The Common Vein

• Summarized and adapted from article
  • 1 Reid,  Janet R. Precision Education for Personalized Learning
• The creation of adaptive learning systems
  • via AI
  • to  personalize the learning experience.
• Requires the knowledge base
  • ie the curriculum (at least) to be
    • presented in a variety of
    • different ways
    •  and for the system to determine
    • the individuals strength and weaknesses
    • and adapt the presentation (“adaptive learning” -1)
    • and then delivers the material according to the individuals needs
    • This is the basis for “precision education” (1) which contains these 3 elements
      • a knowledge repository,
        • housed in a learning management system (LMS),
          • highly indexed, repository
            searchable database
          • responsive to the residents  actions.
      • user interface (UI),
        • has machine learning to
        • adapt to the user needs and a
      • recommender algorithm (RA).
• Personalized learning is an
  • educational approach that
  • tailors instruction to meet the
  • individual needs, abilities, and learning styles
• The goal is to create a more customized and effective learning experience, acknowledging that students have
  • diverse strengths,
  • weaknesses,
  • interests, and
  • paces of learning.
• Individualized Pace:
  • students progress through the curriculum
  • at their own pace.
  • advanced students to move more quickly an
  • provides additional support for those who need more time to grasp a concept.
• Differentiated Instruction:
  • to accommodate different learning styles and preferences
    • multimedia resources,
    • hands-on activities, or
    • collaborative projects.
• Flexible Content:
  • Curriculum and learning materials are
  • adapted to match students’ interests,
  • abilities, and
  • prior knowledge.
  • This can involve adjusting the
    • complexity of the content or providing alternative resources to cater to diverse needs.
• Student Choice
  • Students have some autonomy in
  • selecting topics or projects based on their interests. This can increase engagement and motivation, as students are more likely to be invested in their learning when it aligns with their passions.
• Data-Driven Instruction:
  • Continuous
    • assessment and
    • feedback
      • help inform the adaptation of teaching methods.
    • AI use data to understand individual student progress and make real-time adjustments to support their learning needs.
• Technology Integration:
  • use of technology to
  • facilitate adaptive learning platforms,
  • online resources, and
  • interactive tools.
  • enabling residents to access materials
  • at their own pace and
  • provides teachers with data on individual progress.
• Collaborative Learning:
  • personalized learning emphasizes individual needs, but there is a need for  exclude collaborative opportunities.
• Teacher as Facilitator:
  • teachers take on the role
  • to guide and and support residents r
• Goal Setting:
  • residents can be involved with  setting
  • their own learning goals and provides a .
    • sense of responsibility and
    • ownership over their education.
• Continuous Assessment:
  • Curriculum is set
  • WF and Image First Follows the curriculum

?a system [that] can adapt
itself when providing learning support
to different learners to defeat the
weakness of one-size-fits-all approaches
in technology-enabled learning systems?

Links and References

1 Reid,  Janet R. Precision Education for Personalized Learning

•  developed RADIAL
  (Radiology?s Intelligent Adaptive
  Learning),
  • “LMS (Learning Management System)
  • connected to the PACS [8].
  • A second?plug-in,? Intelligent Tutor,
  • usesDICOM data to select and display
    contextual educational resources from
    RADIAL
    . As people tend t