AI-Driven Personalized Learning: Revolutionizing Student Engagement and Teacher Efficiency in Education 5.0

dc.contributor.authorTariq, Muhammad Usman
dc.date.accessioned2025-06-30T12:04:52Z
dc.date.available2025-06-30T12:04:52Z
dc.date.issued2025
dc.description.abstractThe revolutionary potential of AI-driven personalized learning to improve teacher effectiveness and student engagement is examined in this chapter. By customizing content to each learners unique learning needs and preferences AI technologies like intelligent tutoring platforms and adaptive learning systems are revolutionizing educational experiences. By letting students advance at their own speed and addressing their weaknesses and strengths personalized learning creates a more stimulating and productive learning environment. The importance of artificial intelligence (AI) in boosting student motivation is examined in this chapter using interactive tools such as gamification augmented and virtual reality apps and real-time feedback systems. The chapter discusses how AI affects student learning as well as how it can help teachers become more efficient by automating repetitive administrative duties like attendance and grading. Teachers can concentrate more on professional development teamwork and instruction by freeing up valuable time. Keywords: AI, Student Engagement, Education, Learning
dc.identifier.citationTariq, M. U. (2025). AI-Driven Personalized Learning: Revolutionizing Student Engagement and Teacher Efficiency in Education 5.0. In AI and Emerging Technologies for Emergency Response and Smart Cities (pp. 75-96). IGI Global Scientific Publishing.
dc.identifier.doihttps://doi.org/10.4018/979-8-3693-9770-1.ch004
dc.identifier.urihttps://repository.adu.ac.ae/handle/1/7126
dc.language.isoen
dc.publisherIGI Global
dc.titleAI-Driven Personalized Learning: Revolutionizing Student Engagement and Teacher Efficiency in Education 5.0
dc.typeBook chapter

Files

License bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description: