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From One-Size-Fits-All to Just-for-Me: Adaptive Learning Algorithms Transforming Blended Education
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Author(s): Uddalak Mitra (JIS College of Engineering, India), Shafiq Ul Rehman (Kingdom University, Bahrain), Barnik Podder (JIS College of Engineering, India), Arpan Dutta (JIS College of Engineering, India)and Bishtu Bhowmick (JIS College of Engineering, India)
Copyright: 2026
Pages: 32
Source title:
Reshaping Blended Learning Environments With AI
Source Author(s)/Editor(s): Shafiq Ul Rehman (Kingdom University, Bahrain)
DOI: 10.4018/979-8-3373-3815-6.ch005
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Abstract
Blended learning has emerged as a transformative educational model, combining digital instruction with conventional classrooms. As academic institutions try to answer the growing demands of learners, AI-aided adaptive learning algorithms become a tool for personalized data-driven means of instruction. The chapter explores adaptive learning algorithms for maximizing effective use of a blended learning environment by tailoring content, pace, and feedback to a student's needs in real-time. Focusing on the architecture and application of adaptive systems in various learning platforms, and discusses how AI models evolve continually on the basis of learner's behavior and performance. Real-world case studies provide the chapter with examples of adaptive algorithms in practice across disciplines. Issues of ethics like data privacy and biases in algorithmic decisions, are also critically analyzed. This chapter extends promising recommendations to instructors, instructional designers, and policymakers to develop an inclusive, efficient, and effective mode of AI-based education.
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