The Dynamics of the Self-Regulation Process in Student-AI Interactions
Unlocking the Dynamics of Student-AI Problem Solving
Generative Artificial Intelligence (AI) has demonstrated significant value in code generation and support for programming tasks, leading to its widespread adoption in both industry and academia. This proliferation has introduced new opportunities and risks for learning, teaching, and the broader landscape of computer science education. Despite this rapid integration, there remains limited understanding of how students engage with these tools, particularly in terms of collaboration, dependence, and delegation.
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Student-AI Interactions Analyzed
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Students Participating
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Total Codes Identified
Revealing the Dynamics of Student-AI Problem Solving
Our analysis of student-AI interactions in programming education reveals a predominant focus on surface-level task completion over deeper metacognitive strategies, leading to concerns about cognitive offloading and undermining the development of independent learning.
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Common 'Inspect and Adapt' Prompts
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'Test Case' Evaluations
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SRL Explanation Instances
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126
Occurrences of 'Rephrasing Problem Statement'
Students frequently initiated interactions by rephrasing the problem statement (126 occurrences) or providing assignment instructions. However, deeper engagement with 'Problem Context' (8 occurrences) or detailed 'Problem Specification' (15 occurrences) was notably less common, suggesting a focus on prompt initiation rather than thorough problem understanding.
Students heavily utilized iterative conversations to refine their requests, with 168 instances of specific prompts and 110 instances of varied abstraction levels. This indicates a consistent engagement in back-and-forth dialogue to evolve their solutions, often forming feedback loops between instructions and AI responses.
Typical Iterative Problem-Solving Workflow
Instruct
→
AI Response
→
Monitor
→
Refine
→
AI Response (Updated)
648
Occurrences of refining AI solutions ('Inspect and Adapt')
0
Explicit 'Test Case' Evaluations
Learners predominantly relied on 'Inspect and Adapt' (648 occurrences) to refine AI-generated solutions, highlighting a focus on reactive adjustments. The complete absence of 'Evaluation Using Test Cases' (0 occurrences) and limited 'Solution Explanation' (37 occurrences) suggest a lack of deeper evaluative practices.
| SRL Phase | Observed Student-AI Interaction | Theoretical SRL Expectation |
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| Planning |
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| Monitoring |
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| Reflection |
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The Reactive Problem-Solving Loop
The dominant observed behavior was a reactive loop: students would request a high-level feature, copy and paste the AI-generated code, test it locally, and if it failed, report back to the AI by pasting the error message or asking for an adjustment. This cycle shows strong transitions from monitoring to seeking an AI response and from refining to an AI response, but it bypasses deeper cognitive engagement, limiting opportunities for adaptive expertise.
Critical Warning: Cognitive Offloading
This 'cognitive offloading' is alarming. As AI becomes a more dominant presence in online self-practice, there is a growing risk that learners will default to surface-level engagement, avoiding the deliberate effort required for critical thinking, problem-solving, and long-term retention. Without targeted interventions or design strategies, AI use in learning may inadvertently reinforce a dependency model that stifles cognitive growth and undermines independent learning.
The study revealed that student self-regulation was largely confined to task performance activities, primarily instructing, refining, and problem specification. This finding highlights a significant divergence from theoretical models of Self-Regulated Learning, which envision a more balanced and deeply engaged participation across all SRL phases during problem-solving with AI.
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