Changes at "GenAI Did Not Break Academia, It Revealed It: A Proposal for Structural Reform and Responsible AI Integration"
Body (Français)
- Team name
- Lobby D
- Team members (First name, LAST NAME, University)
- Adam DEBBAH, CentraleSupélecMalo USELLE, CentraleSupélecMahdi AYADI, CentraleSupélec
- What area does your use case primarily fall under?
- Daily life / student life / campus
- The AI use case you are working on
- We aim to redesign academic assessments and curricula to address the rise of generative AI and we want to properly integrate a controlled use of AI into the academic world with all the stakeholders(teachers, students and academic institutions). The project focuses on shifting from traditional "homework" to oral presentations and interactive defense of knowledge. This involves a deliberate simulation of a classroom where AI is integrated into the research phase, but the evaluation focuses on "raw sciences" and fundamental reasoning. Students and faculty collaborate to ensure that AI serves as a tool for deepening, rather than replacing, core intellectual labor. Controlled use of AI can be the usage of AI with a watermark AI model (with a private key shared between the ministry of education and that AI companies) so that AI can be used but the usage of it is assumed, AI to help schools and students to make better choices of courses and curricilums. Promoting the use of AI as teachers would be a first step and developing tailor-made AI teachers for homework would be great (with teacher monitoring the usage).
- Why this use case matters
- Current assessment models are struggling with the ubiquity of AI, leading to a potential decline in foundational knowledge, what we call "raw sciences." This situation creates a tension between efficiency and deep cognitive engagement. If students rely solely on AI for outputs, they risk losing the ability to perform first-principles thinking. This project is crucial for maintaining academic integrity and equity, ensuring that the "spirit of the curriculum" evolves to foster critical human intelligence and sustainability in learning rather than mere automation.
- Your team's motivation and learning objectives
- Our team is motivated by the desire to rehabilitate "pure" scientific reasoning in an AI-saturated world. We want to move beyond the binary of "banning" or "fully outsourcing" tasks to AI. Through this challenge, we hope to understand how to transform educational expectations to better reflect human cognitive strengths. Our goal is to question the current drift toward intellectual passivity and develop a framework where technology strengthens, rather than erodes, the student's mastery of fundamental concepts.
- Your initial contribution
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-Read our essay in the following link:
- -https://drive.google.com/file/d/1QJbU1ln1xTvMRMskOIirvLO2dEIQhtpl/view?usp=sharing
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+Our final contribution takes the form of a formal academic paper, a significant evolution from our first essay submission.
- +While the philosophical core remains the same, we have restructured and deepened the work considerably:
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- +- The regulatory and technical proposals from the first contribution have been dropped entirely in favor of two focused, actionable propositions:
- +(1) a reform of academic assessment toward oral and live evaluation formats that cannot be delegated to AI, and
- +(2) the integration of machine learning into institutional feedback infrastructure to give schools the tools to identify and respond to student disengagement in real time.
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- +- We conducted an original student survey distributed across our networks and social media, collecting 139 responses, to ground our theoretical hypotheses in first-hand empirical data; we present this as a first initiative and call for a larger-scale study, ideally spanning multiple institutions and countries, that could produce the representative data needed to inform real policy reform.
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- +- The paper now includes a related work section grounding our thinking in existing scholarship, a theoretical framework built around our own hypotheses on the root causes of AI misuse in academia, and a methodology section presenting the results of a student survey we conducted across our networks.
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- +We are aware of the limitations of our data and say so honestly. What we stand behind is the diagnosis: academic dishonesty is a structural problem, and any response that does not address the structure will not last.
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- +Find the full contribution in the following link:
- +https://drive.google.com/file/d/1grIsJBUm99-PVuf-2zNLgzQ6arKVVSC6/view?usp=sharing
- +
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