International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 11 | Nov 2025
p-ISSN: 2395-0072
www.irjet.net
ARTIFICIAL INTELLIGENCE IN PERSONALIZED LEARNING WITH A FOCUS ON CURRENT DEVELOPMENTS AND FUTURE PROSPECT Jayashree R1, Mr Sathishkumar M2 1 PG Student, Department Of Computer Applications, Jaya College Of Arts and Science, Thiruninravur,
Tamilnadu, India
2Assistant Professor, Department Of Computer Applications, Jaya College Of Arts and Science,
Thiruninravur, Tamilnadu,India ---------------------------------------------------------------------***--------------------------------------------------------------------instructional materials based on learners’ progress, while AI Abstract - The integration of Artificial Intelligence (AI) in
chatbots can offer instant academic assistance and emotional support.
personalized learning has transformed traditional educational paradigms by enabling adaptive, data-driven, and studentcentered learning experiences. Recent advancements in AI technologies—such as machine learning algorithms, natural language processing, and predictive analytics—have facilitated the development of intelligent tutoring systems, adaptive learning platforms, and personalized feedback mechanisms. These tools analyze learners’ behaviors, preferences, and performance to tailor instructional content and pacing, thus enhancing engagement and learning outcomes. Current developments include the use of generative AI for content creation, chatbots for real-time support, and learning analytics for early intervention and performance prediction.
2.LITERATURE REVIEW Artificial intelligence (AI) in personalized learning reframes instruction from one-size-fits-all to adaptive, learnercentered pathways that tailor content, pacing, feedback, and assessment to individual needs. Modern AI-driven personalization is built around a stack of capabilities learner modeling, adaptive sequencing/pedagogical policies, automated content and feedback generation, and dashboards for teachers and students which together aim to increase engagement, accelerate mastery, and support differentiated instruction at scale. Recent syntheses highlight how this stack has matured from rule-based systems to probabilistic and deep learning approaches that operate on rich interaction data. Intelligent Tutoring Systems (ITS) remain a foundational class of interventions for personalized learning. ITSs provide step-by-step tutoring, worked examples, hinting strategies, and adaptive task sequencing; systematic reviews and meta-analyses over the past decade report consistent short-term gains in performance, especially in well-structured domains like mathematics, though effect sizes and generalizability vary by implementation quality and study design. The literature emphasizes that ITS effectiveness often depends on careful modeling of skills, high-quality domain models, and the fidelity of classroom implementation.
Key Words: Artificial Intelligence, Personalized Learning, Adaptive Learning, Educational Technology, Future of Education
1.INTRODUCTION The rapid advancement of Artificial Intelligence (AI) has revolutionized numerous sectors, and education is no exception. In recent years, AI has emerged as a transformative force in reshaping teaching and learning processes, particularly through the development of personalized learning systems. Personalized learning refers to instructional approaches that tailor educational experiences to meet the unique needs, abilities, interests, and learning styles of individual students. Unlike traditional one-size-fits-all models, AI-driven personalized learning leverages algorithms and data analytics to adapt content, pace, and assessment in real time, ensuring that each learner receives the most suitable support and challenge. AI technologies—such as machine learning, natural language processing, predictive analytics, and recommender systems—are being increasingly integrated into learning management systems and digital educational platforms. These technologies enable continuous monitoring of learner performance, identification of learning gaps, and generation of adaptive feedback, thus promoting self-directed and efficient learning. For instance, intelligent tutoring systems and adaptive e-learning platforms can automatically adjust
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3. METHODOLOGY 3.1 Existing System The Existing system of personalized learning primarily relies on traditional digital learning platforms and rule-based adaptive systems. These systems use predefined algorithms and fixed learning paths designed by educators or domain experts. The personalization process in this framework is limited to analyzing learners’ test scores, time spent on activities, or basic demographic data to adjust the difficulty level or content sequence. Intelligent Tutoring Systems (ITS) and Learning Management Systems (LMS) with adaptive features represent the main technological foundation of the current approach. These systems apply basic artificial
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