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Programmatic assessment for learning: A programmatically designed assessment for the purpose of learning: AMEE Guide

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Programmatic assessment for learning (PAL) uses structured data collection to enhance learning. This guide clarifies PAL terminology, learning processes, validity, and future AI integration in assessment.

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Area of Science:

  • Educational Assessment
  • Learning Sciences

Background:

  • Programmatic assessment for learning (PAL) is an evolving educational approach.
  • It involves the structured collection of assessment data specifically for learning enhancement.

Purpose of the Study:

  • To provide recommendations on key aspects of PAL.
  • To clarify terminology and outline guiding learning processes.
  • To examine validity and future directions, including AI's role.

Main Methods:

  • Review of the evolution of programmatic assessment.
  • Outline of learning processes (distributed learning, interleaving, overlearning, test-enhanced learning).
  • Analysis of validity from a programmatic perspective and future AI opportunities.

Main Results:

  • Clarification of PAL terminology and its historical development.
  • Identification of key learning processes that inform PAL design.
  • Insights into the evolving nature of validity and AI's impact on assessment.

Conclusions:

  • PAL offers a structured framework for leveraging assessment data to improve learning.
  • Understanding learning processes and validity is crucial for effective PAL implementation.
  • Artificial intelligence presents significant opportunities and challenges for the future of assessment.