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<p>This paper evaluates programmatic assessment (PA) as an educationally coherent framework for medical education, addressing longstanding paradoxes in traditional high stakes models where siloed performance data may fail to capture true learner competence. Programmatic assessment is a comprehensive, programme level strategy that uses multiple low stakes data points over time to inform high stakes progression decisions. While traditional summative assessments provide standardised, objective thresholds essential for national licensing, a growing body of longitudinal evidence indicates that the little and often approach of PA promotes deeper learning and improved retention of clinical skills.</p><p>In an era of disruption from Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs), this paper explores how PA can be operationalised to enhance the validity of assessment decisions. We outline the core tenets of the model and resolve practical tensions, such as the balance between psychometrics and human judgement. We also introduce the concept of performance resilience, explaining how continuous feedback prepares students for high stakes clinical crises and national exams. Finally, we identify common implementation traps and offer ten practical tips, grounded in change management theory, to guide realistic institutional adoption. We conclude that programmatic assessment enhances learning, supports equity, and strengthens public trust in medical education.</p>

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Programmatic Assessment: Tenets to Remember, Tensions to Resolve, Traps to Avoid, and Tips to Take on Board

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月23日

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  • Alison M Sturrock,
  • Rakesh Patel

摘要

This paper evaluates programmatic assessment (PA) as an educationally coherent framework for medical education, addressing longstanding paradoxes in traditional high stakes models where siloed performance data may fail to capture true learner competence. Programmatic assessment is a comprehensive, programme level strategy that uses multiple low stakes data points over time to inform high stakes progression decisions. While traditional summative assessments provide standardised, objective thresholds essential for national licensing, a growing body of longitudinal evidence indicates that the little and often approach of PA promotes deeper learning and improved retention of clinical skills.

In an era of disruption from Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs), this paper explores how PA can be operationalised to enhance the validity of assessment decisions. We outline the core tenets of the model and resolve practical tensions, such as the balance between psychometrics and human judgement. We also introduce the concept of performance resilience, explaining how continuous feedback prepares students for high stakes clinical crises and national exams. Finally, we identify common implementation traps and offer ten practical tips, grounded in change management theory, to guide realistic institutional adoption. We conclude that programmatic assessment enhances learning, supports equity, and strengthens public trust in medical education.