Digital Naming Conventions for LLM Outputs: A Human-Centered Approach

"How do we preserve the authenticity of human creativity while embracing AI collaboration in education?"

This is part of the AI in Education series where I guide you through seven transformative principles to help you navigate the integration of artificial intelligence in your classroom with confidence and clarity.

Last month, I shared that there are three fundamental shifts every educator should understand about AI-enhanced learning environments. And these insights don't just apply to teachers.

The principles of thoughtful AI integration can transform any area of your life, whether it's curriculum development, student assessment, or professional development. But only if you're willing to embrace the complexity of human-AI collaboration while maintaining clear boundaries and ethical guidelines.

I've shared the importance of establishing clear AI usage policies and creating transparent assessment criteria. Today we're exploring perhaps the most practical yet philosophically rich challenge: creating standardised naming conventions for AI-assisted student work.

When you stop to think about it, this seemingly mundane administrative task opens up fascinating questions about the nature of authorship in the age of AI. How do we acknowledge the spectrum of AI assistance in student work? Where do we draw the line between enhancement and generation?

These questions matter deeply because they strike at the heart of academic integrity and student development. When we have clear systems for documenting AI involvement, we feel confident in our ability to guide students through responsible AI use. But often times along the way, we encounter resistance from those who fear that acknowledging AI assistance might diminish the perceived value of student work. Or maybe we struggle with students who don't understand why such detailed documentation is necessary. Maybe you've found yourself wondering if all this record-keeping is worth the effort.

And that leaves a lot of dedicated educators feeling uncertain about how to proceed.

Take a moment to reflect on your own experiences with student work that involves AI tools. How do you currently distinguish between fully human-generated content and AI-assisted work? What systems, if any, do you have in place?

And as a follow-up to that, consider how your current approach might scale as AI tools become more prevalent and sophisticated in educational settings.

The Evolution of Academic Documentation

Educators undergo different phases in their relationship with AI tools. There are initial periods of excitement and experimentation, where the possibilities seem endless and the challenges minimal. This is especially true for innovative educators like yourself who are eager to embrace new technologies.

But eventually, every person reaches a critical juncture—whether it's after their first plagiarism case or their hundredth submission—when they begin to recognise the vital importance of systematic documentation.

We've seen this pattern in other areas of education as well.

"How do I know if this is really the student's own work?"

"What's the appropriate level of AI assistance for this assignment?"

And one of two things can happen in that moment. Either we can retreat into rigid restrictions and blanket prohibitions...

Or we can develop nuanced systems that acknowledge the reality of AI while preserving academic integrity.

But if you do find that your current naming conventions are inadequate for the AI age, it's time to implement a more robust system.

The cost of ambiguity in this area is simply too high.

A Framework for Digital Naming Clarity

This isn't something a lot of people talk about, and yet it's incredibly important for the future of education.

And you might be thinking, "Isn't this just adding unnecessary complexity to an already challenging situation?"

You're not alone. When I first started working with schools on AI integration, I encountered significant resistance to implementing detailed naming conventions. Many saw it as a bureaucratic overhead that would stifle creativity and spontaneity.

About 70% of the educators I initially spoke with felt that any formal system for tracking AI involvement would be too cumbersome to maintain.

What do you have if you don't have clear naming conventions? Chaos. Confusion. And most importantly, missed opportunities for understanding how students are actually engaging with AI tools.

You see, I believe that you need to think of naming conventions not as restrictions, but as a form of documentation that tells the story of student learning in the AI age.

Even if it seems tedious at first, you have to establish clear protocols for identifying different levels of AI involvement...

When people don't implement systematic naming conventions, they lose valuable data about student AI tool usage patterns, instead of gaining insights that could inform better teaching practices.

And they make assumptions about student work that may not reflect reality.

Implementation Strategies

So, what can you do?

I propose a tiered naming system that reflects different levels of AI involvement:

1. Pure Human Creation (PHC)
2. AI-Enhanced Research (AER)
3. AI-Assisted Writing (AAW)
4. AI-Collaborative Development (ACD)
5. AI-Generated Content with Human Editing (AGHE)

This allows you to track the evolution of student work while maintaining transparency about AI involvement. And it provides valuable data for understanding how students are actually using AI tools in their learning process.

Let's break down each tier in detail:

Pure Human Creation (PHC)

• Traditional assignments completed without any AI assistance
• Example filename: PHC_Smith_EssayDraft1_2024
• Importance: Establishes baseline for student capabilities
• Use cases: Initial drafts, personal reflections, creative writing

AI-Enhanced Research (AER)

• AI tools used for gathering information and brainstorming
• Example filename: AER_Smith_ResearchNotes_2024
• Tracking method: Students document AI queries and responses
• Benefits: Develops critical thinking in AI interaction

AI-Assisted Writing (AAW)

• AI used for grammar, style, and structure suggestions
• Example filename: AAW_Smith_EssayRevision_2024
• Documentation requirements: Log of AI suggestions and student decisions
• Learning outcomes: Enhanced editing and revision skills

AI-Collaborative Development (ACD)

• Interactive development process between student and AI
• Example filename: ACD_Smith_ProjectPlan_2024
• Assessment focus: Quality of student-AI dialogue
• Skill development: AI prompt engineering and critical evaluation

AI-Generated Content with Human Editing (AGHE)

• Base content generated by AI, substantially edited by student
• Example filename: AGHE_Smith_FirstDraft_2024
• Evaluation criteria: Quality of human modifications and improvements
• Learning emphasis: Content curation and enhancement skills

The Philosophy Behind the System

This naming convention system isn't just about organisation—it's about acknowledging the complex reality of modern education. Each prefix tells a story about the learning process and the student's relationship with AI tools.

Consider how this system supports various educational objectives:

Transparency in Assessment

• Clear documentation of AI involvement
• Easier evaluation of student contributions
• Support for academic integrity

Student Skill Development

• Understanding appropriate AI use cases
• Developing AI interaction skills
• Building critical evaluation abilities

Educational Research

• Data collection on AI tool usage
• Pattern identification in student learning
• Evidence-based policy development

Professional Development

• Teacher awareness of AI integration
• Targeted support for specific use cases
• Collaborative learning opportunities

Cultural Implementation

Successfully implementing these naming conventions requires more than just technical understanding. It requires cultural buy-in from all stakeholders:

For Teachers

• Professional development sessions on the naming system
• Regular review and refinement of conventions
• Collaboration on best practices
• Documentation of success stories and challenges

For Students

• Clear guidelines and examples
• Regular reminders and support
• Positive reinforcement for proper documentation
• Opportunities for feedback and system improvement

For Administrators

• Policy development and support
• Resource allocation for implementation
• Regular evaluation of system effectiveness
• Communication with stakeholders

Future Considerations

As AI technology continues to evolve, our naming conventions must remain flexible enough to accommodate new developments:

Emerging AI Tools

• Integration of new AI capabilities
• Updates to classification system
• Regular review of categories

Educational Trends

• Adaptation to new teaching methods
• Support for innovative assignments
• Integration with emerging technologies

Policy Development

• Regular review and updates
• Stakeholder consultation
• Evidence-based refinement

Practical Implementation Tips

To make this system work in your educational setting:

Start Small

• Begin with a pilot program
• Focus on specific subjects or grade levels
• Gather feedback and refine

Provide Support

• Create clear documentation
• Offer training sessions
• Establish help resources

Monitor and Adjust

• Regular system reviews
• Feedback collection
• Continuous improvement

Celebrate Success

• Share positive outcomes
• Recognise good practices
• Build community support

Looking Ahead

The future of education lies in our ability to thoughtfully integrate AI while maintaining the essence of human learning. These naming conventions are just one piece of that puzzle, but they represent our commitment to transparency, integrity, and effective pedagogy.

Remember:

• Clear documentation supports better learning
• Systematic approaches enable innovation
• Transparency builds trust
• Flexibility ensures longevity

We spend so much of our lives trying to navigate the complexities of modern education; we owe it to ourselves and our students to create systems that support clear understanding and growth.

Here's to building a future where human creativity and AI assistance can coexist transparently and productively. I look forward to sharing more insights in my upcoming explorations of AI in education!

Phil