Hey Fellow Case Writers!

This week we focus on crafting effective AI use statements; policies that clearly communicate expectations around artificial intelligence in educational settings.

To be honest, it is like the Wild West out there with AI use and abuse. Considering there are limited ways to know whether the writing is human or AI and more importantly, if the author is allowed to use AI - Setting Expectations is Critical.

Enter AI Use Statements.

Why Case Publishers Need Clear AI Guidelines

As case study publishers and repositories, we're facing unprecedented challenges from generative AI. Authors are using ChatGPT, Claude and other tools to draft, edit, and enhance their cases, sometimes without disclosure. Meanwhile, faculty are struggling to determine whether student case analyses are original or AI-generated.

I've had many conversations with editors and publishers alike who are wrestling with these questions. What constitutes appropriate AI use in case development? How much assistance crosses the line? What disclosure standards should we require? What is the meaning behind a published case and what does that represent from the author?

Here's why establishing clear AI policies is crucial for our industry:

• Authors need explicit guidance on acceptable AI use in case writing

• Faculty need to know how cases were developed to set appropriate student guidelines

• Inconsistent standards across publishers create confusion in the field

• Without clear policies, we risk undermining the credibility of our case collections

• Properly documented AI use can actually enhance case development while maintaining integrity

Elements of an Effective Case Publisher AI Statement

Based on my research with leading case publishers, an effective AI use statement for case publishers should include:

1. Clear scope definition: Which aspects of case writing can/cannot involve AI assistance

2. Disclosure requirements: How authors must document any AI use in their submissions

3. Quality standards: How AI-assisted content will be evaluated and verified

4. Attribution guidelines: How to properly credit AI contributions in published materials

5. Review process details: How editors will assess AI-assisted submissions

Four Approaches to AI Policies for Case Publishers

Here are the main approaches I've seen emerging across case publishing organizations:

The Traditional Approach

AKA: "Human Authorship Only"

This conservative approach prohibits AI use in substantive case content creation.

Example: "Cases submitted to our repository must be fully human-authored. AI tools may be used for copyediting or formatting only, and such use must be disclosed in the submission process."

Reality check: While this preserves traditional authorship standards, it may become increasingly difficult to enforce and potentially limits innovation in case development. With the simple change of a word, the intent to edit becomes to generate and create.

The Limited Assistance Model

AKA: "Research and Refinement Only"

This approach allows AI for research and polishing but requires human authorship for core content.

Example: "Authors may use AI tools for background research, generating ideas, and refining prose, but the core case narrative, teaching notes, and analytical content must be primarily human-created. Authors must complete an AI assistance disclosure form detailing where and how AI was used."

Reality check: This balances innovation with academic integrity, though requires careful definition of what constitutes "core content."

The Transparency Framework

AKA: "Full Disclosure Approach"

This model permits extensive AI use with comprehensive documentation.

Example: "Authors may utilize AI throughout the case development process, provided they submit a detailed AI collaboration statement that documents: 1) which sections involved AI assistance, 2) what prompts were used, 3) how outputs were verified and edited, and 4) what percentage of the final content they estimate is AI-generated."

Reality check: This pragmatic approach acknowledges AI's growing role while maintaining transparency, though it requires more sophisticated review processes.

The Collaborative Intelligence Model

AKA: "AI as Co-Writer"

This progressive approach treats AI as a legitimate collaborator in the case development process.

Example: "Our publisher views AI as a valuable co-developer in case creation. Authors should leverage AI capabilities while applying human judgment, subject expertise, and ethical considerations. Cases must include an AI collaboration appendix detailing the collaborative process and differentiating human versus AI contributions."

Reality check: This forward-looking approach embraces AI's potential but requires significant changes to our traditional understanding of case authorship and attribution.

Developing Your Organization's AI Guidelines

When crafting AI use statements for your case publishing organization, consider:

1. Core values: What does your organization believe about authorship and intellectual contribution?

2. Publisher differentiation: How might your AI policy align with your brand and strategic positioning?

3. Faculty needs: What do the instructors using your cases need to know about their development?

4. Verification mechanisms: How will you assess compliance with your policy?

5. Competitive landscape: How are peer organizations approaching this challenge?

Implementation Strategies from Leading Publishers

Now lets look at some effective implementation approaches:

• Develop clear templates - Create standardized AI disclosure forms and examples of properly documented AI use

• Pilot with trusted authors - Test your policy with experienced case writers before full implementation

• Educate your ecosystem - Provide training for authors, reviewers, and editors on effective and ethical AI use

• Establish verification protocols - Consider technical approaches to identifying undisclosed AI content

• Update author guidelines - Integrate AI policies into broader submission requirements and review criteria

Creating a Consistent Industry Approach

The case publishing community would benefit from greater standardization in AI policies:

1. Consider forming a cross-publisher working group to develop common standards and terminology

2. Review policies from adjacent publishing fields (academic journals, textbook publishers) for transferable practices

3. Engage faculty stakeholders to understand their needs regarding AI disclosure in teaching materials

4. Develop shared tools for AI disclosure that could work across publishing platforms

5. Plan regular review cycles as AI capabilities continue to evolve rapidly

What This Means for Case Writers

If you're writing cases for publication, here's how to navigate this evolving landscape:

• Familiarize yourself with each publisher's specific AI policies before submission

• Document your AI use throughout the writing process, including prompts used and sections affected

• Be prepared to explain how you verified any AI-generated content for accuracy

• Consider how AI use aligns with the teaching objectives of your case

• When in doubt, disclose more rather than less about your process

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Wrapping it all Up!

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Warm regards,

Matthew