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Multiple choice to Essay with Feedback Aide rubric conversion

Multiple choice to essay with Feedback Aide rubric conversion is a job in Item Bank Health Check (IBHC) that automatically transforms Multiple choice Questions into Essay with rich text Question.

The question is modified to be open-ended and a rubric is generated to include correct, alternate and incorrect answers. This allows the modified Item to be scored with Feedback Aide.

IBHC is a premium feature and may not be included in your product license. For more information, reach out to your Learnosity Customer Success Manager.

When to use this job

The Multiple choice to essay with Feedback Aide rubric conversion job is particularly effective when you need to:

  • Enhance assessment depth: Transition from selected-response to constructed-response formats to better measure learner understanding.
  • Reduce guessing: Adapt existing content for high-stakes environments where evidence of reasoning is a primary requirement.
  • Scale content transformation: Rapidly generate essay-format Items from large legacy banks reducing manual authoring effort.

Running the job

  • Open the Learnosity Console and navigate to Item Bank Health Check.
  • Create a Collection or select an existing one with the Item bank you want to analyze.
  • Click on Create and select Multiple choice to Essay with Feedback Aide rubric conversion from the menu of available job types.
  • Initiate the process by clicking Run job.

The time required for completion depends on the volume of content within the Collection.

Reviewing results

Following the analysis of the Collection, a review space lists each modified Item.

For each Item, Feedback Aide is enabled by default and the rubric generated can be updated in Edit mode.

You can review and refine the following:

  • Reworded Question stimulus
  • Correct responses: the original MCQ answers
  • Alternate responses: shows lexically diverse phrasings of the correct answer for grading flexibility.
  • Incorrect responses: provides original distractors to serve as calibration guidance for evaluators.

We recommend reviewing results to ensure the AI suggestions match your organization’s specific terminology and pedagogical style.

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