Labeling Essays & Short Answers for AI Grading Models

Tatiana Verbitskaya -

In the modern world, where pupils and students write thousands of essays and short answers, a significant problem arises: how to check them quickly and objectively?

Manual checking has always been slow and expensive, and, most importantly, it involves a subjective element. The scores for the exact text can vary significantly if checked by two different teachers.

To solve this problem, AI scoring, also known as Automated Essay Scoring, emerged. This technology utilizes innovative natural language processing models to provide a text with a fast, consistent, and objective evaluation.

But how to teach a machine to see grammatical errors or evaluate the logic of the presentation? Annotation is necessary here, as this is the key process where experienced teachers and philologists clearly label every evaluation criterion in thousands of texts, including grammar, logic, and content compliance. They also assign a gold standard, ideal score to each text. In fact, experts create a "textbook for ideal checking" by which the AI learns to evaluate texts in the same way as the best teacher.

Quick Take

  • The model learns to recognize not only the overall score but also the analytical components.
  • Highlighting specific text fragments teaches AI to associate certain phrases with a score.
  • The score consistency stage ensures that AI learns only from those texts where the scores of several experts coincide.
  • AI frees up the teacher's time, provides students with high-quality, targeted feedback, and enables the individualization of learning.
  • The model uses semantic similarity to check the content compliance of short answers.

Key Evaluation Criteria that AI Sees

To teach AI to check essays, we must clearly show it the rules by which the text should be evaluated. These rules, known as rubric scoring, break down the evaluation into several dimensions that AI must learn to recognize and understand.

Overall Final Score

First, experts assign a single, overall score to the text. This score, for example, from 1 to 6, reflects the general quality of the essay. The goal of this step is to teach AI to correlate its future predictions with this gold standard evaluation. This gives AI a general idea of what a good or bad essay should look like.

Analytical Components of the Score

The most valuable part of annotation is breaking down the overall score into separate, measurable categories. This allows AI to see precisely why a particular score was given.

  • Grammar and Mechanics. Experts label all grammatical, spelling, and punctuation errors. This allows AI not only to find the error but also to learn to flag plagiarism if the model sees that the structure of the text differs too much from the norm.
  • Content and Topic Compliance. This evaluates how closely the text aligns with the task and whether the student utilizes key terms. This is related to semantic similarity, where AI learns to compare the content of the student's text with the ideal answer.
  • Syntax and Cohesion. This is the evaluation of the logic of the presentation. Experts verify whether transition words are used correctly and whether the thought flows smoothly and logically from one sentence to the next.

The benefit of such detail is vast, as AI can provide students with specific feedback instead of a dry score.

Computer Vision | Keymakr

Logical Structure Labeling

The model must learn to recognize the "skeleton" of the text. Experts label the key structural elements of the essay. They clearly highlight where the thesis is, where the supporting arguments are located, and where the conclusion is situated. This teaches AI to recognize whether the essay has a clear, expected structure, which is a requirement for most academic papers.

Annotation Methods for NLP

For AI to accurately evaluate texts, it needs not just a final score but detailed labeling that explains the reasoning behind that score. Special annotation methods are employed for this purpose. In general, data is marked up by segmenting text and then classifying each highlighted fragment.

Evaluation Fragment Labeling

This method enables AI to understand precisely which words or sentences impact the final score.

The expert highlights specific parts of the text that are evidence of high or low quality. For example, the instructor can highlight a well-argued paragraph as [Quality: High Evidence] or highlight an incorrectly constructed sentence as [Error: Tense Mistake].

Thanks to this, AI learns to associate specific phrases, styles, and levels of detail with high or low scores. It sees what is good and what needs correction.

Short Answer Annotation

This method is used for questions that require an accurate, factual answer, such as those in the natural or exact sciences.

Here, the annotator does not evaluate the style but checks for the presence of key facts. They label whether the answer contains a [Correct Concept], whether there is a [Missing Detail], or whether the text includes [Irrelevant Information].

This enables AI to quickly and accurately verify whether the answer contains all the necessary key information, regardless of its formulation.

Score Consistency Stage

This is the most essential step in ensuring the objectivity of training. Human evaluation is subjective. Two experienced teachers can give the same essay different scores. If we train AI on inconsistent data, the model will be inaccurate.

To avoid this, the exact text is annotated by several experts. The system collects the scores and checks how closely they align with each other. The model learns only from data where all expert scores were close or the same. This creates a perfect gold standard and ensures that AI will evaluate texts consistently and objectively.

AI as a Pedagogical Tool

The implementation of AI scoring for texts has far-reaching consequences that go far beyond simply assigning scores. It radically changes how teachers work and students learn.

Freedom of Time for the Teacher

The principal practical value of AI is freeing up the most valuable resource: the teacher's time. The model can analyze and evaluate thousands of essays in minutes. While a person can check 20 to 30 papers in an evening, AI processes volumes that were previously unavailable.

Teachers gain the opportunity to abandon monotonous routine work. They can dedicate the freed-up time to individual work with students who need it most, developing new, creative assignments, or improving their own qualifications.

High Quality Feedback

AI models provide students with much deeper feedback than just a numerical grade in a gradebook. Instead of a dry score of "4 out of 5," the model provides specific recommendations that help the student improve their skills.

For example, AI can clearly point out: "Check the use of the comma in the complex sentence at the beginning of the third paragraph", or "Strengthen the evidence base for your second argument." Such detailed formative feedback allows the student to learn from their mistakes immediately, not a week later when they receive the checked notebook.

Individualization of Education

The data collected by the AI system becomes a powerful tool for improving the curriculum. The system analyzes thousands of papers in a group or school. It identifies the types of errors that are most common.

Based on this analysis, learning materials or assignments can be automatically adapted to focus precisely on this knowledge gap, ensuring a personalized approach to learning.

FAQ

What is the "Gold Standard" in the context of annotation?

The gold standard is the ideal, consistent score assigned to the text by experts whose opinions coincide. AI uses these "gold standards" as the single correct sample for training its model, ensuring that it evaluates the work in the same way as the best instructor.

How does AI understand the logic and structure of an essay?

Experts do not just assign a final score, but also label the logical elements of the text. They highlight where the thesis is, where the supporting arguments are located, and the conclusion. This teaches AI to recognize the "skeleton" of the text and check whether it has a clear, expected structure.

What is the most significant benefit for students from AI scoring?

The biggest benefit is the high-quality feedback. Instead of a simple number, AI provides specific recommendations. For example, it can point out: "You made a mistake in using the passive voice" or "More evidence is needed for argument 2." This allows students to learn from their mistakes instantly.

How does AI check if the text is not plagiarized?

AI models learn to recognize not only grammar but also the student's style and structure. Suppose the model detects that the text or its parts deviate significantly from the style, or contain phrases that do not align with the student's level or vocabulary. In that case, it can raise plagiarism flags for further human review.

What is "semantic similarity" and how is it used?

Semantic similarity is used to evaluate short answers. AI compares the content of the student's answer with the ideal answer. Even if the student uses different words but correctly conveys the key concepts, AI will still count the answer as correct.

What does it mean that AI contributes to "adaptive learning"?

The AI system analyzes thousands of papers across the entire class and identifies the types of errors that are most common. For example, most students have problems with comma usage. Based on this analysis, learning materials can be automatically adapted to focus precisely on these common knowledge gaps.