AI-Assisted Educational Framework for Floodplain Manager Certification: Enhancing Vocational Education and Training Through Personalized Learning

Details

• Journal Title:
  IEEE Access
• Personal Author:
  Sajja, Ramteja ; Pursnani, Vinay ; Sermet, Yusuf ; Demir, Ibrahim
• NOAA Program & Office:
  NWS (National Weather Service) ; CIROH (Cooperative Institute for Research to Operations in Hydrology)
• Description:
  Floodplain management is critical for mitigating flood risks and safeguarding communities. The FloodPlain Manager (FPM) certification is essential for professionals in this field, but current preparation methods often fall short in providing comprehensive, accessible, and engaging study resources. This research introduces a novel AI-assisted educational tool designed specifically for FPM certification preparation and training process. Leveraging advanced natural language processing and machine learning techniques, this tool offers personalized learning experiences, interactive question-and-answer sessions, and real-time feedback to aspiring floodplain managers. The system architecture integrates certification-specific content through a sophisticated document parsing process, ensuring relevance and accuracy. Evaluation of the tool, conducted through text similarity analysis, demonstrates its effectiveness in preparing candidates for the FPM certification exam. With 91.7% accuracy for open-ended questions and 95.12% for multiple-choice questions, the tool offers a personalized learning experience through dynamic flashcards and adaptive quizzes, highlighting its potential to enhance vocational training and exam readiness. This study underscores the transformative role of AI in professional education and suggests future directions for expanding the tool’s capabilities and application to other certifications.
• Source:
  IEEE Access, 13, 42401-42413
• DOI:
  https://doi.org/10.1109/ACCESS.2025.3548591
• ISSN:
  2169-3536
• Format:
  PDF
• Publisher:
  Institute of Electrical and Electronics Engineers (IEEE)
• Document Type:
  Journal Article
• Funding:
  Grant no. NA22NWS4320003
• License:
  https://creativecommons.org/licenses/by/4.0/
• Rights Information:
  CC BY
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:2f4659221996ffb516ad647d9b60c8562f02423b3c3631ce32a980defb2cac5cc916773d8e50b95af44cd533bf34fd7b2cfb380282d283d02bc13811530fc80b
• Download URL:
  https://repository.library.noaa.gov/view/noaa/72804/noaa_72804_DS1.pdf
• File Type:
  [PDF - 3.01 MB ]