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

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Authors

Ramteja Sajja, Yusuf Sermet, Ibrahim Demir, Vinay Pursnani

Abstract

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 was conducted through two primary methods including text similarity analysis to assess the correctness of generated answers and a survey with university staff to gather feedback on the tool's effectiveness. Results indicate a high level of accuracy in response generation and positive feedback from educators, highlighting the tool's potential to enhance vocational training for FPM certification. 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.

DOI

https://doi.org/10.31223/X5WD91

Subjects

Education, Engineering

Keywords

Artificial Intelligence, Educational Technology, Personalized Learning, Vocational Training, Adaptive Learning, Professional Certification

Dates

Published: 2024-11-26 18:44

Last Updated: 2024-11-27 02:44

License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None

Data Availability (Reason not available):
Data is shared in the paper.