ADAPTIVE AI-DRIVEN PLATFORMS FOR PERSONALIZED ONLINE ENGINEERING EDUCATION: A CONCEPTUAL FRAMEWORK FOR SKILL DEVELOPMENT

Abstract

This paper proposes a conceptual framework for adaptive AI-driven platforms in online engineering education, designed to support personalized, skill-oriented, and experiential learning. Conventional online learning models often struggle to accommodate differences in learners’ prior knowledge, learning pace, and the practice-intensive nature of engineering education. Addressing these limitations, the study adopts a theoretical and integrative approach that synthesizes insights from online learning research, artificial intelligence in education, and engineering pedagogy to develop an adaptive framework comprising five AI-enabled components: a Learning Analytics Module (LAM), Adaptive Content Delivery System (ACDS), Intelligent Tutoring System (ITS), Virtual Lab and Simulation Integration (VLSI), and Personalized Assessment Engine (PAE). The proposed framework enables continuous learner profiling, adaptive instructional sequencing, context-sensitive tutoring, simulation-based experiential learning, and personalized assessment through analytics-driven feedback loops. By explicitly linking conceptual understanding, hands-on practice, and skills assessment, the framework addresses key challenges in online engineering education, including feedback latency, learner disengagement, and high dropout rates. The paper also discusses implementation considerations related to data privacy, ethical AI, infrastructure requirements, faculty readiness, and system integration. The novelty of this work lies in its engineering-specific adaptive architecture, which extends general Artificial Intelligence in Education (AIED) models by integrating cognitive, experiential, and application-oriented learning dimensions, and provides a foundation for future empirical validation in higher education contexts.