AI-Assisted Item Grouping and Test Blueprint Development

A blog by Vishwanath Subbanna, Principal Pre-sales Consultant, Excelsoft Technologies 

Test blueprinting is a robust process in the assessment lifecycle that facilitates efficient item grouping and ensures the creation of high-quality, well-balanced tests. It enables you to systematically organize the items based on characteristics such as similarity, meta-data, domain parameters, factors derived from item analytics, and others. This streamlines selecting the appropriate questions and determining the optimal number of questions when creating tests.

A few characteristics used in organizing the items:

• Similarity: Similarity involves categorizing items that evaluate the same knowledge or skill using comparable content or formats. For instance, it organizes all multiple-choice-single-response, multiple-choice-multiple-response, and fill-in-the-blank items on a particular topic.
• Meta-data: Organize items based on additional information about them, such as their author, creation date, geography, language, competency, skill, learning outcomes, and learning objectives.
• Subject/domain parameters: Categorize items by the specific subject area or domain they cover (e.g., Math, Science, History).
• Item psychometrics: Group items based on item analysis data such as:
  • The difficulty index indicates how difficult an item is for average-ability test-takers.
  • The discrimination index shows how well an item differentiates between high-performing and low-performing test-takers.
  • The guessing factor estimates the likelihood of a test-taker getting an item correct by chance alone.
  • The exposure parameters track the frequency with which an item has been used in past tests to prevent overuse and maintain test security.
  • The aging parameters analyze information currency and remove outdated questions from the pool presented to the test-takers.

Test and Item administrators can leverage item grouping to:

• Construct item groups: Organize the items logically using the various grouping methods.
• Develop test blueprints: Define a test’s desired structure and content by specifying various item filters.

Leveraging AI for constructing ability-based, well-balanced, and effective test blueprints:

By generating a set of filters, AI can significantly improve test quality by reducing the manual effort required from SMEs. SMEs can focus more on refining and fine-tuning the AI-generated test blueprints to ensure they precisely align with the desired test objectives and outcomes.