Changes at "Resisting cognitive laziness: a DASHBOARD to make AI use visible and transform critical thinking"

Reboot Mental

ARPASANU-SUSOI Nicoleta-Roxana ; nicoleta-roxana.arpasanu-susoi@etu.univ-montp3.fr ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ARAMBURU Josela ; josela-ines.aramburu@etu.univ-montp3.fr ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ MIRANDA Anthony ; anthony.miranda@etu.umpv.fr ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀All students are from Univesité Paul Valéry

Training / education / pedagogy

This use case matters because generative AI is no longer a future scenario in higher education; it is already part of students’ everyday academic life. Recent evidence shows that AI use is now almost universal among students: 95% report using AI in at least one way, and 94% say they use generative AI to help with assessed work. AI is therefore not an external or occasional tool anymore. It is already woven into reading, writing, structuring ideas, explaining concepts and preparing assignments. At the same time, this normalisation creates a serious educational tension: students may become more efficient, but not necessarily more intellectually present. OECD evidence warns that when AI is used mainly to deliver direct answers, it can improve short-term task performance while reducing active engagement and weakening deeper learning. In other words, the problem is no longer whether students use AI, but whether their use of AI still supports thought, judgement and real understanding.⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀This is why the issue cannot be reduced to student behaviour alone. It concerns the whole triangle of student, university and Big Tech. On the student side, learners like Emma use AI because of academic overload, time pressure and the need for support, not simply because they want to avoid effort. But once AI produces fluent and plausible outputs, students can lose sight of what they actually verified, understood, reformulated or accepted too quickly. Our project starts from this precise point: the current problem is one of cognitive opacity. The student receives an apparently clean final answer, but the intellectual path becomes blurred. The dashboard matters because it makes visible what is usually invisible: source checking, detection of hallucinations, bias identification, reformulation, triangulation and reflection. It turns AI use from a hidden shortcut into a documented learning process.⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀On the university side, the problem is equally urgent. Many institutions are trying to respond, but often in fragmented or inconsistent ways. The HEPI survey shows that institutional encouragement around AI remains mixed, that students still feel uncertainty about acceptable use, and that there is a real anxiety around false accusations of cheating. At the same time, fewer than half of students feel their teaching staff are helping them develop AI skills for the future. This suggests a gap between the speed of AI adoption and the pedagogical support currently available. My reading of this is that many teachers are not simply resistant; they are often left without sufficiently clear, discipline-sensitive tools to guide students toward critical and pedagogical uses of AI. OECD work reinforces this concern: general-purpose systems such as ChatGPT or Claude are not designed around curriculum, pedagogy, learner modelling or teacher autonomy. So the challenge for universities is not just to regulate AI, but to reclaim a formative role: teaching students how to verify, question, compare, and think with AI rather than merely through it.⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀The Big Tech dimension is also central. Today, AI companies need better human feedback if they want to reduce hallucinations, improve factual reliability, and limit biased or stereotyped outputs. Our project is important because it imagines a more intelligent and more ethical relationship between students and AI companies. Instead of extracting value invisibly from user activity, the system would allow students to contribute voluntarily and under opt-in control the corrections they have genuinely produced: detected hallucinations, verified errors, identified biases, and domain-specific reformulations. In the project, these corrections are not random clicks; they are contextualised acts of critical work that could enrich RLHF pipelines with higher-quality educational feedback. This is especially valuable because student corrections are anchored in real disciplinary tasks, not abstract benchmark settings. Properly governed, this could help improve model quality while recognising students as contributors rather than passive consumers.⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀At the same time, this must not be framed naively. Big Tech does not just need more data; it needs better data and better human judgement. Hallucinations and stereotyped outputs are not fixed simply by scale. They require critical feedback, contextual interpretation, and human oversight. That is why our proposal insists on an ethical model: student control over data, explicit consent, pseudonymisation, and the possibility of keeping the cognitive report private rather than sharing it. The value of the project is precisely that it does not imagine the university surrendering its mission to platforms. On the contrary, it repositions the university as the place that trains critical supervisors of AI, while allowing scientifically useful feedback to flow toward model improvement only under conditions that protect human agency, privacy and pedagogical purpose.⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀So, this use case matters because it responds to a real structural transformation already underway. Students are already living with AI. Universities are under pressure to move beyond prohibition and build critical AI literacy. AI companies need high-quality human feedback to reduce errors and harmful biases. Our project sits exactly at this intersection. It proposes that the answer is not surveillance, punishment or blind adoption, but a new infrastructure of visibility: one that documents intellectual effort, strengthens critical thinking, supports teachers pedagogically, and channels human correction into AI improvement without sacrificing student sovereignty. That is why this is not just a dashboard. It is a proposal for a new social and pedagogical contract around AI in higher education.

Our team’s motivation has evolved significantly. At the beginning, we were imagining a relatively simple dashboard that would make AI use more visible. As the project matured, we realised that visibility alone was not enough. The real challenge is not just to visualise AI interaction, but to protect and strengthen the student’s cognitive agency, reduce the drift toward passive dependence, and redesign the relationship between learning, assessment and AI. That is why the project evolved from a tool-centred idea into a broader pedagogical and ethical architecture: an opt-in system, a cognitive coach, a one-page report controlled by the student, discipline-specific university training, and a governance model that keeps the university’s formative role intact instead of outsourcing it to platforms. Our learning objectives are therefore fourfold: to understand how AI changes students’ cognition and emotions; to design indicators that capture verification, transformation and reflection rather than mere productivity; to build an ethically robust model grounded in human agency, privacy and fairness; and to move from a punitive logic of suspicion toward a culture of documented, critical and teachable AI use. In that sense, our project today is not just “a dashboard” anymore: it is a proposal for how higher education could turn AI use from opacity into intellectual accountability and learner sovereignty.

Our initial contribution is the development of a pedagogical dashboard focused on a specific and highly realistic use of generative AI in higher education: the summarisation of academic content. The real idea of the project is not to build another AI assistant, but to create a device that makes visible the cognitive process that takes place when a student uses generative AI to summarise a text. ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀𝟭. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗥𝗘𝗔𝗟 𝗶𝗱𝗲𝗮 𝗼𝗳 𝘁𝗵𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁?