, open-ended text-based answers, essays, and complex STEM assessments.
Speed up STEM visual format recognition
Use AI-powered visual format recognition to quickly analyze and verify equations, graphs, flowcharts, notations, chemical formulas, and more. AI recognizes handwritten work and diagrams – as well as complex elements like fractions and integral signs – so you can grade hand-created work accurately and efficiently.
Leverage large course analytics
Maintaining consistency in grading can be challenging on large courses. Use AI analytics to get actionable insights into whole class performance to tailor your teaching to address gaps. For example, by using AI to identify patterns in exam results that suggest areas where students are struggling.
Eliminate human grading bias
Grading bias does occur in human assessment processes. It’s been proven, for example, that students with surnames lower in the alphabet receive lower grades, due to the alphabetical ordering of student submission in learning management systems. This type of unintended bias can be eliminated using AI for answer grouping. This groups student answers together, to be assessed simultaneously, for more efficient and consistent grading.
Use dynamic rubrics
Rubrics are guidelines for student assessments, often used as scoring criteria for grading and marking student work. They provide transparency for students and clarity for assessors. AI can use your rubrics to mark complex STEM assignments according to your guidelines and provide meaningful feedback to learners.
Maintain human oversight
Keep teachers in control of the grading process by giving them the ability to adjust automated elements of AI-assisted grading, such as answer groupings and rubrics. This lets you benefit from AI assistance without being restricted by it.