7  AI-Assisted Case Study Development

One of the key innovations in the Cyber Dimensions methodology is the systematic use of generative AI large language models (LLMs) to accelerate and enhance case study development. This chapter provides practical guidance for leveraging AI tools to create sophisticated, multi-layered cybersecurity scenarios while maintaining educational integrity and professional authenticity.

Theoretical Foundation and Strategic Approach

Traditional case study development often requires extensive research, scenario creation, and artifact generation that can take weeks or months to complete. Further, choosing actual, historical case studies that are well-known to students (consider the case of Edward Snowden) may invite prejudice or bias. AI-assisted development transforms this process by enabling rapid generation of comprehensive case studies while maintaining educational quality through systematic human oversight. This approach generates content in minutes rather than weeks, maintains character and organizational continuity across complex scenarios, creates extensive artifact collections with realistic technical details, supports rapid iteration based on learning objectives, and enables development of multiple interconnected cases for comprehensive curricula. By using the worldbuilding method developed here, minor tweaks can automatically cascade throughout all content.

Posthuman Pedagogical Alignment

AI-assisted development aligns perfectly with posthuman educational principles by recognizing that knowledge creation emerges through collaboration between human expertise and technological capabilities. The instructor provides educational vision, learning objectives, and quality control, while AI contributes rapid content generation, consistency management, and creative scenario development.

This approach embodies the material-semiotic practices central to posthuman pedagogy: educational content emerges through the dynamic interaction between human pedagogical expertise and AI technological capabilities, creating learning experiences that neither could achieve independently.

The partnership maintains clear divisions of responsibility. Human educators provide learning objectives, quality standards, educational vision, authenticity verification, and basic narrative direction. AI systems contribute content generation, scenario development, artifact creation, consistency tracking, and multimedia development. Together, these capabilities produce sophisticated educational experiences that engage students in authentic professional contexts.

Strategic Development Workflow

flowchart TD
    A[📋 Educational Planning] --> B[🤖 AI Collaboration]
    B --> C[⚡ Content Generation] 
    C --> D[🔍 Quality Review]
    D --> E[🎯 Refinement]
    E --> F[🔗 Integration]
    F --> G[📊 Assessment Alignment]
    
    A --- A1["• Learning Objectives<br/>• Student Context<br/>• Curriculum Integration"]
    B --- B1["• Structured Prompting<br/>• Worldbuilding Context<br/>• Character Development"]
    C --- C1["• Scenario Creation<br/>• Artifact Generation<br/>• Timeline Development"]
    D --- D1["• Technical Accuracy<br/>• Educational Alignment<br/>• Professional Realism"]
    E --- E1["• Content Enhancement<br/>• Character Refinement<br/>• Narrative Coherence"]
    F --- F1["• Cross-Case Continuity<br/>• Worldbuilding Updates<br/>• Assessment Preparation"]
    
    style A fill:#e8f4f8
    style B fill:#f0f8e8
    style C fill:#fff8e8
    style D fill:#f8e8f0
    style E fill:#f0e8f8
    style F fill:#e8f8f0

Figure 7.1

Effective AI-assisted case study development follows a systematic workflow that begins with strategic educational planning. Before engaging with AI tools, instructors must establish clear educational parameters including specific cybersecurity concepts to be demonstrated, professional skills to be developed, and assessment methods and evaluation criteria. The scenario parameters should define industry context and organizational type, incident type and complexity level, character roles and professional relationships, and technical systems and security tools involved. Educational constraints must address student preparation level and prerequisite knowledge, time allocation for case study completion, and integration with existing curriculum and other cases.

The AI collaboration phase employs structured prompting that provides comprehensive context while maintaining educational focus. Effective prompting requires context setting that informs AI about educational goals, student audience, and curriculum integration requirements. Scenario specification describes the type of cybersecurity incident, organizational context, and complexity level appropriate for learning objectives. Artifact requirements specify the types of realistic materials students will need to analyze, such as logs, emails, reports, and social media. Character development requests consistent professional personas that can appear across multiple related cases.

Quality Assurance Framework

AI-generated content requires systematic review to ensure educational effectiveness and professional authenticity. Technical accuracy review verifies that generated scenarios reflect current cybersecurity practices, tools, and procedures. Educational alignment confirms that case study elements support stated learning objectives and skill development. Narrative consistency checks character development, organizational details, and timeline coherence across multiple scenarios. Professional realism validates that workplace dynamics, communication patterns, and decision-making processes mirror actual cybersecurity practice.

Cognitive-load-alertAI Content Review Requirements

AI-generated content requires more systematic review than human-authored materials because AI may produce technically accurate but educationally inappropriate complexity, or create realistic-seeming scenarios that don’t actually reflect professional practice.

The quality assurance process follows systematic verification procedures. Professional review involves practicing cybersecurity professionals verifying technical accuracy and workplace realism. Educational review confirms that all content elements serve clear learning purposes. Student testing pilots AI-generated cases with target student populations to identify comprehension barriers. Iterative improvement uses feedback to refine both individual cases and AI collaboration approaches.

Practical Implementation with Real Examples

The following example demonstrates how structured AI collaboration produces sophisticated educational content. This is the actual initial prompt used to develop the BELLATOR case study for the Cyber Dimensions textbook, designed to accompany a module on cyber warfare.

Evidence-baseReal Development Example

This prompt successfully generated a comprehensive case study covering cyber warfare ethics, autonomous AI weapons, and international law that has been used successfully with hundreds of cybersecurity students. This doesn’t mean the case study is finished after submitting this prompt! Rather, it’s the beginning of a collaborative project between you as the expert and the AI as the workhorse.

BELLATOR Case Study Development Prompt

# Prompt for LLM

It's time to create the fictitious case study/scenario for the module on cyber warfare. The main focus should be on the ethics of autonomous AI weapons and the legal and policy considerations in international relations.

I've provided other case studies as attachments and I'm providing the module lecture with the full module lecture notes and learning objectives. Please ask me any clarifying questions before you start building the case study.

Here is the information to get you started:

## Learning Objectives

By engaging with this multilayered scenario, you will have the opportunity to:

1. Gain a foundational understanding of Jus In Bello and Jus Ad Bellum, and their relevance to cyber operations.
2. Learn about the Hague and Geneva Conventions and their applications to cybersecurity.
3. Understand the principles and guidelines set forth in the Tallinn Manual and its role in cyber warfare.
4. Analyze the specific U.S. laws governing cyber operations, distinguishing between offensive and defensive operations.
5. Explore emerging cyber-related elements such as artificial intelligence and quantum encryption.

## Case Study: "Operation Shadow Whisper"

In 2025, Country A develops an advanced AI system called "Shadow Whisper" designed to infiltrate and manipulate the AI-driven financial trading systems of Country B. The AI is capable of making subtle, nearly undetectable changes to trading algorithms, gradually destabilizing Country B's economy. The operation doesn't cause immediate, visible damage, but over time it could lead to significant economic harm. The case study would explore:

- The threshold of "armed attack" in non-kinetic warfare
- Application of Just Information Warfare principles to economic targets
- Legal and ethical implications of AI-on-AI warfare
- Challenges in attribution and response in AI-driven cyber operations

The case study should contain the following in separate Claude markdown artifacts:

- An extensive background on the fabricated story
- Clear (but not overt) ties to the module learning objectives
- Highly realistic materials for students to examine including but not limited to:
    - News articles
    - Blog posts
    - Social media threads
    - Government meeting transcripts
    - Leaked memos
    - Legal documents
- A clear and narrative-driven timeline of events
- Consistent key individuals that may reappear across the case study

Analysis of Effective Prompting Strategy

This prompt demonstrates several key principles for effective AI collaboration. Clear educational context establishes specific learning objectives and connects them to cybersecurity concepts students need to master. Scenario specification provides a concrete but flexible framework (“Operation Shadow Whisper”) that allows AI to develop detailed narratives while maintaining educational focus. Artifact requirements specify the types of realistic materials students will analyze, ensuring the case study supports active learning rather than passive reading. Character consistency requests persistent characters who can appear across multiple scenarios, supporting cross-case continuity and student engagement. Professional realism emphasizes the need for “highly realistic materials” that mirror actual cybersecurity practice and professional communication.

The BELLATOR case study generated from this prompt demonstrates the power of systematic AI collaboration. The resulting case study includes fifteen realistic artifacts spanning news reports, government communications, social media threads, and legal documents. Complex ethical scenarios require students to apply international law and just war theory to cyber operations. Multi-stakeholder perspectives show how the same events appear different to various professional roles. Progressive complexity scaffolds student learning from basic concepts to sophisticated ethical analysis.

Assessment-strategyEducational Effectiveness Validation

Student feedback on the BELLATOR case study consistently highlights the realism and engagement value of the AI-generated materials, with many noting that the artifacts “feel like real documents” and help them understand the complexity of professional cybersecurity decision-making.

Platform Selection and Technical Considerations

Choosing the appropriate AI platform significantly impacts content development effectiveness. Different platforms excel in various aspects of educational content creation, and understanding these differences enables strategic tool selection. The selection process should consider educational content creation factors including context window size (how much background material can you provide), instruction following (how well does the platform adhere to educational specifications), consistency (does the platform maintain character and narrative coherence), iteration support (can you refine content without starting over), and technical accuracy (how reliable is domain-specific cybersecurity content).

LLM Platform Comparison Framework

Table 7.1 provides a systematic comparison framework for evaluating AI platforms for cybersecurity case study development. Different platforms excel in different aspects of case study development. Consider starting with one platform for initial development, then using specialized platforms for specific refinement tasks rather than attempting to master multiple systems simultaneously. These suggestions will quickly become out of date, so running tests for yourself is highly encouraged.

Evidence-baseAuthor's Note on Rating LLMs for Case Study Development

The following comparison is based on my own experience with these models. Your mileage may vary! Also note that context window is not included, as, between Claude’s sub-agents and Gemini’s 1 million token window, this is becoming less and less of a consideration.

Table 7.1: LLM Platform Comparison for Educational Content Development

Platform Feature	GPT-4 Series	Claude Series	Gemini Pro	Open Source Models
Educational Focus	General Purpose	Strong Academic	Research-Oriented	Specialized Training
Technical Accuracy	Good	Excellent	Good	Variable
Character Consistency	Moderate	Excellent	Good	Requires Fine-tuning
Iteration Support	Good	Excellent	Moderate	Platform-Dependent
Cost Consideration	Moderate-High	Moderate	Moderate	Low-Free
Privacy Controls	Commercial	Commercial	Commercial	Full Control

Platform-specific optimization strategies should guide tool selection based on content type:

Narrative Development:

• Claude: Excels at maintaining character consistency and educational tone
• GPT-4: Provides strong creative scenario generation with technical integration
• Gemini: Offers effective research integration and factual accuracy

Technical Content:

• Claude: Delivers superior technical accuracy in cybersecurity domains
• GPT-4: Provides good general technical knowledge with verification needs
• Open Source Models: Offer specialized capabilities for specific technical areas

Educational Integration:

• Claude: Provides natural academic writing style and pedagogical awareness
• GPT-4: Offers flexible output formatting and educational tool integration
• Gemini: Supports research citation and evidence integration

Quality Assurance and Refinement Process

The quality assurance process described here supplements the Quality Assurance chapter, aligning with posthumanist notions of collaboration between humans and technology rather than treating AI as a simple tool. The following example demonstrates comprehensive quality assurance for AI-generated content using the Quarto project framework discussed throughout this toolkit.

We're going to work on updating one of my class's fabricated case studies. We will take a number of steps:

1. We'll work on improving the engagement of it (just making it more interesting and engaging over all, along with more realistic)
2. Then we'll work on consistency and internal logic (timelines, realism, squashing contradictions, et cetera)
3. Then we'll work on incorporating it into the world we've been building by making connections to existing characters, places, organizations, timelines, and so on.

We use Quarto markdown rendered to HTML, so I'm going to give you a few different files below.

1. The full case study as a .qmd file in a <qmd /> tag. There are some HTML comments that are essentially "notes to self" for me but also clarification and background for you.
2. The common CSS file that is used between all the case study qmd files. When editing styles in the qmd, use these existing styles. If we need to create new styles, we will only do so with my express permission.
3. The "worldbuilding" YAML that is also loaded across all case studies. There are some variables that are case-study-specific (denoted by the first word of the variable being the name of the case study) and some that are case-study-agnostic, meaning they exist generally in the "world" we're building. The year, for example. As we progress I'd like to make more of these "general" to connect the case studies in fun and intriguing ways.

I'd like you to read through all files 3 times: first for a general understanding of the case study, a second time for detail, and a third time for clarity and confirmation. Report back when you have done this. Do not begin creating, editing, or providing feedback until I tell you to proceed.

<qmd>
[paste entire existing case study here]
</qmd>

```css
[css here]
```

```yaml
[Paste worldbuilding yaml here]
```

This systematic approach to quality assurance demonstrates the collaborative partnership between human expertise and AI capabilities. The process follows a structured methodology that addresses engagement and realism, consistency and internal logic, and worldbuilding integration. Each phase includes specific verification requirements and revision protocols that ensure educational effectiveness and professional authenticity.

Advanced Techniques and Troubleshooting

Effective AI collaboration requires understanding common challenges and systematic approaches to resolution. Most AI collaboration challenges stem from insufficient context, unclear objectives, or misaligned expectations, and systematic troubleshooting improves both immediate outcomes and long-term collaboration effectiveness.

Common Collaboration Challenges and Solutions

Technical Accuracy Issues:

Diagnostic Questions:

• Was sufficient technical context provided in prompts?
• Do content requests extend beyond the AI’s knowledge cutoff?
• Does the AI have access to current cybersecurity best practices?

Solution Strategies:

• Provide specific technical requirements and current standards
• Include relevant documentation or examples
• Request justification for technical details
• Schedule professional review before content finalization

Character Realism and Consistency Problems:

Diagnostic Questions:

• Was complete worldbuilding context provided?
• Does character development span multiple sessions without continuity support?
• Does the AI understand professional cybersecurity roles?

Solution Strategies:

• Include _worldbuilding.yml content in all prompts
• Provide character background summaries for multi-session development
• Request specific professional context and workplace dynamics
• Ask AI to explain character motivation and behavior

Educational Content Complexity Issues:

Diagnostic Questions:

• Were student preparation level and prerequisites specified?
• Are learning objectives clearly defined in prompts?
• Do scenarios include appropriate scaffolding?

Solution Strategies:

• Provide detailed student context including prior coursework
• Request specific complexity level and scaffolding suggestions
• Ask for multiple difficulty variations
• Test content with target student populations before finalization

Lack of Educational Focus:

Diagnostic Questions:

• Were clear learning objectives provided?
• Are assessment methods specified in prompts?
• Does the AI understand cybersecurity education goals?

Solution Strategies:

• Lead with learning objectives rather than scenario details
• Specify assessment alignment requirements
• Request explicit connections between content and educational goals
• Ask AI to justify educational value of specific elements

Implementation Template Library

Systematic AI collaboration requires reusable templates that ensure consistent quality and educational alignment. The comprehensive case study development template should address educational framework including target audience, learning objectives, assessment methods, and integration requirements. Scenario specifications should define industry context, organizational structure, incident type, and complexity level. Content requirements should specify narrative overview, character development, artifact collection, and timeline structure. Quality standards should address technical accuracy, professional authenticity, and educational effectiveness.

Enhancement and refinement templates should focus on technical enhancement through verification of specific tools and procedures, addition of realistic technical details, and authentication of security responses. Character development should expand background information, develop professional relationships and communication patterns, and add authentic workplace context. Educational alignment should strengthen support for specific learning objectives, add scaffolding for complex concepts, and include clear assessment opportunities. Cross-case integration should connect worldbuilding elements, reference previous cases for continuity, and set up future learning opportunities.

Integration with Toolkit Methodologies

AI-assisted development works most effectively when integrated systematically with other toolkit methodologies. Understanding these connections ensures coherent case study development that leverages all available pedagogical tools.

Theoretical-foundationSystematic Integration Approach

Effective cybersecurity case study development emerges through coordinated application of posthuman pedagogical principles, systematic worldbuilding, evidence-based assessment, and rigorous quality assurance - with AI serving as an accelerant for human expertise rather than a replacement.

Foundational Integration: Posthuman Principles and AI Collaboration

AI collaboration should begin with clear understanding of posthuman educational principles. This theoretical foundation guides prompting strategy and quality evaluation by ensuring that learning emerges through assemblages of humans, technology, and context. Students develop response-ability through authentic professional scenarios, knowledge is constructed through material-semiotic practices, and assessment focuses on ethical engagement rather than correct answers. Scenarios should present genuine ethical complexity without predetermined solutions, include multiple valid professional perspectives, require students to engage with both technical and human factors, and support collaborative rather than competitive learning.

Development Integration: Worldbuilding and AI Assistance

The worldbuilding framework provides the systematic foundation that makes AI collaboration efficient and consistent. Integration follows a structured workflow that establishes worldbuilding parameters in _worldbuilding.yml, provides AI with complete worldbuilding context, generates case content using established characters and organizations, updates worldbuilding data with new developments, and cross-references consistency across multiple cases.

Worldbuilding-informed prompting ensures character consistency by featuring established characters from previous cases, maintaining professional relationships, and showing realistic career development. Organizational continuity uses established organizations with defined culture and procedures, references previous incidents and organizational learning, and maintains technical infrastructure and security maturity levels. Timeline coherence occurs in specific years per worldbuilding specifications, references appropriate technology and threat landscapes, and aligns with broader narrative progression.

Assessment Integration: AI and Evidence-Based Evaluation

The assessment framework should guide AI collaboration to ensure generated content supports meaningful evaluation of student learning. Assessment-aligned content generation should support progressive investigation through artifacts that reveal information gradually, evidence requiring analysis and interpretation, and multiple investigation pathways with different insights. Stakeholder navigation should include characters with conflicting professional priorities, ethical dilemmas requiring perspective-taking, and realistic organizational politics and constraints. Portfolio development should provide opportunities for student reflection and analysis, connections to prior learning and professional development, and materials suitable for professional portfolio inclusion.

Implementation Planning and Future Directions

Successful implementation requires systematic approach development that begins with single case studies to develop AI collaboration skills before attempting comprehensive curricula. Quality control procedures should be established before generating extensive content. Professional networks with practicing cybersecurity professionals should be developed for technical accuracy verification. Template libraries should be created for reusable prompt templates and quality checklists to ensure consistent development outcomes.

Platform selection should consider the full range of capabilities for sophisticated collaboration with extensive background materials, customization capabilities that improve educational content generation, output control that allows iterative refinement rather than complete regeneration, and integration options that fit existing educational technology ecosystems.

Ethical Considerations and Professional Responsibility

Theoretical-foundationEducational Integrity with AI Tools

Students benefit from understanding that sophisticated educational content can emerge through thoughtful human-AI collaboration, but they deserve transparency about the development process and continued human oversight of educational quality.

Recommended disclosure approaches should acknowledge AI assistance in content development while emphasizing human educational design and quality control. The focus should remain on learning outcomes and educational effectiveness rather than specific development methods. AI collaboration should be used as an opportunity to discuss the role of AI tools in professional cybersecurity practice.

Human educational leadership remains essential in areas where AI tools cannot replace human expertise. Educational design requires understanding how students learn cybersecurity concepts and develop professional capabilities. Professional context demands knowledge of what cybersecurity practice actually looks like in different organizational contexts. Quality judgment involves recognizing when content serves educational purposes versus when it creates unnecessary complexity. Student relationships require understanding individual student needs and adapting content accordingly.

Contributing to the Field

Innovations in AI-assisted cybersecurity education contribute to the broader development of effective human-AI collaboration in higher education. Educators should consider documenting successful techniques by sharing effective prompting strategies and quality control procedures. Research opportunities exist to study learning outcomes and student engagement benefits of AI-assisted case studies. Professional development can involve presenting methods at cybersecurity education conferences and professional gatherings. Open educational resources can contribute successful AI collaboration frameworks to the broader cybersecurity education community.

AI-assisted case study development represents a significant opportunity to enhance cybersecurity education while maintaining the human expertise and educational vision that make learning experiences truly effective. Through thoughtful collaboration with AI tools, educators can create sophisticated, engaging educational content that prepares students for the complex realities of professional cybersecurity practice while preserving the pedagogical integrity and professional authenticity that characterize excellent cybersecurity education.