In comprehensive details, present the struture of the following dissertation report (including approximate word count per session) in a way that someone else writing a dissertation on another artefact can use this structure.

Abstract
The growing use and integration of technology in educational facilities have enhanced their susceptibility to cyberattacks, hence the need for advanced protective measures. This study aims at developing and testing an AI-based chatbot with a focus on diagnosing and addressing cybersecurity threats in learning institutions. This research utilizes the design science research approach to develop a tool that utilizes natural language processing and decision-making algorithms to afford timely and accurate risk evaluations. Discussing cybersecurity strategies, the literature review emphasizes the importance of the adaptability and further demonstrates how AI-based technology can be beneficial for education while it is still predominantly used in fields like finance. 

When designing the system, the concept of modularity is used in conjunction with the principles of user-centered design, which guarantees the reliability and usability of the chatbot. In testing, the chatbot performed well in terms of correctly identifying and answering questions related to typical cybersecurity situations and being engaging and interactive, while shortcomings in dealing with APTs and fine tuning of response time were observed. Usability testing provided evidence that the interface was easy-to-use but also revealed that there is potential to optimize the available features even further based on the needs of participants. 

These limitation highlight the need to address ethical concerns specifically privacy and data protection when developing the chatbot. Halal-compliant products and solutions thus enhance user confidence and adherence to best practices within the prescribed legal frameworks. Thus, the dissertation draws a conclusion about the discussed shifts in the field, arguing for the continuous evolution of the chatbot and considering the potential for its expansion with new technologies. 

Overall, this research significantly enhances the conversation on AI’s utility in cybersecurity, providing both goal-oriented findings and the potential for a more limited but sustainable approach relevant to the specific situations in educational institutions. Thus, when achieving its objectives, the project creates a favorable starting point for further developments and additions of applications based on AI cybersecurity tools in educational and other less-represented areas.


Table of Contents
Abstract	1
Chapter 1: Introduction	3
Chapter 2: Literature Review	5
Chapter 3: Methodology	9
Chapter 4: System Design	14
1.Performance:	24
2.Scalability:	24
3.Usability:	24
4.Reliability:	24
5.Maintainability:	24
6.Compatibility:	24
7.Data Privacy and Compliance:	24
Chapter 5: Results	26
Chapter 6: Discussion and Conclusion	31
References	34




Chapter 1: Introduction
1.1 Background and Context
The advancement of digital technology has made cybersecurity threats surge in the educational institutions as they deal with information that is similar to the financial type (Familoni and Shoetan 2024). With the increase in the use of different media technology in the operation of these institutions,training and exposure to more complex cyber threats such as phishing, ransomware attacks, and data breach have increased. Translation AI and Cybersecurity is an engaging topic that identifies the potentials and risks of AI as systems like chatbots start assuming significant responsibility for threats’ identification and prevention (Asif et al. , 2024).

1.2 Problem Statement
AI can support and enhance cybersecurity measures, whereas many educational organizations have significant problems with the lack of resources and qualified staff to implement proper cybersecurity. This positions them in a disadvantageous angle relative to other industries that have better resource endowment such as the finance industry which already use AI in threat identification and mitigation (Familoni and Shoetan, 2024). There is a pressing requirement for a more straightforward and approachable application that can enhance strong cybersecurity within such scholars. In Pallathadka, et al., (2024) provide a critique of the limitations of modern conversational AI technologies in combating the complex and ever-evolving nature of cybersecurity threats targeting educational ecosystems.

1.3 Aims and Objectives
The goal of this dissertation is to build an intelligent conversational chatbot for evaluating cybersecurity risks with a focus on educational organizations. With support of generative AI capabilities, the chatbot will not only improve cybersecurity within such institutions but also contribute towards developing models that can be applied to assess risk in different environments. The core objectives include:
a. Creating an engaging conversational structure 
b. Incorporating NLP and decision-making functions.
c. Testing the application of the chatbot in practice-based methods for education to determine its efficiency and ease of use (Koukou & Stavrou, 2024).
1.4 Novelty and Contribution
There is a stronger focus on the actual implementation of AI chatbots is that educational cybersecurity is a less explored area in comparison to the finance and healthcare industries (Pinaki, 2023). Beyond the security concerns, this project may serve as an example of how the AI-assisted dialogue systems can incorporate cybersecurity measures to not only meet its objectives but also to contribute to the ongoing AI ethical debate, with a focus on user consent and data protection (Asif et al. , 2024).

1.5 Dissertation Structure Overview
This dissertation moves forward with a Literature Review that assesses the state of the art of AI in cybersecurity in different industries and recognizes the lack of research in the educational domain. After that, the Methodology chapter provides a logical framework for the creation and evaluation of the cybersecurity chatbot with references to the main ethical concerns mentioned in the literature (Vaish et al. , 2024). The System Design describes the architectural layout and the incorporation of various AI solutions. The Results section summarizes the outcomes of the initial implementations, and the Discussion and Conclusion sections discuss the findings, study limitations, and further research avenues.

This way, this research is both positioned at the intersection of AI and cybersecurity, aiming not only to contribute to a safer digital education environment but also to write a new chapter in the discussion on the role of AI in the digital landscape of the contemporary society (Costa and Coelho, 2024).

Chapter 2: Literature Review
2.1 Introduction
The increasing reliance on digital platforms in educational institutions has made cybersecurity a critical area of focus. The integration of AI, particularly chatbots, into cybersecurity strategies presents new opportunities and challenges. This literature review explores the current state of cybersecurity in educational settings, the role of AI-driven solutions, the challenges they present, and the ethical considerations involved. It evaluates existing research to highlight gaps and suggest areas for future investigation.

2.2 Current State of Cybersecurity in Educational Institutions
Educational institutions remain among the most vulnerable to cyber attacks because of their exposed networks and the large quantities of easily identifiable data: people’s personal data or other records and ideas, including patents and copyrights (Familoni and Shoetan, 2024). While the financial industry has relatively strong protections in place, educational organizations might be less secure. Pallathadka et al. (2024) mention that there are still many institutions that lag behind in terms of cyber hygiene meaning that they remain at risk from simple threats like phishing and ransomware attacks.

However, as indicated in Asif et al. (2024), the cybersecurity introduced in educational environments is mainly more of a post-incident measure than a preventative one. This reactive security model is insufficient since threat environments are continually changing and a proactive strategy is needed. The checks and balances provided by existing structures failed to consider structural and budgetary problems in education and hence requires new structures that addresses the needs of the entities.

2.3 Role of AI and Chatbots in Cybersecurity
Comprehensive findings indicate that chatbots powered by AI might offer the best solution for bolstering the levels of cybersecurity in schools (Koukou and Stavrou, 2024). Such tools can help identify threats and manage risks independently, which frees up the work of IT departments and enhances security. Organizing and questioning capabilities of the chatbots makes it possible for the users of the chatbots to dominate the systems without necessarily being conversant with programming (Shafi & Gahangir, 2024).

Nevertheless, the usage of AI chatbots in educational cybersecurity is not yet as advanced compared to sectors such as finance and healthcare which have incorporated such technologies (Pinaki, 2023). In their study, Pallathadka et al. (2024) noted that even though, chatbots if implemented well can revolutionalise security frameworks, however, there is a dearth of holistic research about their efficacy and drawbacks in learning contexts. Such difficulties highlight the importance of focused research on the effectiveness and feasibility of using AI chatbots in these contexts.

2.4 Challenges and Limitations of AI-driven Solutions
Even though the use of AI has so much to offer, its incorporation into cybersecurity frameworks has its drawbacks. They include: Oversight - There is the tendency for people to rely too much on these systems hence compromising the oversight role (Abdelhamid et al. , 2024). Also, AI systems such as chatbots are also susceptible to adversarial attacks whereby the input data can be altered to elicit wrong conclusions or answers (Costa and Coelho, 2024).

Other challenges that can be regarded as ethical issues deal with the protection of user information (Camacho, 2024). AI systems frequently presuppose access to large quantities of data in order to operate efficiently, which is why matters of data security and user permission become questionable. In their respective articles, Giordani 2024 identifies that such AI applications as chatbots can potentially breach data privacy laws, hence the necessity for sound ethical standards and legal compliance into the use of AI.

2.5 Cybersecurity Frameworks and AI Integration
The implementation of AI alongside the existing cybersecurity frameworks in educational institutions’ has been improvisational or in other words, unimplemented systematically (Mahmood et al. , 2024). However, the use of AI can complement traditional approaches and their successful implementation depends on the awareness of both AI and the field where it is applied. Familoni and Shoetan (2024) have highlighted the need to align AI approaches with the risk management frameworks that are typical for educational organisations while differing from the ones typical for many business-related organisations.

Akula (no date) captures the possibility of complementing AI in Risk management frameworks to enable real-time detection and addressing of threats. However, there should be careful incorporation of the two such that the AI escalation does not complicate the processes that are already in place.

2.6 Ethical Considerations in AI-powered Cybersecurity
Every aspect of using AI needs to be governed by ethical standards no matter what field is one is implementing the system in. It is in this regard that questions about consent, clear disclosure and data protection remain pertinent and critical in educational settings (Asif et al. , 2024). Vaish et al. (no date) also stress on the importance of trust in the context of adoption of AI technologies calling for institutionalization of ethical norms of usage that will safeguard the rights of the users.

Additionally, AI may recreate prejudices that exist in the training set data and result in the unequal application of security measures or in erroneous alarms that will reduce the confidence of users in the system as well as the productivity of the system (Kapoor, 2024). Those need to be addressed constantly and subjected to checking and changing of the AI models to ensure that they are bias free and work for all kinds of people.

2.7 Emerging Trends and Future Directions
The current advancement in technology will determine the future of AI in educational cybersecurity. There are new advanced AI models that are characterized by deep learning and generative AI, that provide better threat detection and prevention mechanisms (Hasan et al. , 2024). These models can identify and counteract threats in a faster and more precise manner, given that they are incorporated well into systems.

Continued improvements in AI are recommended by Bhardwaj et al. (2024) such that it complements human users instead of replacing them. Also, as AI continues to develop and gain more and more prominence in society, there is a need to expand on the ethical measures of these technologies’ deployment and operation, making sure that they are employed not only properly but also fairly (Sonko & De Sotto, 2024).

2.8 Conclusion of Literature Review
The analysis of the literature highlights the necessity to improve cybersecurity efforts in educational setting with the applications of AI and chatbots. But this change from possibility to feasibility needs multifaceted research and development at the same time assessing spot needs and realities that characterize the education sector. As technology advances and grows more complex, so does the approach that should be taken to protect academic settings. This continuous advancement highlights the need to engage in more research and discussions dealing with the use of AI in cybersecurity and in ways that are most effective and ethical.

Existential risks to education institutions remain prominent, and this review highlights the need for specific RD investments to fill these gaps and properly deploy AI technologies to improve the security of educational settings (Mingo, 2024). Extending from the current exposition and concerns, subsequent efforts accomplish the direction of enhancing the current state of cybersecurity in academic settings that hold value for learners, instructors, and university systems.

Chapter 3: Methodology
3.1 Introduction
Here, the strategic approach that has been adopted in the development of the AI-Chatbot for the screening and management of cybersecurity threats in education facilities is described. This chapter covers the established research methodology, data collection methods, system design, implementation procedure, and evaluation metrics hence analyzing reasons behind the choice of methods used. The overall goal of this project is to design an easy-to-use and effective solution, using the conversational AI agents to solve the cybersecurity issues specific to education context (Koukou and Stavrou, 2024).

3.2 Research Design
This position adopts a design science research (DSR) framework to inform the-design and assessment of the chatbot. The concept of design science is especially effective in designing and developing IT artifacts, especially when it comes to addressing practical problems by means of new and effective designs and rigorous testing (Pinaki, 2023). The cyclic nature which is inherent to DSR guarantees the enhancement of the chatbot, the process of its extolment fully complies with the needs of educational institutions in the sphere of cybersecurity. It helps in the implementation of an AI chatbot that will help in closing the gap between what the current technology offers and what is needed in the education sector.

3.3 Data Collection
In this study, data collection played a central role in ensuring that development of the chatbot was informed by the actual environments in educational organisations. The qualitative data was gathered through face-to-face interviews and surveys while the quantitative data was based on questionnaires and questionnaires on the current state of cybersecurity and the possibility of the use of AI tools in this area.

Pre-survey, a comprehensive literature review was conducted to provide a frame of reference for technological and contextual frictions encountered by educational institutions (Familoni & Shoetan, 2024). The review shed light on relevant cybersecurity threats and revealed the loopholes in contemporary protection strategies that assert the necessity of an AI-centered approach (Mahmood et al. , 2024).

Semi-structured interviews were used to obtain qualitative data from IT professionals, educators, and cybersecurity specialists who work or interact with educational organizations. These conversations were aimed at identifying and discussing existing cybersecurity measures, potential threats, and possible roles of the chatbot. The collected data specified the chatbot’s functional functionalities and conversational pattern, which must allow it to focus on meeting the most crucial needs that the participants presented (Akpan et al. , 2024).
3.4 System Design
The system design integrates ideas with the architecture of a modular and scalable system that incorporates AI technologies into the interface. It is a cyclical process where the feedback can be integrated at any time to improve the functionality of the chatbot.

3.4.1 Architectural Framework
In order to develop the chatbot, a three-tier structure was used: presentation tier, application tier, and data tier. This modular design helps in easy maintenance and scalability, which is crucial for coping with emerging threats in cybersecurity (Ayanwale and Ndlovu, 2024). The presentation layer is the GUI – the graphical user interface – layer that is easily understandable and user friendly. The system divides different components into an application layer that encompasses the Natural Language Processing engine and the decision-making algorithms, and the data layer that deals with data acquisition and processing.

3.4.2 Use Case and User Requirements
Use cases derived from data collection that provided the basis for the design were supported by detailed descriptions of various use cases defining typical operating environments for the chatbot. These scenarios informed how functional and non-functional requirements were defined, including risk assessment, timely response, and security of data. Akula (no date) has noted that it is important to correlate such requirements with other frameworks for REM to have broad and efficient coverage.

3.5 Implementation
The implementation phase mapped the design specifications into a working system with the help of modern AI tools and programming frameworks which are used for building conversational agents.

3.5.1 Natural Language Processing
The NLP component of the chatbot was handled in a hybrid fashion that consists of rule-based and learning schemes, which include transformers such as GPT-2. This way, the system can accommodate multiple queries at once without compromising its ability to be adaptable to further additions and threats (Hasan et al. , 2024). Advanced models improve NLP, allowing the chatbot to respond effectively to inquiries and solve problems.

3.5.2 Decision-Making Algorithms
Decision-making is the essence of the chatbot’s operation, and based on decision trees and fuzzy logic to handle situations where information is either missing or unclear (Mingo, 2024). These algorithms are further evolved through data obtained from real-life cybersecurity conditions, thereby helping the chatbot to provide specific recommendations regarding situations exclusive to the learning environment.

3.5.3 GPT-2 on HuggingFace
GGPT-2 was chosen for this project based on its degree of performance, ease of use, and affordability. GPT-2 stands out as very proficient, although not the largest model, offering accurate natural language generation without excessive requirements in terms of computational power compared to GPT-3. This makes it particularly useful to implement in environments where resources are scarce. Further, GPT-2 has rich documentation and multiple pre-trained models that are easily accessible in the Open Source environment such as Hugging Face. Hugging Face was chosen for its simplicity and the availability of models for deployment and an option to deploy the model and check its performance in the web environment. This gave confidence in such aspects as compatibility with existing development tools employed in this project as well as support for widely used frameworks such as PyTorch.

3.6 Evaluation
Testing consequently included function and usability testing to ascertain if this chatbot meets this stated goals and provides top-quality Consumer Experience.

3.6.1 Functional Testing
Based on the use case analysis, functional testing was performed with a set of standard test cases. These tests confirmed that the system offered dependable fundamental features like risk evaluation, recommendation generalizations, and stability of performance under concurrent users (Pinaki, 2023).

3.6.2 Usability Testing
The usability testing of the chatbot involved participants from the target group, for instance, educators and cybersecurity personnel, to engage with the chatbot in simulated environments. The qualitative feedback relied on measures such as usability, comprehensiveness of the answers provided as well as overall satisfaction. The collected insights helped to adjust the user interface and conversational flow to the user’s expectation and ability (Joon and Simmons, 2024).
3.7 Ethical Considerations
Ethical practices are an inherent part of AI solutions’ creation and deployment, especially in learning contexts where concerns about data protection and users’ permission are essential.

3.7.1 Data Privacy
This chatbot was designed with an emphasis on the Privacy by Design, that is, the data privacy consideration is integrated into its functionality. All data management processes are encrypted and user data is safe from unauthorized access due to anonymity (Camacho, 2024). Periodic scrutiny and sweeps ensure that these measures are continually valid and conform to the necessary norms.

3.7.2 Informed Consent
All the test participants provided written informed consent for the study’s purpose and use of their data (Asif et al., 2024). Measures were put in place to ensure that participants were fully informed of their rights and that the contributions made were voluntary.

3.8 Conclusion of Methodology
The methodology employed in this dissertation underscores a commitment to developing an AI chatbot capable of addressing the unique cybersecurity challenges faced by educational institutions. By adopting a design science framework, this study combines rigorous development processes with critical evaluation, ensuring that the resulting tool is both functional and user-friendly. The project integrates contemporary AI advancements with ethical considerations, laying the groundwork for future research and development in educational cybersecurity. This approach aligns with the broader objectives outlined, providing a foundation for ongoing innovation and refinement in this critical field.


Chapter 4: System Design
4.1 Introduction
The system design phase is important to ensure that the cybersecurity risk assessment is not only well protected and resistant to attacks, but also easy to use. In this chapter, details concerning the architectural design, design principles, and technical building blocks of the chatbot are discussed, along with the rationale for the decisions with reference to the goals of increasing protective measures in educational contexts. This way, this project aims to develop and offer an innovative yet functional tool by incorporating AI technologies in a user-oriented process (Koukou and Stavrou, 2024).

4.2 Architectural Framework
The architecture of the chatbot was made modular, which positively affected scalability and maintainability, and flexibility, all necessary in the context of continuous evolution of threats and risks in the field of cybersecurity. The functionality of each component is highly individual, enabling developers to update and work through changes without rearranging the whole construction plan (Pallathadka et al. , 2024).

4.2.1 Three-Tier Architecture
The use of the three-tier architecture containing the presentation layer, the application layer, and the data layer helps to keep system functionality and security concerns separated from each other, which is important for managing overall performance (Familoni & Shoetan, 2024).

Presentation Layer: This layer includes the user interface, which has been created using easily understandable web-based technologies. Its design is clean and straightforward, which is a reaction to the fact that both technical experts and novices must be able to effectively navigate the application (Akpan et al. , 2024).
Application Layer: The format and other program-related data are located in the application layer that contains the basic functions of the chatbot, including the NLP engine and decision-making models. This layer handles the user input and also maintains the conversation capabilities of the chatbot, made possible by Python’s extensive libraries for machine learning (Hasan et al. , 2024).
Data Layer: This layer deals with data storage and retrieval process to ensure that user engagements and risk evaluations are well contained. Higher levels of encryption are used in order to secure data, enhancing the user rights and security of the systems (Camacho, 2024).

4.2.2 API Integration
To improve its functionality and compatibility, the architecture of the chatbot takes advantage of API calls so that it can communicate effectively with other cybersecurity applications, services, and databases. This integration facilitates the access of real-time threat intelligence for the chatbot, enhancing the relevance and accuracy of risk assessments (Asif et al. , 2024).

4.2.3 GPT2 and Transformers
GPT-2 is a large scale language model released by OpenAI and based on the transformer architecture. Transformers are deep learning models used on data that is ordered and therefore perfect for handling language data. Essentially, unlike normal RNNs or LSTMs, transformers use a concept known as self-attention, which means they can grade the significance of each word in a sentence regardless of the position of the words. This architecture enables GPT-2 to produce grammatically-sound and contextually relevant text by analyzing the connection between all words in the input sequence at once. GPT-2 operates based on being pre-trained on millions of text examples which enable it to create human-like responses to a very broad range of prompts. Due to its capability of creating text swiftly, Nd4t is often employed for text completion tasks, summarization, and chatbot application, among others.

4.3 User Interface Design
One significant aspect that has been identified as critical to the design of the system was the user interfaces. This process involved the adoption of the user-centered design approach that seeks to address the needs and preferences of users (Vaish et al. , no date).

4.3.1 User-Centered Design Principles
The user interface was also redesigned based on results from usability testing; there were several versions before the final version that was smoothed out to improve both form and function (Joon and Simmons, 2024). Key principles included:

Simplicity: The interface was made As simple as possible, with readable formats and navigation and no unnecessary steps to be taken when using the chatbot (Bhardwaj et al. , 2024).
Accessibility: Other elements of usability such as screen reader support and customizable text size were also incorporated to ensure the chatbot functionality is available for individuals with different level of disabilities & preferences (Akpan et al. , 2024).

4.3.2 Multi-Device Compatibility
Due to the large range of devices in use within education facilities, UI is fully developed to be fluid and adjust to the size of the screen and its orientations. This makes it possible to have standard features which will make the application be operates and be friendly for users on the desktops, tablets, and smartphones respectively (Pinaki, 2023).

4.4 Natural Language Processing (NLP) Engine
The chatbot has the capacity to take and answer user queries and this is made possible by the NLP engine. It uses a mix of rule-based systems and machine learning algorithms to allow it more flexibility given its high precision.

4.4.1 Hybrid NLP Approach
This way, the chatbot can handle virtually any type of question and adjust to the ever-changing input from the client (Hasan et al. , 2024). This approach categorizes the chatbot to be able to understand advanced and multidimensional queries as well as give appropriate responses (Akula, no date).

4.4.2 Continuous Learning and Adaptation
As in modern AI, the NLP engine is developed to learn from interactions and update the understanding and the ability to give accurate responses. This self-improving mechanism is especially important for keeping the chatbot relevant under changing conditions and for its ability to continue meeting the user’s requirements in the future (Costa and Coelho, 2024).

4.4.3 Embedding and Fine-Tuning
In natural language processing, embedding and fine-tuning are two separate approaches used to adapt language models. Unless a task is otherwise specified, an embedding is an encoding of text that preserves semantic similarity, which can be used in tasks such as semantic search, or as input into other models. In contrast, tuning is used to further refine certain aspect of a learned model by fine-tuning to a specific dataset in order to enhance performance on a particular task. Although fine-tuning may produce a model that can effectively solve one problem, it is a more complex process that takes more time, money, and advanced skills to accomplish. On the other hand, the embeddings approach is easier, less computationally intensive, and has a lower risk of overfitting which makes it a suitable approach for a feature like semantic search in a chatbot. All of these factors mean that the most suitable method of embedding was selected for this project as it is effective, easy to implement and within reasonable resources available.
4.5 Decision-Making Algorithms
The personalized recommendations are possible due to the decision-making process of the chatbot wherein the inputs are evaluated, and the most suitable risk handling is suggested.

4.5.1 Decision Trees and Fuzzy Logic
The second approach, fuzzy logic, allows the system to work with uncertain or incomplete data, which is common in cybersecurity situations (Mingo, 2024). This way of assessment and making recommendations are particularly valuable when it comes to the security challenges that always arise in education and learning (Pallathadka et al. , 2024).

4.5.2 Integration with Real-Time Data
The decision-making process is enhanced by the fact that it is possible to input real-time threat intelligence through APIs. This RTDI aids the chatbot in demonstrating current and appropriate security advice based on the latest threat landscapes (Asif et al. , 2024).

4.6 Security Considerations
Security was a primary concern while designing the system and several methods were incorporated to safeguard the user data as well as the entire system.

4.6.1 Data Encryption and Secure Development
In both transit and while stored in its database, user data is encrypted with secure algorithms, preventing interception by unauthorized parties. Moreover, the system was initiated based on basic principles for writing secure code, thus reducing the chance of threats and enhancing the overall security (Camacho, 2024).

4.6.2 Privacy by Design
In line with ethical guidelines, the system was designed with privacy at its core, ensuring user data is handled responsibly and transparently (Asif et al., 2024). This privacy-centric approach aligns with the broader ethical considerations inherent in deploying AI technologies in educational environments (Joon and Simmons, 2024).

4.7 UML Diagrams
4.7.1 Sequence Diagram

The provided Sequence Diagram captures the message flow within a system with different parts responsible for handling user queries using an advanced AI model. The diagram commences from the “Streamlit Frontend,” which is the user interface layer and starts from the user making a query. This query is then handed over to the “Embedding Model,” which transforms the query into a numerical structure or vector for processing. The embedding is then passed to the “Semantic Search” module, which then attempts to search the “Knowledge Base” using the given embedding. This module obtains the required information and appends it to the prior query before posting it to the “GPT-2 Model.

The “GPT-2 Model” takes the distilled knowledge and the query and produces a response which is then tokenized by the “Tokenizer” component for output. Lastly, this response is dispatched to the user through the ‘Streamlit Frontend’, with the ‘Session State’ being altered in order to track the session’s history, in turn maintaining context between the different interactions. This sequence diagram appropriately captures the flow of data and control between the different components of a module, and lays bare the modularity and interaction intelligence of the system in processing natural language questions and formulating the competent answers.

4.7.2 Block Diagram

The block diagram depicts how the chatbot delivers cybersecurity information. The system follows the architecture where the frontend is built with Streamlit and serves as the interface. Users enter search queries, and these search queries are passed through an embedding model known as Sentence Transformer. It also shows that the embedding model transform the text to numerical form that can be processed further.

Then semantic search (knowledge retrieval) is executed. This includes looking for the information relating to cyber security in a knowledge base and then identifying the results of the searching process that occurred because of the embedded user inputs. The obtained information is then forwarded to a GPT-2 model that synthesizes a detailed and substantive answer. Globally, the input and output text is divided into tokens to ease processing by the GPT-2 model, with a tokenizer being employed to carry out the process. Finally, a session state is maintained to manage the history of the conversation, allowing the chatbot to provide contextually relevant responses.

4.7.3 Use-Case Model


The above figure shows a use case diagram of Cybersecurity Risk Assessment System that depicts how users and administrators in interacting with the system. The basic user interactions include querying, viewing answers and managing the session history. When a user poses a knowledge question, the action of getting the particular knowledge is also part of the search process that informs an appropriate answer. This way, users get proper and relevant feedback based on what the system has learned implementing the feedback loop. The action of maintaining session history is prolonged, which underlines its significant importance in improving usability because it allows preserving context during interactions.

For administrators, the diagram shows two key responsibilities: co-ordinating the knowledge base management and the scrutiny of the MIS. It remains the responsibility of administrators to maintain the accuracy, currency, and comprehensiveness of the knowledge base, which affects the depth and quality of responses generated by the system. Also, the communication has to monitor performance so that the administrators can recognize problems as they occur, so the system can stay in optimal condition. With this diagram, the basic workflows of the cybersecurity risk assessment system can be clearly presented, and the differences between users and administrators, as well as their communication, can be illustrated.

4.8 Chatbot Implementation
The chatbot for this project is meant to help the users answer cybersecurity questions by offering contextually appropriate responses. The chatbot architecture comprises two primary components: a GPT-2 model for text generation and a Sentence Transformer model for semantic search.


Upon receiving a query, the chatbot first converts it into numerical representation using the Sentence Transformer model. It is then matched with a pre-existing knowledge database that is relevant to a number of cybersecurity subtopics. As it can be seen from the figure above, applying semantic search, the chatbot determines the entry in the knowledge base that corresponds to the query entered by the user.

When the appropriate data is searched, it is properly integrated with the user’s input, and the result is allowed to run through GPT-2. This combined input makes the response generated by GPT-2 not only relevant to the user query, but also factually accurate. Then the GPT-2 model computes a stylistically and semantically appropriate answer that is presented to the user.





This system design allows the chatbot to leverage the strengths of both the transformer architecture for text generation and semantic search for information retrieval, ensuring that the responses provided are not only fluent but also accurate and informative.

4.9 Requirements
4.9.1 Functional Requirements
In order to accomplish the objective and aim of this research work, The Cybersecurity chatbot requires a robust implementation that must include the following functionality:

1.User Query Handling: The chatbot should accept text input from users via the Streamlit interface.
It should support natural language queries related to cybersecurity risk assessment.
2.Embedding Generation: The system should generate semantic embeddings for user queries using a pre-trained Sentence Transformer model (e.g., all-MiniLM-L6-v2).
3.Knowledge Retrieval: The chatbot should perform a semantic search on the knowledge base to find relevant information based on the user's query. It should return the top n most relevant knowledge entries.
4.Response Generation: The chatbot should use a GPT-2 model to generate contextually appropriate responses based on user queries and retrieved knowledge.
5.Conversation History Maintenance: The system should maintain a session history to track user queries and chatbot responses during a single session.
6.Response Display: The chatbot should display responses to the user in the Streamlit interface.
The system should allow users to view and scroll through previous interactions.
7.Error Handling: The system should handle and provide meaningful error messages for unsupported queries or system errors (e.g., model loading failures).
8.Security and Privacy: The chatbot should ensure the confidentiality of user data and interactions.
Implement basic security measures to prevent unauthorized access to the backend systems.

4.9.2 Non-Functional Requirements
The proposed solution should have the capability and capacity to deliver the non-functional requirements listed below:
1.Performance: The chatbot should generate a response within 2-3 seconds on average to ensure smooth user experience. The system should be able to handle multiple concurrent users without significant degradation in response time.
2.Scalability: The system should be able to scale horizontally to handle an increasing number of users and queries.The backend infrastructure should support adding more computational resources to handle more complex models or larger knowledge bases.
3.Usability: The user interface should be intuitive and easy to use, with clear instructions for interacting with the chatbot. The system should support basic input validation to guide users in formulating effective queries.
4.Reliability: The chatbot should be available 99.9% of the time, with planned downtime for maintenance announced in advance. In case of a failure, the system should have a mechanism to recover gracefully and retain the session history.
5.Security: User data should be encrypted in transit and at rest to prevent unauthorized access.
The system should log all interactions for audit and security purposes, ensuring data privacy.
6.Maintainability: The codebase should be modular and well-documented to facilitate easy updates, debugging, and feature enhancements. The system should support automated deployment and testing to streamline maintenance.
7.Compatibility: The chatbot should be compatible with major web browsers (Chrome, Firefox, Edge, Safari). The system should support integration with other cybersecurity tools or platforms if needed.
8.Data Privacy and Compliance: The system must comply with relevant data privacy regulations (e.g., GDPR, CCPA) regarding the collection, storage, and processing of user data. Ensure that users are informed about how their data is used and provide consent mechanisms where necessary.

The chatbot fit into these non-Functional requirements:
1.Performance:
i.The chatbot should generate a response within 5-10 seconds on average 
2.Scalability:
i.The system should be able to scale horizontally to handle an increasing number of users and queries.
3.Usability:
i.The user interface should be intuitive and easy to use, with clear instructions for interacting with the chatbot.
4.Reliability:
i.The chatbot should be available 99.9% of the time, with planned downtime for maintenance announced in advance.
5.Maintainability:
i.The codebase should be modular and well-documented to facilitate easy updates, debugging, and feature enhancements.
6.Compatibility:
i.The chatbot should be compatible with major web browsers (Chrome, Firefox, Edge, Safari).
7.Data Privacy and Compliance:
i.Ensure that users are informed about how their data is used and provide consent mechanisms where necessary. – We don’t store the data, so this isn’t obtainable.

4.10 Conclusion of System Design
The system design of the cybersecurity risk assessment chatbot reflects a comprehensive approach to addressing the unique challenges of educational settings through innovative AI integration. By balancing technical sophistication with user-centered principles, the design ensures that the chatbot is both effective and accessible. The reflective insights gained through iterative design and evaluation will inform future enhancements, ensuring the system’s continued alignment with the evolving cybersecurity landscape. This thoughtful design process underscores the project’s commitment to delivering a tool that not only enhances security but also exemplifies responsible AI deployment (Familoni and Shoetan, 2024).

Chapter 5: Results

5.1 Introduction
This chapter provides insights into the results attained from the pilot implementation and validation of the cybersecurity risk assessment chatbot. The testing was conducted to evaluate the effectiveness, capabilities, and usability of the system in educational environments. They pointed out positive aspects in design and implementation aspects as well as areas that could be enhanced or made better. Thus, by critically assessing these outcomes, we derive additional information regarding the efficiency and prospects of utilizing various AI-based technologies for securing education space (Koukou and Stavrou, 2024).

5.2 System Performance
Measures of the performance of the system relied on aspects such as probabilities and risk determinations, response time, and stability of the system. These performance measures are significant to make the chatbot effective and feasible for usage in a natural setting such as an education context (Pallathadka et al. , 2024).

5.2.1 Accuracy of Risk Assessments
The test cases comprised a set of predefined risk assessment tests through which the efficacy of the chatbot’s risk assessments was gauged. These cases originated from typical cyber-security contexts typical of educational organizations, namely phishing, unauthorized access, and data leakage (Familoni and Shoetan, 2024). In terms of risk identification and risk control measures, the chatbot proved to be very effective and accurate. For instance, when engaged in a phishing setting, the chatbot pinpointed the potential threat as an email attack and outlined specifically how to avoid such schemes.

However, there were issues when faced with more complicated cases, for example, APT cases, where the opt could be too generalized. Such a limitation may, therefore, necessitate the modification of the decision-making algorithms on top of gathering more training data to improve the accuracy and specificity of the recommendation scenarios (Asif et al. , 2024).

5.2.2 Response Time
A measure of user interaction, this involved determining the time it took for the system to process user inputs and the time taken to provide a result. The response time was less than three seconds in different testing conditions, which is suitable to represent a real-time application (Pinaki, 2023). This is due to the well-designed NLP engine and decision-making algorithms that ensure challenging queries are processed rapidly.

The response time was deemed acceptable (for a GPT2 model); however, variation with a special emphasis on the more elaborate queries was identified, which points to further improvements, especially in terms of data processing and query management (Bhardwaj et al. , 2024).

5.2.3 System Stability
The stress testing was applied to determine the stability of the system and the stress resembled high traffic conditions that are possible in large educational facilities. Nonetheless, the chatbot remained steady throughout these tests and proved that it could effectively manage multiple concurrent messages without dropping the quality of service (Joon and Simmons, 2024).

Despite this, minor defects regarding data retrieval bottlenecks were observed, while, while not causing outright crashes, did sometimes slow replies. These bottle necks will have to addressed in the subsequent versions to allow the system to perform optimally under heavy usage (Costa and Coelho, 2024).

5.3 User Interaction and Usability
The assessment also concerned user engagement and system ease of use, aspects that are paramount to determining the application of the chatbot in learning environments.

5.3.1 User Interface
The IT staff and educators were used to test the usability of the chatbot and the interface and the improvements needed were discussed. The interface was also positively received with the testers praising the simplicity of the layout and how items on the screen interacted with each other (Akpan et al. , 2024). Appreciation of easy-to-use familiar interface elements like clear paths and response inputs also made the user experience more positive.

Nevertheless, users identified a number of improvements to the interface in their feedback, including the incorporation of more customisation options like settings for the choice of themes and text size as suggested by Williams in his work in 2024.

5.3.2 Conversational Flow
The coherence and relevance of the conversational flow, which is one of the main facets of chatbot interactions, was assessed. It was overall coherent and logical, as the OS managed to answer user questions of all types from straightforward fact-based ones to the more complex kinds (Pallathadka et al. , 2024).

Nevertheless, some challenges were noted with ambiguous inputs, where the chatbot occasionally struggled to maintain context, leading to less relevant responses. Enhancing the NLP engine to better handle such inputs is essential for improving the consistency and relevance of interactions (Mingo, 2024).

5.3.3 Clarity of Responses
The tone and language used in the conversation must be clear to allow the user to clearly understand and follow the recommendations that the chatbot gives. Respondents were able to mostly understand the questionnaire responses and their content was deemed to be relevant with reasonable depth. In the given conditions that required such information, the chatbot was able to supply explanations and definitions of technical content and make it easily understandable to laymen (Toussaint et. al, 2024).

However, feedback also highlighted that in some cases, these responses were oversimplified for users who have more technical knowledge, meaning response depth should be relative to users’ experience (Bhardwaj et al. , 2024). After the study, this aspect will be an area of improvement in order to cater for different levels of competence of the users.

5.4 Limitations and Future Improvements
As with any evaluation process, there are several strengths and limitations which have been highlighted below:

5.4.1 Handling Complex Scenarios
One of the major limitations includes the coverage of intricate cyber threat situations like APTs (Sebastian, 2024). This will need to be tackled by fine-tuning the decision making process and also by making sure that the chatbot is trained on vast, updated databases to make the recommendations more specific and accurate (Asif et al.<|End of the citation|>, 2024).

5.4.2 Optimization of Response Times
There is a need to fine tune these response times especially for the intricate search queries to make sure that the system is still engaging. Improving the basic data processing and search engines would help to minimize fluctuations in effectiveness (Costa and Coelho, 2024).

5.4.3 Enhancing Customization and Accessibility
User feedback highlighted the need to expand the availability of customization and accessibility options within the interface. Subsequent versions should incorporate choices to cater to the various target groups and guarantee that users of various kinds are being accommodated (Akpan et al. , 2024).

5.5 Conclusion of Results
This chapter has provided a critical analysis of the initial testing results of the chatbot that will show how effective it is in the assessment of cybersecurity risks and its applicability for other uses. Thus, the outcomes confirm the efficiency of the system in achieving its goals; However, overcoming the limitations mentioned will be crucial for improving the system’s capabilities. Moreover, their constant improvement based on the results of user surveys and advances in technology will enhance the application of the chatbot to the improved levels of cybersecurity in schools, achieving the aims of this study.

Chapter 6: Discussion and Conclusion
6.1 Discussion
The development and initial deployment of the cybersecurity risk assessment chatbot reflect major achievements in improving cybersecurity in educational institutions. This project fits with the larger goals of using AI to mitigate intricate and context-dependent issues that other methods cannot solve (Familoni and Shoetan, 2024). Utilizing the results from system testing and assessing the testing methodologies used in this study, this chapter aims to examine the consequences derived from the research and consider possible future developments.

The incorporation of the AI-based chatbot in a cybersecurity setting presents a number of advantages as was evidenced by the systems’ capacity to handle advanced questions and respond with concise risk management solutions. This resonates well with the design science approach where the focus is placed more on the learning cycle and improvement thereof (Akula, no date). However, it is important to note that the present form of the chatbot is somewhat lacking in its abilities, especially on detecting advanced persistent threats and providing a more subtle course of action (Alsumayt et al. , 2024). Using more complex models and updating training datasets, the system can be adjusted to the emerging threats’ characteristics more effectively.

Such response-time fluctuations, experienced during testing, signify the need to enhance data processing speed (Bhardwaj et al. , 2024). This aspect is important so as to keep the users interested and active, especially where the situations may be time-sensitive. Moreover, although users expressed their satisfaction with the friendly user interface of the system, the desire for customization and ease of access in this area reveals a major area for improvement (Popoola et al. , 2024). Regarding the next layers of advancement, adding user experiences and making design cycles of the chatbot help it to be more open and accessible to users with different experience levels in using technology products.

Moreover, the project highlights the importance of ethical factors in the application of AI especially with regard to privacy and security of data. The application of Privacy by Design principles allowed for data privacy considerations to be included in all functional aspects of the system, conforming to best practices and regulatory standards (Camacho, 2024). This focus on ethical deployment not only protects the information of consumers but also ensures confidence and acceptance in the utilization of the tools in sensitive sectors such as education.

6.2 Conclusion
In conclusion, this dissertation successfully demonstrates the potential of AI-driven chatbots to enhance cybersecurity measures in educational institutions. Through a carefully considered and methodically executed design science framework, the project achieved its core objectives of developing a user-friendly tool capable of conducting effective risk assessments and providing actionable recommendations. The chatbot’s deployment within educational settings marks a step forward in addressing the cybersecurity challenges that these institutions uniquely face.

Reflecting on the project’s journey reveals both accomplishments and areas for future improvement. While the system’s effectiveness in common cybersecurity scenarios underscores its potential utility, addressing the limitations identified during testing—such as enhancing complexity handling and response times—will be key to its continued development and success. These iterations can be guided by ongoing developments in AI, ensuring the chatbot remains relevant and effective in an ever-evolving technological landscape (Pallathadka et al., 2024).

6.3 Contributions to the Field

The research contributes valuable insights into the intersection of AI and cybersecurity, offering a practical solution tailored to the needs of educational institutions. By focusing on a sector less explored compared to others like finance or healthcare, this project fills a critical gap, providing a template for similar implementations in other domains (Koukou and Stavrou, 2024). Furthermore, by placing a strong emphasis on ethical considerations, the research adds to the growing discourse on responsible AI deployment, highlighting the importance of integrating privacy and data security into system design.

6.4 Future Work
Moving forward, future work should focus on expanding the chatbot’s AI capabilities, exploring the integration of more advanced machine learning techniques to enhance its decision-making algorithms (Taskeen and Garai., 2024). External collaborations with cybersecurity experts could improve the system’s recommendations for dealing with advanced threats. Additionally, implementing user feedback to increase customization and accessibility options will remain a priority, ensuring the tool meets the diverse needs of its users.

Given the rapid pace of technological advancement, exploring the chatbot’s integration with emerging technologies, such as blockchain for data security, could provide an avenue for future research (Mingo, 2024). Moreover, longitudinal studies assessing the chatbot’s impact on cybersecurity awareness and behavior within educational institutions could offer deeper insights into its efficacy and inform further enhancements.

In summary, this dissertation underscores the significant potential of AI-based tools to transform cybersecurity frameworks in educational settings while emphasizing the need for ongoing development and ethical considerations. By building on these insights, future research can continue to leverage AI innovations to safeguard educational institutions, providing a secure digital environment conducive to learning and growth.


References
Abdelhamid, S., Mallari, T. and Aly, M. (2023) Cybersecurity Awareness, Education, and Workplace Training Using Socially Enabled Intelligent Chatbots. In: Lecture notes in networks and systems [online]. pp. 3–16.
Akpan, I.J., Kobara, Y.M., Owolabi, J., Akpam, A. and Offodile, O.F. (2024a) An investigation into the scientific landscape of the conversational and generative artificial intelligence, and human-chatbot interaction in education and research arXiv.org. June 15, 2024 [online]. Available from: https://arxiv.org/abs/2407.12004.
Akpan, I.J., Kobara, Y.M., Owolabi, J., Akpan, A.A. and Offodile, O.F. (2024) Conversational and generative artificial intelligence and human–chatbot interaction in education and research. International Transactions in Operational Research [online]. Available from: https://doi.org/10.1111/itor.13522.
Akula, V.K. (no date) Exploring the Integration of Cybersecurity in Chatbot Risk Management With Overall Enterprise Risk Management - ProQuest [online]. Available from: https://www.proquest.com/openview/75603d0dd51234a5d43e2b318f5bdb86/1?pq-origsite=gscholar&cbl=18750&diss=y.
Alsumayt, A., Alfawaer, Z.M., El-Haggar, N., Alshammari, M., Alghamedy, F.H., Aljameel, S.S., Alabbad, D.A. and Aldossary, M.I. (2024) Boundaries and Future Trends of ChatGPT Based on AI and Security Perspectives. HighTech and Innovation Journal [online]. 5 (1), pp. 129–142. Available from: https://hightechjournal.org/index.php/HIJ/article/view/625.
Asif, M., Manan, A., Rehman, A.M.U., Asghar, M.N. and Umair, M. (2024) AI-Driven Chatbot for Intrusion Detection in Edge Networks: Enhancing Cybersecurity with Ethical User Consent arXiv.org. August 8, 2024 [online]. Available from: https://arxiv.org/abs/2408.04281.
Ayanwale, M.A. and Ndlovu, M. (2024) Investigating factors of students’ behavioral intentions to adopt chatbot technologies in higher education: Perspective from expanded diffusion theory of innovation. Computers in Human Behavior Reports [online]. 14, p. 100396. Available from: https://www.sciencedirect.com/science/article/pii/S2451958824000290.
Bhardwaj, V., Dhaliwal, B.K., Sarangi, S.K., Thiyagu, T.M., Patidar, A. and Pithawa, D. (2024) Conversational AI. [online]. pp. 435–457. Available from: https://doi.org/10.1002/9781394200801.ch26.
Camacho, N.G. (2024) The Role of AI in Cybersecurity: Addressing Threats in the Digital Age. Deleted Journal [online]. 3 (1), pp. 143–154. Available from: https://doi.org/10.60087/jaigs.v3i1.75.
Costa, A.F. and Coelho, N.M. (2024) Evolving Cybersecurity Challenges in the Age of AI-Powered Chatbots: A Comprehensive Review. IFIP Advances in Information and Communication Technology [online]. pp. 217–228. Available from: https://doi.org/10.1007/978-3-031-63851-0_15.
Falade, P.V., 2024. Deciphering ChatGPT’s Impact: Exploring Its Role in Cybercrime and Cybersecurity. Int. J. Sci. Res. in Computer Science and Engineering Vol, 12(2).
Familoni, N.B.T. and Shoetan, N.P.O. (2024) CYBERSECURITY IN THE FINANCIAL SECTOR: A COMPARATIVE ANALYSIS OF THE USA AND NIGERIA. Computer Science & IT Research Journal [online]. 5 (4), pp. 850–877. Available from: https://doi.org/10.51594/csitrj.v5i4.1046.
Giordani, J. (2024) Mitigating Chatbots AI Data Privacy Violations in the Banking Sector: A Qualitative Grounded Theory Study. Deleted Journal [online]. 2 (4), pp. 14–65. Available from: https://doi.org/10.59324/ejaset.2024.2(4).02.
Hasan, N., Polin, J.A., Ahmmed, Md.R., Sakib, Md.M., Jahin, Md.F. and Rahman, Md.M. (2024) A novel approach to analyzing the impact of AI, ChatGPT, and chatbot on education using machine learning algorithms. Bulletin of Electrical Engineering and Informatics [online]. 13 (4), pp. 2951–2958. Available from: https://beei.org/index.php/EEI/article/view/7158.
Joon, N. and Simmons, R. (2024) Innovating Cybersecurity Education Through AI-augmented Teaching. European Conference on Cyber Warfare and Security [online]. 23 (1), pp. 480–486. Available from: https://papers.academic-conferences.org/index.php/eccws/article/view/2224.
Kapoor, V. (2024) Artificial Intelligence and Cyber Security in the Service Industry. In: Apple Academic Press eBooks [online]. pp. 265–303.
Koukou, C. and Stavrou, E. (2024) The Potential of Generative AI Chatbots as Career Advisors in Cybersecurity: Professionals’ Perspectives. In: IFIP advances in information and communication technology [online]. pp. 191–206.
Mahmood, S., Chadhar, M. and Firmin, S. (2024) Countermeasure Strategies to Address Cybersecurity Challenges Amidst Major Crises in the Higher Education and Research Sector: An Organisational Learning Perspective. Information [online]. 15 (2), p. 106. Available from: https://www.mdpi.com/2078-2489/15/2/106.
Mingo, H.C. (2024) The Emerging Cybersecurity Challenges With Artificial Intelligence. In: Advances in medical technologies and clinical practice book series [online]. pp. 163–185.
Pallathadka, H., Sanchez, D.T., Peconcillo, L.B., Jawarneh, M., Godinez, J.A.T. and De Vera, J.V. (2024) Conversational Chatbot‐Based Security Threats for Business and Educational Platforms and Their Counter Measures. [online]. pp. 107–126. Available from: https://doi.org/10.1002/9781394200801.ch8.
Pinaki, S. (2023) Insights2Techinfo. October 31, 2023 [online]. Available from: https://insights2techinfo.com/enhancing-cyber-security-in-the-banking-sector-with-chatbot-assistance/.
Popoola, N.O.A., Akinsanya, N.M.O., Nzeako, N.G., Chukwurah, N.E.G. and Okeke, N.C.D. (2024) Exploring theoretical constructs of cybersecurity awareness and training programs: comparative analysis of African and U.S. Initiatives. International Journal of Applied Research in Social Sciences [online]. 6 (5), pp. 819–827. Available from: https://doi.org/10.51594/ijarss.v6i5.1104.
Sebastian, G. (2024) Unraveling the Nexus: AI and Chatbot’s like Chatgpt Impact on Cybersecurity. SSRN Electronic Journal [online]. Available from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4786611.
Shafi, P.M., Gahangir, H (2024) IEEE Conference Publication | IEEE Xplore. January 8, 2024 [online]. Available from: https://ieeexplore.ieee.org/abstract/document/10427923.
Sonkor, M.S. and De Soto, B.G. (2024) Using ChatGPT in construction projects: unveiling its cybersecurity risks through a bibliometric analysis. International Journal of Construction Management [online]. pp. 1–9. Available from: https://doi.org/10.1080/15623599.2024.2355782.
Taskeen, N. and Garai, S. (2024) Emerging Trends in Cybersecurity: A Holistic View on Current Threats, Assessing Solutions, and Pioneering New Frontiers. Blockchain in Healthcare Today [online]. 7 (1). Available from: https://doi.org/10.30953/bhty.v7.302.
Toussaint, M., Krima, S. and Panetto, H. (2024) Industry 4.0 data security: a cybersecurity frameworks review. Journal of Industrial Information Integration [online]. 39, p. 100604. Available from: https://doi.org/10.1016/j.jii.2024.100604.
Vaish, A., Kumar, R., Bobek, S. and Sternad, S. (no date) Development of Cyber Security Platform for Experiential Learning DigitalCommons@Kennesaw State University [online]. Available from: https://digitalcommons.kennesaw.edu/jcerp/vol2024/iss1/22/.
Williams, R. (2024) Deep HoriXons: 3D virtual generative AI assisted campus for deep learning AI and cybersecurity. [online]. Available from: https://doi.org/10.1117/12.3011374.