Think or Delegate? Decision-Making in the Age of AI

ENS Team

Juliette Petillot, École normale supérieure Sébastien Brion, École normale supérieure India Sachdev, École normale supérieure Paul Landrier, École normale supérieure Souleymane Faye, École normale supérieure

Training / education / pedagogy

Samuel, an M1 student aiming for a research career, writes his thesis with AI. At each step, he must decide: think, or delegate? AI becomes an invisible co-author shaping his reasoning, choices, and confidence. This case explores how students arbitrate between autonomy and automation, revealing distinct decision-making profiles and their cognitive consequences on learning.

This case challenges a blind spot in higher education: AI does not just assist students, it subtly reshapes how they decide to think. Behind similar outputs lie radically different cognitive paths—outsourced reasoning, guided thinking, or critical resistance. This raises a key tension: is AI augmenting intelligence, or displacing it? The stakes go beyond performance to affect autonomy, deep learning, and epistemic confidence. It also creates invisible inequalities between students who master AI reflexively and those who depend on it. Understanding these dynamics is crucial to design pedagogical frameworks that preserve agency while embracing AI.

We’ve all started using AI in our studies, sometimes to save time, sometimes to get unstuck, and sometimes without really thinking about what it changes. This project is a way for us to take a step back and understand what’s actually happening when we rely on it. We want to explore how it affects the way we think, make decisions, and learn, and why some of us use it very differently than others. More than judging AI, we want to question our own habits and figure out how to use it without losing what really matters in learning.

Problem framing: AI in higher education is mostly evaluated through outputs (performance, cheating), while its deeper impact remains overlooked: it reshapes how students decide to think. The key shift is not what students produce but how they arbitrate between thinking, delegating, or co-constructing with AI. Use case: We study an ENS student writing an M1 thesis. Throughout the process, AI can generate ideas, structure arguments, or rewrite content. Behind similar outputs, very different cognitive paths emerge depending on how the student engages with AI. Hypothesis - AI use creates distinct decision-making profiles: - Delegation - Optimization - Co-construction - Resistance These profiles lead to different levels of autonomy, critical thinking, and learning depth. Proposed contribution - We propose a decision-awareness framework: - Help students identify their AI use profile - Make decision processes visible - Provide adapted guidelines for more reflective use - Enable institutions to design differentiated pedagogical responses The goal is not to limit AI, but to restore agency in learning. Methodology: - Student surveys at ENS - Interviews with AI & ethics researchers - Literature review This will allow us to test and refine our typology and framework. Expected impact: - Self-assessment tool for students - Basis for more nuanced AI policies By shifting the focus from usage to decision-making, we aim to support more conscious and equitable learning practices.