The global expansion of distance learning has given rise to urgent demands for dynamic, engaging, and future-ready STEM curricula. This paper explores how Artificial Intelligence (AI), when applied through adaptive learning systems and data-informed instructional design, can reshape the delivery and impact of online STEM education. By exploiting AI for real-time analytics, personalised content recommendations, and predictive learner support, we propose a framework for building more responsive and inclusive learning environments that enhance student engagement, retention, and academic performance.

This research builds on recent empirical studies, case analyses, and data simulations to develop an adaptive AI model using core artificial intelligence tools. Central to the model are supervised learning models for performance prediction, natural language processing (NLP) engines for feedback interpretation, and rule-based systems for content recommendation.

These core AI components collectively drive the development of the model by enabling three foundational capabilities in STEM curriculum transformation:
(1) delivering differentiated learning pathways based on dynamic learner profiles and engagement patterns;
(2) providing intelligent, real-time feedback using NLP to interpret student inputs and learning behaviour; and
(3) applying predictive analytics to identify at-risk students and trigger personalised support interventions.

The model is designed for seamless integration into existing learning management systems (LMS) and is supported by visual analytics dashboards that enable educators to make data-informed instructional decisions and monitor learner progress continuously.

Initial results from simulated datasets designed to mirror real-world online learning conditions using variables such as student demographics, VLE activity, assessment history, and learner sentiment suggest that adaptive AI systems can significantly outperform traditional online delivery methods. The simulations demonstrated retention rates exceeding 90%, a 15% improvement in average grades, and 78% of students reporting positive learning experiences in terms of engagement and satisfaction. These figures reflect the capacity of adaptive systems to deliver personalised interventions and improve learner outcomes at scale. These AI-enabled components demonstrate potential for improving educational outcomes at scale, fostering individualised instruction, and making STEM education more accessible.

In addition, this paper evaluates important practical and ethical considerations, including data privacy, the potential for algorithmic bias, infrastructural limitations, and the readiness of educational institutions to adapt. By employing adaptive AI strategies in curriculum design, we aim to support the development of learning environments that are both streamlined and impactful.

Keywords: Adaptive Learning Systems, AI in Education, STEM Curriculum Design, Learning Management Systems (LMS), Natural Language Processing (NLP), Predictive Learning Analytics.