End-to-End Deployment of the Educational AI Hub for Personalized Learning and Engagement: A Case Study on Environmental Science Education

Ramteja Sajja; Yusuf Sermet; Ibrahim Demir

doi:10.1109/ACCESS.2025.3554222

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End-to-End Deployment of the Educational AI Hub for Personalized Learning and Engagement: A Case Study on Environmental Science Education

Journal article

Open access

Peer reviewed

End-to-End Deployment of the Educational AI Hub for Personalized Learning and Engagement: A Case Study on Environmental Science Education

Ramteja Sajja, Yusuf Sermet and Ibrahim Demir

IEEE access, Vol.13, pp.55169-55186

01/01/2025

DOI: 10.1109/ACCESS.2025.3554222

Appears in UI Libraries Support Open Access

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https://doi.org/10.1109/ACCESS.2025.3554222View

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Abstract

This study introduces an end-to-end framework for deploying conversational AI-enabled educational assistants, focusing on personalized support for students across diverse subject areas, including Business, Culture, Environmental Sciences, History, Politics, and Science, as outlined in our evaluation framework. The system leverages advanced conversational AI technologies to provide targeted, course-specific learning experiences by facilitating access to complex data and integrating seamlessly with Learning Management Systems (LMS) like Canvas. Key metrics—information retrieval accuracy, question-answering accuracy, and hallucination accuracy—were selected to rigorously evaluate the system’s ability to retrieve relevant contexts, generate accurate responses, and identify unanswerable questions. Additionally, the Educational AI Hub agents utilize innovative document parsing methods, such as the Nougat technique, to interpret content accurately, enabling adaptable academic support tailored to individual learning needs and extending to quantitative fields through code execution capabilities. This study also emphasizes the importance of accessibility, inclusivity, and user privacy. The results showcase the potential for enhanced engagement and improved understanding of environmental concepts and software tools, demonstrating the significant impact of conversational AI in educational settings, especially in disciplines involving complex data interactions. A case study, presented at the 12th International Congress on Environmental Modelling and Software, illustrates the Educational AI Hub’s effectiveness in enhancing student engagement and delivering personalized learning experiences in environmental sciences education.

Education

Water Quality

Education

Artificial intelligence

Adaptive learning

Virtual assistants

Accuracy

Electronic learning

Water quality

Learning management systems

Large language models

Decision making

Artificial intelligence (AI)

document parsing techniques

generative pre-training transformer (GPT)

large language models (LLM)

natural language processing (NLP)

personalized learning

UIOWA OA Agreement

Details

Title: Subtitle: End-to-End Deployment of the Educational AI Hub for Personalized Learning and Engagement: A Case Study on Environmental Science Education
Creators: Ramteja Sajja - University of Iowa
Yusuf Sermet - University of Iowa
Ibrahim Demir - University of New Orleans
Resource Type: Journal article
Publication Details: IEEE access, Vol.13, pp.55169-55186
DOI: 10.1109/ACCESS.2025.3554222
ISSN: 2169-3536
eISSN: 2169-3536
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Number of pages: 18
Grant note: University of Iowa
This work was supported by the University of Iowa's Innovations in Teaching with Technology Awards.
Language: English
Date published: 01/01/2025
Academic Unit: Electrical and Computer Engineering; Civil and Environmental Engineering; IIHR--Hydroscience and Engineering; Injury Prevention Research Center
Record Identifier: 9984803811902771

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