Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education
Project Overview
The document explores the development and implementation of an Artificial Intelligence-Enabled Intelligent Assistant (AIIA) designed to enhance personalized and adaptive learning in higher education. By leveraging advanced AI and Natural Language Processing (NLP), the AIIA creates an interactive learning environment that minimizes cognitive load and provides tailored support by integrating seamlessly with Learning Management Systems (LMS). Key applications of the AIIA include understanding student inquiries, generating quizzes, and offering individualized learning pathways, all aimed at improving student engagement and learning outcomes. Additionally, the document addresses various challenges and limitations associated with the use of AI in education, highlighting the need for ongoing development and adaptation of these technologies to effectively meet the diverse needs of learners. Overall, the findings suggest that generative AI has significant potential to transform educational experiences by providing personalized support and fostering a more engaging learning atmosphere, while also acknowledging the complexities involved in its successful implementation.
Key Applications
AI-Enabled Intelligent Assistant (AIIA)
Context: Higher education, targeting students and instructors
Implementation: Integrated with LMS and built using a NodeJS backend leveraging AI and NLP techniques.
Outcomes: Enhanced learning experiences, increased engagement, personalized support, and improved learning outcomes.
Challenges: Technical limitations in data extraction from PDFs, integration with LMSs, and ensuring academic integrity.
Implementation Barriers
Technical Barrier
Handling of unstructured data from PDF files and integration challenges with LMS platforms.
Proposed Solutions: Develop custom libraries for LMS integration and explore OCR technologies for PDFs.
Ethical Barrier
Concerns regarding academic integrity and potential for cheating with AI tools.
Proposed Solutions: Incorporate mechanisms to prevent cheating and promote academic integrity within the system.
Project Team
Ramteja Sajja
Researcher
Yusuf Sermet
Researcher
Muhammed Cikmaz
Researcher
David Cwiertny
Researcher
Ibrahim Demir
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Ramteja Sajja, Yusuf Sermet, Muhammed Cikmaz, David Cwiertny, Ibrahim Demir
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai