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Related Experiment Videos

Development and effectiveness verification of AI education data sets based on constructivist learning principles for

Seul-Ki Kim1, Tae-Young Kim2, Kwihoon Kim3

  • 1Department of Computer education, Korea National University of Education, Chungju, Chungbuk, Republic of Korea. tmfrlska85@gmail.com.

Scientific Reports
|March 29, 2025
PubMed
Summary

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This summary is machine-generated.

This study developed new AI education datasets grounded in constructivism, enhancing students' AI literacy by connecting learning to real-world experiences. These datasets offer a valuable alternative to traditional materials for effective AI education.

Area of Science:

  • Artificial Intelligence Education
  • Computer Science Education
  • Educational Technology

Background:

  • Traditional AI education often lacks contextual relevance for students.
  • Existing datasets may not adequately support constructivist learning principles.
  • There is a need for AI educational resources that foster deep AI literacy.

Purpose of the Study:

  • To develop and evaluate constructivist-oriented AI education datasets.
  • To enhance students' AI literacy through real-world problem-solving.
  • To create sustainable and accessible AI educational resources.

Main Methods:

  • Reconstructed the machine learning dataset development cycle.
  • Developed and refined AI datasets through expert panel interviews.
Keywords:
Artificial intelligence educationAuthentic activityConstructivismContextDatasets

Related Experiment Videos

  • Deployed datasets on educational platforms and analyzed usage metrics.
  • Conducted comparative analysis of AI literacy impact.

    • Main Results:

    • Developed four novel AI education datasets suitable for replacing conventional ones like the Iris dataset.
    • Confirmed high accessibility and utility on major Korean AI education platforms.
    • Demonstrated effectiveness in enhancing AI literacy through practical application.

    Conclusions:

    • Constructivist AI datasets effectively connect prior knowledge with real-world experiences.
    • These datasets deepen understanding of AI model learning processes.
    • They provide authentic, data-driven computing experiences crucial for AI literacy.