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Using Generative AI for Personalised and Interactive Learning: A Framework for Integrating Large Language Models (LLMs) in Higher Education

  • Omama Khan*
  • , Izhar Ahmad
  • , Sikandar Khan
  • , Gulzar Alam
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

As education evolves, generative AI and large language models (LLMs) provide essential tools for enhancing student engagement and instructor effectiveness. This paper explores the opportunities and challenges of leveraging LLMs for personalized and interactive learning in higher education environment. It examines the capability of LLMs to provide tailored educational content, engage in conversational interactions, and generate adaptive assessments. It also considers ethical concerns, bias mitigation, and privacy considerations surrounding the use of LLMs in education. In addition, it explores the potential pedagogical implications, including the impact on student engagement, accessibility, personalized feedback, and instructors support. The proposed framework aims to guide institutions in achieving educational benefits while managing the associated challenges.

Original languageEnglish
Title of host publication11th International Conference on Engineering and Emerging Technologies, ICEET 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798331567552
DOIs
StatePublished - 2025
Event11th International Conference on Engineering and Emerging Technologies, ICEET 2025 - Kuala Lumpur, Malaysia
Duration: 22 Oct 202523 Oct 2025

Conference

Conference11th International Conference on Engineering and Emerging Technologies, ICEET 2025
Country/TerritoryMalaysia
CityKuala Lumpur
Period22/10/2523/10/25

Bibliographical note

Publisher Copyright:
© 2025 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 4 - Quality Education
    SDG 4 Quality Education

Keywords

  • Education
  • Generative AI
  • Interactive and personalised learning
  • LLMs
  • Large language models
  • Pedagogies

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Instrumentation
  • Engineering (miscellaneous)
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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