LLMGraderGrading is only the first step.
LiveSynthesis by LLMGrader Launch Browser Version

AI-assisted grading and learning insight

Grading is only the first step.

LLMGrader helps instructors turn student responses into evidence about learning—by each student, across the class, and over time.

Free for educational use

Instructor-reviewed grading and feedback

Draft grades and individualized feedback using the instructor’s questions, solutions, rubrics, and guidance. Create a separate password-protected PDF feedback file for each student.

Evidence about understanding

Move from individual responses to class-wide patterns, persistent difficulties, and progress across assessments.

A complete assessment workflow

How LLMGrader works

Each stage produces information the next stage can use. Instructors remain responsible for the final grade, feedback, and instructional decisions.

  1. 1

    Choose an AI model

    Select a private local model, an institutional endpoint, or a cloud provider and confirm that LLMGrader can reach it.

  2. 2

    Create the quiz

    Enter the quiz structure, then add each question, solution, point value, and rubric.

  3. 3

    Refine the assessment

    Use the optional Chat Panel to improve questions, check solutions, and develop grading rubrics.

  4. 4

    Import student answers

    Follow the two import steps, including the guided D2L workflow or LLMGrader CSV files.

  5. 5

    Grade, review, and return feedback

    Generate suggested grades and feedback. After reviewing and approving every result, create an individual password-protected PDF feedback file for each student.

  6. 6

    Examine learning

    Summarize grades, identify common themes across the class, and—in the desktop version—follow progress over time.

Read the detailed how-to guide

From responses to evidence

From grading to instructional insight

Instructor-reviewed grading is the foundation for understanding learning by each student, across the class, and over time.

  1. 1

    Student responses

    Answers are considered with the instructor’s questions, solutions, rubrics, and guidance.

  2. 2

    Instructor-reviewed grading

    Suggested grades and feedback are reviewed, edited, and approved by the instructor.

  3. 3

    Conceptual understanding

    See what individual students understand and find class-wide strengths and misconceptions.

  4. 4

    Development over time

    Compare assessments to distinguish persistent difficulties from developing understanding.

  5. 5

    Instructional action

    Use the evidence to guide feedback, follow-up teaching, and support for particular students.

Grading Guidance

Direct another grading pass, reconsider a deduction, or adjust the emphasis and detail of feedback without changing the underlying rubric.

Common Theme Analysis

Examine the class as a whole to find shared strengths, misconceptions, and useful areas for instructional follow-up.

Course Progress

Use saved assessments and completed Common Theme analyses to evaluate quantitative and conceptual development over time.

Privacy, capability, and cost

Choose how AI is provided

LLMGrader does not require one particular provider. Choose the arrangement that fits your institution’s privacy requirements, available hardware, model needs, and budget.

Run locally

On the desktop version, use an Ollama-compatible model running on a capable instructor computer. Prompts and student answer content remain on that computer.

Maximum local privacy

Institutional endpoint

Connect to an Ollama-compatible server operated behind a university firewall. The browser version can connect when the server permits the website origin and uses HTTPS.

Institutional control

Cloud providers

Use OpenRouter, OpenAI, Anthropic, or—on the desktop—Ollama Cloud. Options include free and inexpensive models as well as frontier models.

Broad model choice

Student names and identifiers are not sent to the LLM. When a cloud model is used, answer content, prompts, solutions, and rubrics are sent to that provider. Use of AI and student work should follow institutional policy.

Learn about local models, hardware, privacy, and cloud options

Choose the version that fits the setting

Browser and desktop versions

Browser version

Runs without installation on a laptop or tablet. Use supported cloud providers or an institutionally hosted compatible endpoint. LLMGrader.com does not retain application data.

Launch Browser Version

Desktop versions

Add locally run models, Ollama Cloud, organized course folders, and advanced Course Progress analysis across assessments.

Ask about desktop access

See the application

A complete LLMGrader workflow

The demonstration follows a fictional accounting class from quiz setup and answer import through grading, feedback, class-level analysis, and progress over time.

Read the video transcript

Related product

LiveSynthesis by LLMGrader

Pose a question, collect live responses from every participant, and use AI to create a fast first synthesis. See common ground, distinct perspectives, possible misconceptions, and ideas that might otherwise be missed.

Explore LiveSynthesis

About

Created by educators and software developers

LLMGrader was created by professors who combine years of university teaching with software-development experience.

Sanjay SrivastavaPeter MolnarYusen Xia

Contact

Questions or institutional use

Contact us about desktop access, institutional deployment, or support for your learning management system.

llmgrader@outlook.com