AI grading in exams: What is it and how does it work

Grading open-ended responses is a complex and essential process in education, recruitment, and training. By automating this task, organizations can significantly reduce manual workload while ensuring more objective, standardized, and consistent evaluation outcomes.

According to a study from the University of Surrey, an AI-powered grading tool has the potential to revolutionize assessment, offering up to 80% time savings and full consistency in grading.

Key takeaways

• AI grading automates the evaluation of open-ended questions such as essays, short answers, speaking, video, and coding tasks, reducing manual workload and improving consistency.

• It works through clear criteria. AI analyzes candidate responses against predefined instructions or rubrics, ensuring evaluations align with expectations.

• Different AI models may suit different tasks.

• AI grading boosts efficiency and fairness. Studies show up to 80% time savings and consistent, bias-free results when properly implemented.

• AI systems must address bias, privacy, and contextual understanding while maintaining transparency and human oversight.

What is AI grading?

AI grading is the use of artificial intelligence to automatically evaluate and score open-ended questions such as short text answers, essays, audio and video responses, or coding tasks. Instead of a human grader, AI systems apply machine learning models and natural language processing (NLP) to analyze a candidate’s answers and provide results.

How does AI grading work?

AI grading is the process of using artificial intelligence to automatically evaluate candidate responses based on predefined criteria. In practice, you provide the AI with the question, the candidate’s answer, and the evaluation criteria. The AI then analyzes the response against those criteria and generates a grade.

The clarity and detail of your instructions determine how AI grades. By giving the AI clear, detailed instructions or a rubric, you ensure more accurate and consistent grading that aligns with your expectations.

Which AI models can be used for grading?

Different large language models (LLMs) such as ChatGPT (OpenAI), Gemini (Google DeepMind), and Claude (Anthropic) can all be used for AI evaluation. Each of these providers offers multiple model versions that vary in capabilities.

• Open-ended writing and essays: Larger models like GPT-4o or Claude 3 Opus provide more nuanced evaluation, since they handle long-form reasoning and style better.

• Short-answer or factual questions: Lighter models such as GPT-4o mini or Gemini 1.5 Flash are well-suited for structured, concise responses.

• Speaking and audio responses: Models with strong multimodal capabilities, like Gemini 1.5 Pro or GPT-4o, can process transcripts alongside contextual cues for more reliable evaluation.

• Coding tasks: Models with advanced reasoning and programming understanding, such as Claude 3.5 Sonnet or GPT-4o with Code Interpreter, are better suited for technical tasks.

TestInvite provides the flexibility to integrate with different AI models depending on your assessment needs. Instead of relying on a single provider, TestInvite allows you to choose the AI model best suited for each question type.

What are the benefits of AI grading?

• AI-assisted grading saves evaluators significant time and effort compared to manual grading.

• Grading criteria can be customized to fit specific needs.

• It is more consistent and systematic, which helps to reduce subjectivity.

• AI delivers transparent feedback on strengths, weaknesses, and areas for improvement, together with the rationale for each grade.

• The same standards are applied every time, removing the effects of fatigue, mood, or unconscious bias.

• It enables fair and scalable assessment across large groups.

• Professionals gain more time to focus on higher-value tasks.

• It helps to deliver immediate results that support faster decision-making.

What are the cons of AI grading?

• AI-powered grading can reflect bias if the training data or algorithms are unbalanced.

• It may struggle to recognize creativity, nuance, or context in responses.

• Without proper measures, it can raise privacy and data security concerns.

How are short-answer questions graded with AI?

While creating the question, the author provides the AI model with instructions and scoring guidelines that define how the response should be evaluated. For short-answer questions, the AI combines the candidate’s response with the question and the instructions, and evaluates aspects such as accuracy, relevance, and completeness. Using the provided instructions and scoring guidelines, it then automatically generates a grade.

Example question and grading instructions

Context:

The test taker is a high school student taking a history exam. The answer is expected to demonstrate basic historical knowledge, clear explanation, and simple but correct language.

Evaluation criteria:

A correct response must mention at least one valid reason, such as:

• Availability of coal or iron resources

• Development of transportation systems (canals, railways, ports)

• Agricultural revolution and increased productivity

• Political/economic stability or supportive government policies

• Early innovations in textiles

Accept paraphrased answers that clearly convey the meaning of one of these reasons.

• The explanation should show relevance (not just a list of unrelated facts).

• Do not penalize grammar or spelling unless it prevents understanding.

How are essay questions graded with AI?

For essay questions, the AI takes the candidate’s written response together with the original question and the accompanying instructions. It then evaluates the essay across multiple dimensions, such as grammar, vocabulary range and precision, logical flow and coherence between paragraphs, overall structure, and the accuracy with which the candidate addresses the question. By aligning this analysis with the provided instructions and scoring guidelines, the system automatically generates a grade that reflects both the content and the quality of writing.

Example question and grading instructions

Context:

The test taker is a college student majoring in Languages. The essay should demonstrate academic writing skills, rich vocabulary, and clear argumentation.

Evaluation criteria:

1. Content & Relevance (40%) 

• Addresses the topic directly.

• Includes both advantages and challenges.

• Uses relevant examples.

2. Structure & Organization (20%)

• Clear introduction, body, and conclusion.

• Logical flow of ideas with transitions.

3. Language & Vocabulary (40%)

• Grammar accuracy, spelling, and sentence structure.

• Academic tone, rich vocabulary, and fluency.

How are speaking questions graded with AI?

For speaking questions, the candidate’s audio response is first converted into text using speech recognition. Once converted to text, the AI conducts a detailed analysis, assessing dimensions such as accuracy, fluency, lexical choice, and relevance in relation to the defined scoring guidelines. Based on this analysis, the system automatically generates a grade that reflects both content quality and language performance.

Example question and grading instructions:

Context:

The test taker is a job applicant taking a pre-employment speaking assessment. The response will be evaluated based on clarity, organization, and language use in a workplace context.

Evaluation criteria:

1.Content & Relevance (40%)

• Addresses the question directly.

• Explains clearly why the chosen skill is important.

• Provides at least one example or justification.

2.Organization & Coherence (20%)

Clear beginning, middle, and conclusion.

Logical flow of ideas with smooth transitions.

3.Language & Fluency (40%)

• Grammar accuracy, correct sentence structure.

• Range and appropriateness of vocabulary for a workplace context.

• Fluency as reflected in the transcript (not overly fragmented, minimal fillers).

How are video recording/interview questions graded with AI?

For video questions, the candidate’s recorded response is first transcribed into text using speech recognition. The AI then evaluates both the transcript and the audio-visual features of the response, such as pronunciation, fluency, clarity, and delivery. These elements are aligned with the given instructions and scoring guidelines, enabling the system to automatically generate a grade that reflects both verbal accuracy and communication skills.

Example question and grading instructions:

Context:

The test taker is a working professional in a corporate environment. The question is designed to assess the content structure, communication skills, and grammar.

Evaluation criteria:

1. Content & Structure (40%)

The response should follow the STAR framework:

• Situation/Task: Describes the context and challenge.

• Action: Explains the steps taken to adapt.

• Result: Clearly states the outcome or lessons learned.

Score high if all elements are covered with clarity and relevance.

2. Communication & Soft Skills (30%)

• Demonstrates problem-solving, adaptability, and decision-making.

• Shows confidence, empathy, and clarity of thought.

3. Grammar & Fluency (30%)

• Minor grammar mistakes: deduct up to 5% (if meaning is still clear).

• Frequent errors or awkward phrasing: deduct up to 10%.

• Severe grammar issues that hinder understanding: deduct up to 20%.

• Fluency, vocabulary richness, and sentence variety add to the score.

How are coding questions graded with AI?

For coding questions, the candidate’s submission is compiled and executed against a set of predefined test cases. The AI evaluates not only whether the code produces correct outputs but also its efficiency, readability, and adherence to the given requirements. This evaluation is aligned with the provided scoring guidelines, enabling the system to automatically generate a grade that reflects both functional accuracy and coding quality.

Context

The test taker is a junior backend developer candidate. Evaluate the submitted Python 3 code and its behavior (no external packages). Focus on correctness first; simple, readable solutions are acceptable.

Evaluation Criteria

1.Correctness (60%)

• Returns the right result on typical and edge cases (different lengths, repeated letters, single-char).

• No hard-coding; works generally.

2.Code Quality & Readability (25%)

• Clear logic, meaningful names, minimal complexity.

• Idiomatic Python (e.g., collections.Counter or simple counting).

3.Efficiency (15%)

• Reasonable time complexity (expected O(n)).

• Avoids unnecessary sorting of full strings if using counting is straightforward (sorting O(n log n) is acceptable at junior level but counts slightly lower than O(n) counting).

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Frequently asked questions (FAQ)