Chapter One — 1.1 Background to the Study

Explainable AI has become one of the more actively explored innovations in the design of modern online learning management systems, promising gains in efficiency and reliability that legacy, largely manual approaches have struggled to deliver.

In practice, however, adoption of explainable AI within online learning management systems has been uneven, and its actual impact on system performance is not yet well understood in a rigorous, evaluable way — a gap this study is positioned to address.

1.2 Statement of the Problem

Existing approaches to system performance within online learning management systems remain largely reactive and fragmented, with little systematic use of explainable AI despite its demonstrated value elsewhere. This study addresses the resulting gap by designing and evaluating a solution built specifically around explainable AI.

1.3 Objectives of the Study

1. To design and implement a explainable AI-based approach to improving system performance in online learning management systems.
2. To evaluate the effectiveness of Explainable AI in enhancing system performance within online learning management systems.
3. To identify the key requirements and constraints relevant to deploying explainable AI in this context.
4. To assess user and stakeholder perception of the resulting system.

1.4 Research Questions

1. How can explainable AI be applied to improve system performance in online learning management systems?
2. How effective is Explainable AI at enhancing system performance within online learning management systems?
3. What requirements and constraints are relevant to deploying explainable AI in this context?
4. How do users and stakeholders perceive the resulting system?

1.5 Significance of the Study

This study is significant to software developers and system architects seeking practical guidance on applying Explainable AI within online learning management systems. It is equally relevant to organizations that rely on these systems, offering a reference point for evaluating whether such an investment is justified, and it adds to the growing body of work on explainable AI applications in software technology / IT.

1.6 Scope of the Study

The study is limited to the design, implementation, and evaluation of a explainable AI-based approach to improving system performance within online learning management systems. Reflecting its PhD-level scope, it does not extend to a full commercial rollout or long-term post-implementation review beyond the study period.