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Enhancing Student Engagement Through AI-driven Analytics in Higher Education Institutions

EasyChair Preprint no. 12209

11 pagesDate: February 20, 2024


Institutions of higher education are increasingly leveraging AI-driven analytics to enhance student engagement. This paper explores the implementation of such technologies and their impact on student engagement in higher education settings. By harnessing data analytics, AI algorithms can provide insights into student behavior, preferences, and learning patterns. These insights enable educators to tailor instructional strategies, interventions, and support services to meet individual student needs effectively. Additionally, AI-driven analytics empower students by providing personalized learning experiences and timely feedback, fostering greater motivation and engagement. However, challenges such as data privacy concerns and algorithmic biases must be addressed to maximize the benefits of AI-driven analytics in higher education. This paper concludes with recommendations for institutions seeking to integrate AI-driven analytics into their educational practices to enhance student engagement and success.

Keyphrases: AI-driven analytics, Data Analytics, educational technology, higher education, personalized learning, student engagement

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Asad Ali},
  title = {Enhancing Student Engagement Through AI-driven Analytics in Higher Education Institutions},
  howpublished = {EasyChair Preprint no. 12209},

  year = {EasyChair, 2024}}
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