Co-Constructing AI Boundaries Framework Component - Reflection

Definition in This Study

Reflection refers to evidence of metacognition, ethical reasoning, boundary articulation, and epistemic stance in students' explicit commentary about their AI interactions. This component captures the metacognitive boundary—students' own words about who is responsible for what in the final knowledge product.

Mollick & Mollick (2023) Connection

The Reflection component echoes M&M's explicit requirement for structured reflection:

• AI as Coach is defined as "Prompt metacognition"
• Pedagogical benefit: "Opportunities for reflection and regulation, which improve learning outcomes"
• AI as Mentor guidelines: "Share your complete interactions with the AI... What are some of your takeaways in working with the AI?"

Key M&M Principle: Structured reflection is essential for learning and ethical accountability.

M&M emphasize that students must articulate their thought processes, decision-making, and learning from AI interactions—not just use AI and move on.

What This Component Analyzes

Primary Focus

• Metacognitive awareness: Understanding of own thinking and decision-making
• Boundary articulation: Explicit statements about human vs. AI roles and responsibilities
• Ethical reasoning: Considerations of appropriate use, transparency, integrity
• Epistemic stance: How students position themselves as knowers and knowledge creators

Secondary Focus

• Learning about AI capabilities and limitations
• Evolution of thinking about AI use
• Future implications for teaching practice

Types of Reflection

1. Process Reflection (Metacognitive)

Focus: How did I work with AI?

• "I started by asking AI to summarize, but realized I needed to ask more critical questions"
• "When the output was superficial, I revised my prompt to be more specific"
• "I used AI as a brainstorming partner, not a writer"

2. Product Reflection (Evaluative)

Focus: What did AI contribute to my final work?

• "AI helped me organize my thinking, but the analysis is my own"
• "I rejected the AI's framing because it was too deficit-oriented"
• "The AI gave me a starting point, but I had to completely rewrite it"

3. Boundary Reflection (Responsibility)

Focus: Who did what? Who is responsible?

• "The AI suggested arguments, but I'm responsible for evaluating them"
• "I made the final decisions about what to include"
• "The synthesis is mine; AI just helped me see connections"

4. Ethical Reflection (Accountability)

Focus: Was my use appropriate? Transparent? Fair?

• "I disclosed all AI use in my references"
• "I made sure not to over-rely on AI for critical thinking"
• "I considered whether using AI was appropriate for this task"

5. Epistemic Reflection (Authority)

Focus: How do I view knowledge and AI's role in creating it?

• "AI can suggest ideas, but I need to verify them against sources"
• "I don't trust AI as an authority on critical theory"
• "I'm the expert on my own teaching context, not the AI"

6. Learning Reflection (Growth)

Focus: What did I learn about working with AI?

• "I learned that my prompts need to be much more specific"
• "I realized AI can reinforce biases if I'm not careful"
• "This process taught me to trust my own expertise more"

Agency and Reflection

Reflection reveals Agency through:

• High agency: "I used AI strategically to accomplish specific goals"
• Low agency: "AI did most of the work" (language of outsourcing)
• Metacognitive agency: Awareness of own decision-making and control

Boundary-work in Reflection

Students engage in Boundary-work by explicitly articulating:

• Role boundaries: "AI's job was X; my job was Y"
• Responsibility boundaries: "I'm accountable for the final product"
• Trust boundaries: "I verified AI outputs against reliable sources"
• Ethical boundaries: "I disclosed AI use and ensured academic integrity"

Epistemic Stance in Reflection

Reflection reveals Epistemic Stance through language patterns:

Key Analytic Questions

When coding Reflections, ask:

1. Metacognitive Awareness:

   • Does student demonstrate awareness of their own thinking?
   • Can they articulate their decision-making process?

2. Boundary Clarity:

   • Are human vs. AI contributions clearly delineated?
   • Does student claim ownership of final product?

3. Ethical Reasoning:

   • Does student consider appropriate use?
   • Is there evidence of transparency and integrity?

4. Epistemic Positioning:

   • How does student position themselves relative to AI?
   • Who is the authority on knowledge?

5. Learning:

   • What did student learn about AI use?
   • How has their thinking evolved?

Examples from Data

High-Quality Reflection: Metacognitive + Ethical + Epistemic

"I initially asked NotebookLM to analyze the readings, but
the response was too surface-level and missed the critical
perspectives from scholars of color. I realized I needed to
prompt it more specifically and provide additional sources.
Even then, I had to heavily revise the output because the
AI's language was too neutral—it didn't capture the urgency
of the issues. I learned that AI can help me organize my
thinking, but I'm responsible for the critical lens and the
final argument. For me, AI is a thinking partner, not an
authority."

Analysis:

• Metacognitive: Describes thought process and strategy revision
• Boundary-work: Clear about AI limitations and human responsibility
• Ethical: Aware of critical perspectives and bias
• Epistemic: Positions self as authority; AI as tool
• Learning: Articulates growth in understanding

Medium-Quality Reflection: Process Focus

"I used AI to help me summarize the articles and organize
my thoughts. It was helpful to get a starting point, but I
had to add my own analysis and examples from my teaching
experience."

Analysis:

• Metacognitive: Basic process description
• Boundary-work: Distinguishes AI (summary) from human (analysis)
• Limited depth: Doesn't explore ethical or epistemic dimensions

Low-Quality Reflection: Minimal Metacognition

"AI was helpful for this project."

Analysis:

• No metacognition: No process description
• No boundary-work: Unclear what AI did vs. what student did
• No learning: No articulation of growth or insight

Concerning Reflection: Over-Delegation

"AI wrote most of the paper. I just edited it for grammar."

Analysis:

• Low agency: AI positioned as author
• Minimal boundary-work: Student relegated to editing role
• Problematic epistemic stance: AI as knowledge producer

Coding Categories for Reflection

Relationship to Other Framework Components

Reflection is the culminating metacognitive layer that overlays all other components:

• Reflects on Co-Constructing AI Boundaries Framework Component - Inputs: "I chose these sources because..."
• Reflects on Co-Constructing AI Boundaries Framework Component - Prompts: "I learned to ask more specific questions"
• Reflects on Co-Constructing AI Boundaries Framework Component - Outputs: "I noticed the AI's output was biased"
• Reflects on Co-Constructing AI Boundaries Framework Component - Integration: "I rewrote the AI text in my own voice"

Reflection makes visible the thinking underlying the other four components.

The Essential Questions Reflection Should Answer

M&M's framework implicitly requires students to answer:

1. What did you learn? (Learning outcome)
2. Who is responsible? (Accountability)
3. What are your takeaways? (Metacognition)
4. How did you ensure quality? (Oversight)
5. What are the ethical implications? (Ethics)

High-quality reflection addresses multiple dimensions.

Pedagogical Implications

Teaching reflective practice with AI:

1. Provide structured reflection prompts
2. Model reflective thinking (instructor examples)
3. Build reflection into assignment sequence (not just at the end)
4. Create space for ethical reasoning discussions
5. Encourage boundary articulation (who did what?)
6. Value metacognitive awareness in assessment
7. Use reflection to assess AI literacy development

Reflection Prompts to Use:

• "Describe your process of working with AI"
• "What did AI contribute vs. what did you contribute?"
• "How did you ensure the quality of AI outputs?"
• "What did you learn about AI's limitations?"
• "Who is responsible for the final product? Why?"
• "How did you maintain academic integrity?"

Data Collection Notes

Where to find evidence:

• Formal reflection assignments (most direct)
• Project narratives (process descriptions)
• NotebookLM chat (informal metacommentary during work)
• Final project notes (commentary on AI use)
• Class discussions (verbal reflections)

What to look for:

• Explicit statements about AI use
• Articulation of boundaries and responsibilities
• Ethical reasoning
• Learning and growth statements
• Epistemic positioning language

Reflection as Data for Triangulation

Reflection serves a dual purpose:

1. Component in itself (evidence of metacognition)
2. Validation data for other components (confirms or contradicts observed behaviors)

Example:

• Observed in Integration: Heavy transformation
• Reflection: "I rewrote everything in my own voice"
• Triangulation: Reflection validates integration observation

Related Notes

• Analytic Framework for AI Human Meaning-Making Practices
• How learners should engage Large Language Models framework
• Agency
• Boundary-work
• Epistemic Stance