Opportunity is founded on education. It can pull people out of poverty and place them on the path of success. Yet even today, education is failing a lot of students because of biased and inconsistent grading. Teachers are also overworked, underpaid, and tend to bring in unintended biases when it comes to grading. Such outcomes are, in turn, unfair and add to the already disadvantaged groups’ disadvantage.
The problems of grading and visibility will be solved by automated grading using artificial intelligence (AI). Based on the principle that machine learning and natural language processing can be utilized to sequentially scan a student’s work to assess performance more objectively, consistently, and efficiently, AI grading tools are possible. However, the more the technology improves, the more automated grading will be the way towards a fairer education for everyone.
The Flaws of Human Grading
To understand why automated grading is needed, it’s important to recognize the issues inherent in current human grading processes. Despite teachers’ best intentions, research shows grading can often be biased and inconsistent in practice.
Sources of Grading Bias
Many factors can influence teachers to unintentionally grade some groups of students more harshly:
- Implicit biases about race, gender, socioeconomic status, etc.
- “Halo effect” – allowing prior perceptions about a student to skew grading.
- Unclear grading criteria leave room for subjectivity.
- Rushing through grading due to time constraints.
- Anchoring and recency effects: letting a student’s most recent or first performance outweigh the full picture.
The impacts of these biases add up. For example, studies have found that teachers give Black students lower grades in math and language arts classes compared to similar white students. Harsher grading of minorities also appears as early as kindergarten.
In response to these challenges, the development of an AI grader offers a compelling solution. By leveraging data-driven algorithms and consistent evaluation criteria, an AI grader can reduce the influence of human subjectivity and systemic bias. When designed with fairness and transparency at its core, this technology has the potential to promote greater equity and reliability in student assessment.
Inconsistency Between Graders
Beyond biases, research shows teachers often lack consistency in grading the same piece of work. In a study published in Educational Measurement: Issues and Practice, researchers found the level of agreement between teachers grading the same exam ranged between 13-30%, depending on the subject.
Such variability leads to the perception of unfairness among students. Whether conscious or not, grading inconsistencies give advantages to some students over others, impacting lives and future opportunities.
The Promise of Automated Grading
Automated grading systems leverage machine learning algorithms to evaluate student work based on pre-defined standards, rubrics, and example responses. By scanning for relevant content rather than superficial features, these AI tools can provide fast, bias-free, reliable grading at scale.
Reducing Bias
Unlike humans, algorithms have no inherent biases or preconceptions. The only biases automated grading systems pick up come from the data they are trained on. However, with careful dataset selection and validation, these biases can be minimized to near zero.
A Level Playing Field
Free from human biases, automated grading judges all students by the same consistent standards. It does not tire out, anchor on past performance, or change criteria like human graders. Students know the exact expectations in advance, allowing them to prepare appropriately rather than having to guess if a teacher will like their work.
Saving Teachers Times
Grading student work is enormously time-consuming. A 2022 survey by the EdWeek Research Center found that the median teacher works 54 hours per week, dedicating approximately 5 hours weekly to grading and providing feedback on student work. Automated grading systems can reduce this burden by taking on routine grading tasks and allowing teachers to focus on higher-value activities.
More Granular Insights
Unlike giving just a single grade or mark, automated grading tools can provide rich insights into students’ strengths and weaknesses. Natural language processing can identify the specific concepts a student grasped or needs more help with, enabling better personalized support.
Current Capabilities and Limitations of Automated Grading
Several automated grading tools have already demonstrated success in grading essays, short answers, math problems, coding assignments, and more. However, the technology still faces some key limitations today.
Subjects Where Automated Grading Excels
Automated grading is easiest to apply accurately in subjects with clear right and wrong answers, like math, science, programming, etc. The first automated grading systems focused on scanning multiple-choice tests and have now advanced to evaluating short numerical answers, math proofs, and computer code.
Essay Grading Systems
Automated essay scoring is also reaching useful levels of accuracy. AI grading tools can now evaluate key elements of writing like thesis clarity, organization, vocabulary use, sentence structure, grammar, and more. Large-scale studies have found that top systems grade essays to the same levels of consistency as humans.
Remaining Challenges
However, accurately grading more subjective or open-ended work still proves difficult for machines in many domains:
- Assessing creativity – unusual solutions machines haven’t seen before often get unfairly marked down.
- Judging the factual accuracy of statements rather than just writing quality.
- Detecting instances of plagiarism and improper citation.
- Evaluating group work and collaboration skills.
- Providing qualitative feedback for improvement rather than just a grade.
As the supporting AI technologies continue to develop, automated grading systems will likely expand to handle these complex cases. But for now, human judgment still holds the advantage in many scenarios.
Hybrid Evaluation
The most practical near-term approach is implementing automated grading to handle routine objective grading tasks while reserving subjective assessments for human reviewers. Together, the strengths of automation and human insight combine to enhance fairness and provide better feedback than either can deliver alone.
Ongoing Refinement Through Machine Learning
Like any machine learning application, automated grading systems improve their accuracy as they process more data. Exposing algorithms to more examples of student work and teacher feedback allows the AI to better distinguish high-quality responses.
Leading systems also calibrate themselves to match the grading distribution of specific teachers and schools. This prevents concerns over the AI being harsher or more lenient than current practices. Administrators can easily tune scoring models over time as expectations evolve.
Smodin: An Emerging AI Grading Assistant
One automated grading tool exemplifying the latest capabilities of AI is Smodin. Offering essay scoring, math problem checking, coding evaluation, and more, Smodin aims to be an always-available teaching assistant for grading activities.
Designed Specifically for Fairness
Many AI grading tools focus solely on efficiency. However, Smodin was created based on the vision that automated grading done right can help produce fairer outcomes for students. Custom machine learning algorithms explicitly calibrate the system’s evaluations to match teacher expectations and avoid biases.
Works Across Multiple Domains
While most tools specialize in one type of content, Smodin provides a unified platform supporting various key subject areas:
- Essay and short answer grading.
- Math problem checking.
- Code evaluation for programming assignments.
- Feedback generation identifies areas for improvement.
This flexibility allows Smodin to handle assignments from different classes and grade each part appropriately. Students also gain a consistent experience using the same system across subjects.
Instructor-Led Dataset Training
A unique advantage of Smodin is the ability for individual instructors to refine the grading to fit their standards. By uploading sample graded assignments to continually train the algorithms, the system adapts to what each teacher specifically looks for rather than a one-size-fits-all model.
Over time, schools accumulate large datasets covering a diverse range of student work. This allows Smodin to become highly calibrated to the grading policies and expectations of that institution.
Ongoing Accuracy Improvements
The Smodin team rigorously tests system performance before deploying updates to ensure enhancements translate to measurable progress. Recently, a scoring overhaul increased short answer grading accuracy by 14% based on benchmark datasets.
Through rapid iteration, Smodin aims to eventually match professional teachers in all aspects of reliable, ethical grading.
The Path Towards Fair Automated Grading
Automated grading powered by AI promises to help address systemic inequities in education. However, achieving the full potential impact requires deliberate design choices and responsible development.
Centering Fairness Throughout Development
Algorithms inherit the biases of the data they learn from. Therefore, AI grading systems must proactively monitor for unintended biases and mitigate them through techniques like balanced dataset aggregation.
Companies must also conduct bias audits before deployment and commit to ongoing improvement, as new issues may emerge. Centering ethical considerations from the start prevents harmful impacts down the line.
Preserving Instructor Discretion
While seeking consistency, automated systems should not eliminate teacher discretion over final grades. Instructors should retain override capabilities to account for exceptions and extreme circumstances that the algorithm may not recognize.
Transparent Design
For acceptance among wary students and faculty, vendors cannot treat algorithms as black boxes. Systems should provide clear explanations of grading decisions and allow inspection of model design.
Shared Development Standards
Currently, automated grading solutions use a mix of proprietary algorithms. This risks inconsistent experiences as students switch between platforms. Common standards for desired capabilities and processes would accelerate progress towards fairness.
With conscientious implementation, automated grading can help schools reward students primarily based on merit rather than other factors outside their control.
Conclusion
In an ideal education system, every student receives fair assessments of their work free from bias. However, inconsistent and prejudicial human grading practices continue obstructing this vision today. By applying machine learning to grade assignments more objectively, automated grading tools like Smodin offer a promising solution.
As the supporting technologies improve, automated grading will likely expand beyond routine testing to enhance feedback and fairness across most subjects. Teachers could shift more attention towards mentoring while algorithms handle the bulk of the grading work.
Achieving this future requires designing systems focused on ethical functionality rather than just efficiency. Following development best practices around transparency and bias prevention will lead to automated grading fulfilling its potential as a force for equity.
