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Related Concept Videos

Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Decision Making01:20

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
Purposive Learning01:22

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a bonus...
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Cognitive Learning

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Related Experiment Videos

Leveraging Traditional and Generative Artificial Intelligence for Programmatic Decision Making in Faculty

Gayle D Haischer-Rollo1, Jessica T Servey, Bizualem Zelelew

  • 1Dr. Haischer-Rollo: Assistant dean for faculty development, associate professor, Department of Pediatrics, School of Medicine, Uniformed Services University, Bethesda, MD. Dr. Servey: Associate dean for faculty affairs, professor, Department of Family Medicine, School of Medicine, Uniformed Services University, Bethesda, MD. Ms. Zelelew: Data analyst II, Office of Faculty Affairs, School of Medicine, Uniformed Services University, Bethesda, MD. Dr. McFate: Director of faculty affairs, assistant professor, Department of Family Medicine, School of Medicine, Uniformed Services University, Bethesda, MD. Ms. Chi: Education coordinator II, Office of Faculty Affairs, School of Medicine, Uniformed Services University, Bethesda, MD. Dr. Duncan: Assistant dean for assessment, assistant professor, Department of Preventive Medicine and Biostatistics, School of Medicine, Uniformed Services University, Bethesda, MD.

The Journal of Continuing Education in the Health Professions
|June 10, 2026
PubMed
Summary
This summary is machine-generated.

The AI-enhanced program evaluation (AIEPE) framework streamlines medical education assessment using AI for faster, deeper insights. This approach transforms data analysis, enabling better decision-making and continuous program improvement.

Keywords:
artificial intelligencelarge language modelsprogram evaluation

Related Experiment Videos

Area of Science:

  • Medical Education
  • Artificial Intelligence
  • Program Evaluation

Background:

  • Traditional program evaluation in medical education struggles with large datasets and manual analysis, hindering timely decisions.
  • The artificial intelligence (AI)-enhanced program evaluation (AIEPE) framework is introduced to overcome these limitations.
  • AIEPE integrates traditional and generative AI for improved efficiency and insight in program assessment.

Purpose of the Study:

  • To introduce and describe the AI-enhanced program evaluation (AIEPE) framework.
  • To demonstrate how AIEPE can augment human evaluators in medical education program assessment.
  • To highlight the potential of AI in transforming program evaluation processes.

Main Methods:

  • The AIEPE framework employs a five-phase, iterative process: Multi-Modal Data Aggregation, Dual-Stream AI-Powered Analysis (traditional and generative AI), AI-Powered Strategic Formulation, Human-in-the-Loop Decision-Making, and Programmatic Implementation and Iteration.
  • Traditional AI performs sentiment and thematic analysis, while generative AI synthesizes narrative summaries.
  • The framework integrates human oversight throughout the evaluation process.

Main Results:

  • Application of AIEPE to a faculty development program evaluation analyzed 3361 qualitative comments.
  • Sentiment analysis showed 87.6% positive comments, and thematic analysis identified key topics.
  • The dual-stream approach revealed that faculty unpreparedness for AI contributed to a high no-show rate in an AI course, leading to a curriculum redesign recommendation.

Conclusions:

  • The AIEPE framework revolutionizes program evaluation, shifting it from a labor-intensive task to a driver of continuous improvement.
  • It empowers academic leaders to transition from data collection to data-informed strategy development.
  • While enhancing efficiency and insight, the framework underscores the necessity of human oversight to manage potential biases and over-reliance on technology.