Changes at "Generative AI as a code tutor: catalyst for autonomy or trap of dependence?"
Title (Français)
- +Generative AI as a code tutor: catalyst for autonomy or trap of dependence?
Body (Français)
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- Team name
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+Virtual Generation
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- Team members (First name, LAST NAME, University)
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+Serigne Fallou Niang, Ndeye Sophie Seck, Fatou Bintou Ba ,Mame Abdoulaye Ndiaye, UN-CHK (Cheikh Hamidou Kane Digital University)
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- What area does your use case primarily fall under?
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+Training / education / pedagogy
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- The AI use case you are working on
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+At UN-CHK (Cheikh Hamidou Kane Digital University) , generative AI as a tutor is transforming distance learning, shifting between empowerment and passivity. Faced with isolation and a lack of support, a programming student uses assistants (ChatGPT, Copilot) to overcome obstacles. Our project proposes a structured pedagogical use of AI: it doesn't provide the solution, but guides the learning process. This intelligent tutoring strengthens autonomy and ensures active progress for the learner.
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- Why this use case matters
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+This situation deserves our attention because it represents a tipping point: AI is no longer simply a research tool, but a cognitive partner that is redefining the act of learning.
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- +Challenges and tensions:
- +Autonomy vs. Dependence: The challenge is to maintain the necessary cognitive effort. If AI does too much of the work, the student develops an illusion of knowledge without real mastery.
- +Reliability and hallucinations: A tutor who confidently makes mistakes can mislead the learner in a lasting way, especially in asynchronous contexts without immediate human verification.
- +Learning and cognition:
- +Opportunity: Reduced costs for students who do not have access to expensive private tutors.
- +Risk: A "two-tiered education" where some benefit from human teachers and others only from automated interfaces.
- +Social relationships and student life:
- +The risk of isolation increases. If AI answers everything, students will seek out their peers or teachers less, weakening the learning community.
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- +Teaching:
- +The role of the teacher is evolving from "transmitter of knowledge" to "architect of learning paths" and mediator of AI.
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- Your team's motivation and learning objectives
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+Our team is eager to tackle this challenge because we are witnessing a looming educational divide: on one hand, students who are enhancing their skills with AI; on the other, those who are using it as a crutch, losing all ability to solve complex problems.
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- +We hope to transform AI from a "solution generator" into a "cognitive mirror." This process will allow us to:
- +Better understand: the precise moment when AI assistance shifts from facilitation (relieving mental workload) to atrophy (stifling critical thinking).
- +Question: the relevance of current assessment methods in computer science. If AI can write code, what should we actually be teaching and grading?
- +Transform: the student's role, so that they move from passive code consumers to critical architects capable of validating and optimizing what AI proposes.
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- Your initial contribution
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+To transform AI from a "crutch" into a "catalyst for autonomy" in coding, our team identifies these types of critical resources:
- +Cognitive science experts: to understand "mental load" and define the point at which AI assistance hinders memorization and logical understanding.
- +Senior/professional developers: to identify the critical skills that AI cannot (yet) replace (architecture, security, code ethics) and that students must master independently.
- +Instructional designers: to help us transform a traditional coding course into an "AI-assisted code review" learning path.
- +Access to large language models (LLMs): using models like GPT-4 or Claude 3.5 Sonnet to test system prompts that force AI to remain in a tutoring role (never provide the complete code, respond with hints).
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- +Integrated Development Environments (IDEs): a sandbox environment where students code and AI intervenes only under specific conditions.
- +Partnership with a coding school or bootcamp: to conduct a comparative study (Group A: free AI vs. Group B: "restrictive tutor" AI).
- +Learning logs: access to anonymized chat histories to analyze when a student abandons their independent thinking to copy and paste a solution.
- +Digital ethics experts: to oversee the protection of student data and ensure that the algorithm does not favor certain coding styles over others.
- +The ultimate goal is to create an AI tutoring protocol that could be adopted by universities.
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