AI‐Driven Personalized Learning

AI-driven personalized learning holds immense potential to revolutionize instruction and make more equitable and fair learning situations. This chapter investigates how artificial intelligence can be utilized to tailor instructive encounters to the special needs of each learner, subsequently tending to systemic disparities that have verifiably impeded certain understudy populations. We look at the capabilities of AI in regions such as versatile appraisals, personalized substance conveyance, and clever coaching frameworks, highlighting their potential to distinguish learning holes, focus on back, and cater to assorted learning styles. Moreover, we examine the basic considerations encompassing the moral suggestions of AI in instruction, counting issues of inclination, information security, and algorithmic transparency. By tending to these challenges proactively, we contend that AI can be an effective apparatus for advancing social equity in instruction, engaging all learners to reach their full potential, notwithstanding their foundation or circumstances. This investigation emphasizes the need for cautious planning, usage, and assessment of AI-driven personalized learning frameworks to guarantee they contribute to a more even-handed and comprehensive instructive scene.