Analytic Framework for AI Human Meaning-Making Practices

M&M Adaptation for AI-Literacy Research

This analytic framework is the methodological lens for examining how preservice literacy educators engaged with AI (specifically, NotebookLM) as cognitive partners during authentic inquiry. It traces the full arc of human–AI collaboration, highlighting Agency, Boundary-work, and ethical reasoning.

🎯 Positioning Your Framework

To trace how future literacy educators engaged with AI as cognitive partners, I adapted the pedagogical structure from Mollick & Mollick’s (2023) Assigning AI: Seven Approaches into a research-oriented framework:

• Inputs – How students framed the task and curated sources
• Prompts – How they directed or constrained the AI
• Outputs – How the model responded
• Integration – How students transformed, rejected, or incorporated AI output
• Reflection – How they articulated boundaries, ethics, and epistemic stance

This framework allowed me to analyze authentic AI–human meaning-making practices, highlighting where Agency, Boundary-work, and ethical reasoning emerged.

🔑 Key Concepts

This framework centers on three interconnected concepts:

• Agency – Student capacity for intentional decision-making and control over AI interactions
• Boundary-work – Active negotiation of boundaries between human and AI contributions
• Epistemic Stance – How students position themselves as knowledge constructors vs. consumers

🔗 Framework Components (The Research Lens)

The five steps of this framework are each grounded in specific pedagogical guidance from Mollick & Mollick, but are adapted here as a qualitative analytic lens.

1. Inputs – What students uploaded, curated, or selected as context
   • M&M Connection: Emphasis on providing context and goal-setting.
   • Co-Constructing AI Boundaries Framework Component - Inputs
2. Prompts – How students directed, constrained, or collaborated with the model
   • M&M Connection: Mirrors the function of "assignment roles" (Tutor, Coach, etc.).
   • Co-Constructing AI Boundaries Framework Component - Prompts
3. Outputs – What the model generated and how students evaluated it
   • M&M Connection: Corresponds to cautions about hallucination, bias, and necessity for human oversight.
   • Co-Constructing AI Boundaries Framework Component - Outputs
4. Integration – How students incorporated, revised, resisted, or transformed AI outputs
   • M&M Connection: Resonates with the mandate to "remain the human in the loop."
   • Co-Constructing AI Boundaries Framework Component - Integration
5. Reflection – Evidence of metacognition, ethical reasoning, boundaries, and epistemic stance
   • M&M Connection: Echoes the explicit requirement for structured reflection in the Coach/Mentor roles.
   • Co-Constructing AI Boundaries Framework Component - Reflection

📚 Foundational Justification

The detailed theoretical argument for this adaptation is housed here:
How learners should engage Large Language Models framework

📖 Supporting Materials

• Co-Constructing AI Boundaries Research Methods Justification – Methodological rigor and approach
• Tracing the AI-Human Conversation Framework – Additional framework analysis
• Co-constructing AI Boundaries – Conceptual notes on co-construction

🏠 Related

• LRA 2025 Presentation – Conference presentation context