An AI tool that assists educators by automatically grading assignments, especially for large classes. It can grade multiple-choice questions, short answers, and even provide feedback on essays based on a predefined rubric.
Marking is one of the heaviest parts of teaching. A lecturer with a large cohort or a teacher with several classes can face hundreds of essays and short-answer responses at once, each needing a fair mark and useful feedback. By the fiftieth script, feedback gets shorter and consistency drifts. Students receive results late, often after the next assessment is already due, so the feedback arrives too late to help them improve.
An AI grading assistant takes on the first pass. It reads each response against the rubric the educator provides, suggests a mark for each criterion, and drafts specific feedback quoting the student's own work. Multiple-choice and short answers with clear expected responses can be marked quickly, while essays receive suggested scores and comments. The educator reviews, adjusts and approves every result, so professional judgement stays in charge while the repetitive reading and typing is reduced.
The educator uploads the assessment task, a detailed rubric with descriptors for each level, and a few marked exemplars that show how the rubric applies in practice.
Student submissions are imported from the LMS. Identifying details are removed or replaced with codes before text is sent to the language model.
The LLM scores each rubric criterion, explains its reasoning with quotes from the submission, and drafts feedback phrased for the student, highlighting strengths and next steps.
A similarity check runs alongside, comparing submissions with each other and with external sources through a plagiarism detection service, flagging matches for the educator to investigate.
The educator works through a review screen, accepting or changing marks and editing feedback, then releases approved results to the LMS gradebook.
A professor uploads 200 student essays and a grading rubric to the AI assistant. The AI provides a suggested grade and detailed feedback for each essay, which the professor can then review and adjust, saving dozens of hours of grading time.
NLP, LLMs, Plagiarism Detection APIs
Student work and marks are personal information, so check that your LLM provider does not train on submissions and consider Australian-hosted services if your institution requires onshore data. TEQSA has published guidance for higher education on generative AI and assessment integrity, and state education departments set their own rules for schools, so confirm local policy before use. Students should be told when AI assists marking. Be cautious with AI-writing detectors, which are unreliable and should never be the sole basis for an academic misconduct finding. Final marks for credentialed assessments should always be signed off by a qualified educator.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
It can produce consistent first-pass suggestions when the rubric is precise and exemplars are provided, but it is not a substitute for expert judgement. It struggles with originality, subtle argument and discipline-specific nuance. Using it as a draft marker that an educator reviews is the responsible approach, and moderation across markers still matters.
Around two to four weeks for a first course. That covers connecting to the LMS, writing a rubric prompt, calibrating against a set of already-marked scripts and agreeing the review workflow. Each new assessment then needs a short calibration step before real submissions are marked.
Not reliably. AI-writing detectors produce false positives, particularly for students writing in a second language, and can be bypassed easily. Assessment design, such as in-class components, drafts, oral follow-ups and personalised tasks, is a more dependable way to protect integrity than detection software.
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