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

IF 3.6 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Access Pub Date : 2025-03-05 DOI:10.1109/ACCESS.2025.3548591

Ramteja Sajja;Vinay Pursnani;Yusuf Sermet;Ibrahim Demir

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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, 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.

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人工智能辅助洪泛区管理者认证教育框架：通过个性化学习加强职业教育和培训

洪泛区管理对于减轻洪水风险和保护社区至关重要。洪泛区经理（FPM）认证对于该领域的专业人员来说是必不可少的，但目前的准备方法往往无法提供全面、可获取和吸引人的学习资源。本研究介绍了一种专门为FPM认证准备和培训过程设计的新型人工智能辅助教育工具。利用先进的自然语言处理和机器学习技术，该工具为有抱负的洪泛平原管理者提供个性化的学习体验、互动问答环节和实时反馈。系统架构通过复杂的文档解析过程集成了特定于认证的内容，确保了相关性和准确性。通过文本相似度分析对该工具进行评估，证明了它在为FPM认证考试的候选人做准备方面的有效性。开放式问题的准确率为91.7%，多项选择题的准确率为95.12%，该工具通过动态抽认卡和自适应测验提供个性化的学习体验，突出了其加强职业培训和考试准备的潜力。这项研究强调了人工智能在专业教育中的变革性作用，并提出了将该工具的功能和应用扩展到其他认证的未来方向。

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来源期刊

IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

9.80

自引率

7.70%

发文量

6673

审稿时长

6 weeks

期刊介绍： IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest. IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on: Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals. Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering. Development of new or improved fabrication or manufacturing techniques. Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.