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Build open-response writing assignments in plain language. Learners answer in their own words, AI grades against the rubric you define, with per-criterion feedback and evidence pulled from their writing. SCORM-ready for any LMS.
An assessment format where the learner answers in their own words, an essay, a short answer, a case-analysis paragraph, instead of picking from a list. The AI grades each response against an instructor-defined rubric, with per-criterion scoring, evidence quoted from the response, and a specific next-step recommendation.
From a prompt and a rubric to a SCORM-ready open-response activity. No essay-grading service, no rubric XML.
Describe what you want the learner to write. The prompt can include source material the learner should reference, or constraints on the response.
Name the criteria you'll grade against, claim quality, use of evidence, structure, mechanics, with weights. The AI grades every response against the same rubric you'd hand a TA.
Grade 3–5 sample responses yourself. The AI matches your standards instead of inventing its own. This dramatically improves agreement between AI and human graders.
One command produces a SCORM 1.2 zip. Per-criterion scores post back to the gradebook in any SCORM-compliant LMS.
The same toolchain powers essay grading, short-answer assessment, code review, case-analysis grading, and reflective-writing assessment.
Grade short-answer and essay assignments at scale, with per-criterion feedback every student gets back. Especially valuable for high-enrollment intro courses.
Practice short-answer questions on demand. Students get rubric-aligned feedback immediately, so practice actually improves performance on the real exam.
Replace multiple-choice compliance quizzes with short-form scenario analyses. AI grades against the rubric, instructors review the borderline cases.
Ask learners to explain code, debug an output, or write a short design-doc paragraph. Grade against rubric criteria, code clarity, root-cause accuracy, tradeoff articulation.
For professional development or clinical-practice training, grade reflections against rubrics like depth-of-analysis and evidence-of-application, not surface presence.
For certifications that require constructed-response performance tasks, AI grading provides consistent, defensible rubric application across all candidates.
When the response comes in, the AI scores each rubric criterion separately. Each score points to specific text in the response, so both learners and instructors can verify exactly why a response got the score it did.
Update the rubric or recalibrate any time. Re-export the SCORM package and the next submission grades against the new rules.
Each rubric criterion gets its own score and feedback, not a single opaque grade. Learners see exactly where they need to improve.
Every score points to specific text from the response. Both learners and instructors can verify that the score reflects what was actually written.
Grade a few responses yourself and the AI matches your standards. Dramatically improves agreement between AI grading and human grading.
Add a rubric criterion, change weights, raise the calibration sample size, or rewrite the prompt, just describe the change in your own words. Your coding agent rebuilds the grader and re-exports the SCORM package without you ever editing a rubric file.
When you're happy, export as a SCORM 1.2 package for your LMS, or use the same HTML bundle standalone.
counter-argument at weight 0.25. Other criteria re-balanced to sum to 1.Paste any prompt into your coding agent to get a complete AI-graded assignment as a single self-contained HTML file. Adjust prompt, rubric, or calibration in plain language.
A 250-word short-essay prompt asking students to apply marginal analysis to a real-world example, with a 4-criterion rubric and 3 calibrated samples.
Using /ai-graded-responses, build a 250-word marginal-analysis essay with a 4-criterion rubric. Calibrate against 3 sample responses.
A grade-10 history DBQ prompt referencing 3 supplied documents, graded on claim quality, document use, and counter-argument across a 6-criterion rubric.
Using /ai-graded-responses, build a grade-10 DBQ on the New Deal with 3 supplied documents and a 6-criterion rubric.
A short-form ethics scenario asking the learner to defend a chosen course of action, graded on policy alignment, stakeholder consideration, and reasoning clarity.
Using /ai-graded-responses, build a scenario-based ethics assignment with a 4-criterion rubric. Reward defensible disagreement with policy.
Engineers explain a debugging walk-through for a non-trivial bug, graded on root-cause clarity, evidence use, and prevention recommendation.
Using /ai-graded-responses, build an engineering bug-write-up assignment with a 5-criterion rubric covering root cause, evidence, and prevention.
Trainee writes a SOAP note for a supplied case. Graded on subjective completeness, objective accuracy, assessment quality, and plan specificity.
Using /ai-graded-responses, build a SOAP-note assignment from a supplied case. Rubric covers S, O, A, and P with weighted criteria.
An open reflection prompt for a mentorship program, graded on depth-of-analysis, evidence-of-application, and forward-looking specificity, not surface presence.
Using /ai-graded-responses, build a mentee reflection-journal assignment with a 3-criterion rubric rewarding depth, application, and specificity.
Every AI-graded assignment exports as a standards-compliant SCORM 1.2 package. Upload the zip, assign it like any other course activity, and per-criterion scores flow back to the gradebook automatically.
cmi.core.score.raw and cmi.core.lesson_statusRubrics, calibration, LMS compatibility, integrity, model choice, answered directly.
An assessment format where the learner answers in their own words, a short answer, an essay, a structured paragraph, and AI grades against an instructor-defined rubric. Each response gets per-criterion scores with evidence quoted from the response and a specific next-step recommendation.
Agreement rates depend heavily on rubric clarity and calibration. Without calibration, AI grading typically agrees with human grading at roughly the same rate as two human graders agree with each other. With calibration (you grade a few responses yourself), agreement goes up substantially.
Yes. Every per-criterion score is accompanied by evidence quoted directly from the response. Both learners and instructors can verify that the score reflects the actual writing.
The system flags borderline scores and responses with unusual patterns (very short, very long, off-topic) for instructor review. AI handles the long tail, instructors handle the judgment calls.
Yes. Every activity exports as a SCORM 1.2 package, which works in Cornerstone, Moodle, Canvas, TalentLMS, Docebo, Brightspace, and every other SCORM-compliant LMS. Per-criterion scores post back to the gradebook automatically.
Yes. Each assignment has its own rubric. You can reuse rubrics across assignments or write a new one each time, whatever fits your assessment design.
Optional integrity heuristics flag responses with AI-generated patterns and copy-pasted content. These are signals, not verdicts, the instructor still makes the academic-integrity call.
AI-Graded Responses is in active development. It will install via your coding agent with a single command, the same way the existing role-play skill works today. Add your email below and we'll let you know the day it ships.