Beyond Standalone Systems: Creating an Ecosystem of Adaptive Training Services

This paper explores the development and initial evaluation of SoarTech Adaptive Training Services (STATS), a modular ecosystem designed to address the limitations of traditional intelligent tutoring systems (ITSs). By adopting Gibbon's [1] layers theory, STATS introduces a structured, service-based approach to ITSs, enhancing adaptability and efficiency. The paper reviews the historical evolution of ITSs, highlighting the shift from rigid architectures to modular, flexible designs. Through a case study in a basic electricity and electronics course, STATS demonstrates its practical application and potential for personalized learning. Initial feedback from 18 participants indicates positive responses towards the system's effectiveness and efficiency, though usability challenges, particularly in interface navigation, were identified. The study underscores the importance of modular design in overcoming the historical challenges of ITS development, such as domain dependence and siloed research. Future directions for STATS include deeper exploration of its layers, exploration of ethical considerations in algorithmic decision-making, and the exploration of interoperability as STATS leverages services from other frameworks like GIFT [2]. This research contributes to the ongoing discourse on the necessity of modular, adaptable ITSs in meeting diverse educational needs and advancing the field of intelligent tutoring.