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.

Despite this potential, many existing online learning management systems were not originally designed with explainable AI in mind, resulting in persistent gaps in fraud detection accuracy that limit their overall effectiveness. This study examines how Explainable AI can be applied to help close that gap.

1.2 Statement of the Problem

Current online learning management systems in many organizations struggle with inadequate fraud detection accuracy, often relying on manual processes or outdated architectures that were not designed for today's operating environment. Without a structured approach to integrating explainable AI, these limitations are likely to persist, exposing organizations to inefficiency, risk, and a poor user experience. This study is motivated by the need to design and evaluate a explainable AI-based approach to addressing this problem.

1.3 Objectives of the Study

1. To design and implement a explainable AI-based approach to improving fraud detection accuracy in online learning management systems.
2. To evaluate the effectiveness of Explainable AI in enhancing fraud detection accuracy 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 fraud detection accuracy in online learning management systems?
2. How effective is Explainable AI at enhancing fraud detection accuracy 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

Beyond its immediate technical contribution, this study offers value to organizations evaluating whether to invest in explainable AI for their own online learning management systems, and contributes to the broader literature on applied software technology / IT by documenting a concrete implementation and evaluation case.

1.6 Scope of the Study

As a BSc-level study, its scope is confined to designing and evaluating a explainable AI-based solution for online learning management systems, focused specifically on fraud detection accuracy; broader deployment considerations fall outside this scope.