Enhancing professional development and training through AI for personalized learning: a framework to engaging learners / Zoel-Fazlee Omar ... [et al.]

• Main Authors: Omar, Zoel-Fazlee, Mior Harun, Mior Harris, Mohd Ishar, Nor Irvoni, Mustapha, Nur Arfah, Ismail, Zurina
• Format: Article
• Language: English
• Published: UiTM Press 2024
• Subjects: Computers in education. Information technology; Back propagation (Artificial intelligence)
• Online Access: https://ir.uitm.edu.my/id/eprint/97200/1/97200.pdf; https://ir.uitm.edu.my/id/eprint/97200/

This paper explores the transformative potential of AI-driven personalized learning in enhancing professional development and training programs. As the workforce landscape rapidly changes, the demand for tailored and engaging learning experiences has become increasingly evident. This paper presents a comprehensive framework that leverages artificial intelligence (AI) to determine suitable learning theories and strategies for individual learners, thus promoting higher learner engagement and skill acquisition. Through analysing learner data, preferences, and performance, AI algorithms enable the customization of training content, delivery methods, and assessment strategies. This study builds a design space with three axes to situate and position AI-Driven Personalized Learning in the larger research field of professional development and training. This paper presents a comprehensive review of existing literature using the PRISMA systematic review methodology to explore the transformative potential of AI-driven personalized learning on professional development and training. The analysis showcases the potential of AI-driven personalized learning to revolutionize the learning process, catering to individual learner needs, preferences, and pace. By embracing this framework, organizations can foster a culture of continuous learning, empowering professionals to thrive in a dynamic and evolving professional landscape.