How AI Grading Works in LiveCase Simulations

How AI Grading Works in LiveCase Simulations | LiveCase blog

A score is useful only when you understand what it measures. In an interactive case, decide which written answers or character conversations should be assessed and what a good response should demonstrate.

Begin with the assessment criteria

A polished response does not necessarily establish understanding. Define the evidence you expect to see and plan how you will review it.

For example, a learner might need to identify a stakeholder, use a case fact, and justify a trade-off.

Test the criteria with strong, weak, and borderline answers before relying on automated results.

How Assessment Lives Inside the Simulation

LiveCase can combine scenario content, questions, branching paths, and AI character conversations.

Consider an illustrative crisis-management case: a learner must decide how a company should respond to a product recall after hearing from different stakeholders.

The author can build branching developments and ask for a written justification or a conversation with a character.

Authors can configure AI grading for written answers and character conversations. That does not mean every branch or decision is automatically scored.

Students can still use external AI tools. State permitted uses and assess their understanding through explanations and appropriate follow-up.

The assessment should produce evidence you can discuss with the learner.

What the AI Actually Scores

Configured criteria determine what the grading should assess. The following are examples of criteria an instructor might define, not built-in universal scoring dimensions:

Use of evidence. Does the response use relevant facts from the case?

Reasoning. Does the learner explain the recommendation and its trade-offs?

Handling uncertainty. Does the response distinguish known facts from assumptions?

Communication. Does the character conversation address the stakeholder's concern?

Review the criteria and test the results. Do not assume automatic scoring of response speed, team dynamics, or consistency across the entire case.

Why Explainable AI Grading Matters

An automated score should be examined alongside the response and the intended criteria. Being inside a simulation does not remove the possibility of inaccurate or unfair grading.

AI grading assesses a response; it is not a detector of AI use or proof of authorship.

Use the learner's answer or conversation to begin a debrief. Ask which evidence mattered and what would change their recommendation.

Explain how students can raise concerns about a score and how instructor review will be handled.

Prepare a graded activity

Use the AI Case Authoring Studio to draft a scenario. Define the written answer or character conversation you want assessed and configure its grading criteria.

Start with a short negotiation. Ask learners to explain a recommendation, then test the criteria with different plausible responses.

Our authoring guide covers the broader workflow. Allow time to review the case and preview the learner experience.

If you prefer a supported build, discuss your assessment requirements with Studio Services.

Try one activity, inspect the results, and revise the criteria before wider use.

FAQ

What kinds of assessments can AI grade in a simulation?

Authors can configure AI grading for written answers and character conversations. The assessment depends on the criteria; it does not automatically grade every qualitative decision.

Does the AI detect if students are using ChatGPT during the simulation?

AI grading does not establish whether a learner used ChatGPT. Simulations do not make external AI assistance impossible.

What should instructors review?

Review the criteria, representative responses, and disputed or surprising results. Confirm the available review controls for your delivery workflow rather than assuming a particular override interface.

What happens in a cohort-based simulation with multiple roles?

LiveCase supports multiple learner roles. Role support does not imply automatic grading of intra-team collaboration or a team-dynamics dashboard.

Is AI grading biased against certain student populations?

An automated score can be inaccurate or unfair. Test responses from varied learners and writing styles, inspect disagreements, and retain human review.