Dissertation
AI-driven virtual teaching assistants for enhanced learning: a framework for personalized support, analytics, and scalability in higher education
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Summer 2025
DOI: 10.25820/etd.008104
Abstract
This dissertation presents the design, development, and evaluation of the Educational AI Hub, a novel framework that leverages artificial intelligence to support adaptive, accessible, and personalized learning in higher education. As educational institutions face increasing challenges in delivering scalable support to diverse student populations, the Educational AI Hub offers a responsive solution that addresses the limitations of traditional teaching methods through real-time, context-aware, and emotionally intelligent interactions.
The Educational AI Hub integrates state-of-the-art natural language processing, ontology-based knowledge modeling, sentiment analysis, and learning analytics into a modular system that can be deployed across disciplines and platforms. Its core component, the Virtual Teaching Assistant (VTA), is capable of handling student queries, generating study aids, adapting to emotional cues, and providing personalized learning pathways. The system also includes an instructor-facing analytics dashboard designed to assist in identifying at-risk students, tracking engagement, and informing data-driven instructional decisions.
To assess its effectiveness, the VTA system was evaluated through a combination of technical performance metrics and real-world classroom deployments in multiple academic domains. The evaluation results demonstrate that the VTA enhances student engagement, reduces instructor workload, and contributes to a more inclusive and interactive learning environment. Instructor feedback highlighted the system’s potential to streamline repetitive communication, while students reported high levels of satisfaction with its accessibility and relevance to course material.
This research contributes to the field of educational technology by offering a scalable, ethically aware, and learner-centered AI framework. It addresses both cognitive and affective aspects of learning, paving the way for future innovations in AI-powered educational systems. The dissertation concludes with a discussion of practical implementation insights, policy implications, and future directions for extending VTAs across institutional and disciplinary boundaries.
Details
- Title: Subtitle
- AI-driven virtual teaching assistants for enhanced learning: a framework for personalized support, analytics, and scalability in higher education
- Creators
- Ramteja Sajja
- Contributors
- Ibrahim Demir (Advisor)Yusuf Sermet (Advisor)Guadalupe Canahuate (Committee Member)Tyler Bell (Committee Member)David Cwiertny (Committee Member)
- Resource Type
- Dissertation
- Degree Awarded
- Doctor of Philosophy (PhD), University of Iowa
- Degree in
- Electrical and Computer Engineering
- Date degree season
- Summer 2025
- DOI
- 10.25820/etd.008104
- Publisher
- University of Iowa
- Number of pages
- xv, 232 pages
- Copyright
- Copyright 2025 Ramteja Sajja
- Language
- English
- Date submitted
- 07/03/2025
- Description illustrations
- Illustrations, graphs, charts, tables
- Description bibliographic
- Includes bibliographical references (pages 190-226).
- Public Abstract (ETD)
This dissertation explores how artificial intelligence (AI) can be used to make learning more personal, accessible, and effective in colleges and universities. As classrooms grow larger and more diverse, many students struggle to get the support they need, and instructors face challenges in answering every question or keeping up with individual student progress. To help address these issues, this research introduces the Educational AI Hub, an AI-powered platform that includes a virtual teaching assistant capable of answering questions, providing study materials, and even detecting when a student might be confused or stressed.
This intelligent assistant is available anytime and can be used on phones, computers, or even smart speakers. It helps students learn at their own pace, find course-related information, and get quick answers without waiting for office hours or email replies. For instructors, the Educational AI Hub offers useful insights into how students are doing and what topics may need more attention, helping teachers make timely and informed decisions.
To test how well this system works, it was used in real college classes and evaluated based on student feedback, instructor observations, and how accurately it handled course content. Results showed that students found it helpful and easy to use, while instructors appreciated the support it offered in managing classroom communication.
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984948540202771
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