arXiv subjects

Hélène Sauzéon

Publications and source records attributed to Hélène Sauzéon.

9 recordsLinked to original sources

arXiv2026

Curiosity and Metacognition: Towards a Unified Framework for Learning and Education in the Age of AI

This chapter examines the relationship between curiosity and metacognition as critical drivers of autonomous and self-regulated learning. We synthesize recent research to propose a unified framework integrating behavioral, computational, and psychoeducational dimensions, arguing that curiosity, i.e. the intrinsic drive to acquire new knowledge, relies fundamentally on metacognitive monitoring and control. From an educational perspective, we evaluate interventions designed to enhance curiosity in classroom settings. While promising, our review indicates that these interventions yield mixed results, often proving differentially effective for struggling learners, thereby underscoring the necessity for approaches tailored to individual profiles. Finally, we address the paradigm shift introduced by Generative AI. While Large Language Models (LLMs) offer unprecedented scalability for personalized inquiry, we argue that their default interaction modes pose significant risks to the dynamics of curiosity-driven learning. To mitigate these challenges, we review strategies to transform AI from a potential cognitive shortcut into a powerful partner for sustained epistemic development.

cs.CY↗

arXiv2026

The Illusion of Understanding: How Middle-Schoolers Fail to Regulate Inquiry with ChatGPT in a Science Task

Generative AI (GenAI) tools allow for effortless task completion, potentially fostering cognitive and metacognitive laziness in students. While surveys indicate widespread GenAI use among students as young as 11, their interactions strategies remain under-explored. A critical indicator of these interactions' quality is the ability to lead Question-Asking (QA) cycles: initiating goal-oriented inquiries, critically evaluating AI responses, and regulating subsequent strategies. While these behaviors predict robust learning in traditional settings, their role in AI-mediated environments remains unclear. Addressing this gap, this study investigates middle school students' (N=63, aged 14--15) capacity to adopt these behaviors with GenAI during science investigation tasks. We analyzed their proficiency in distinguishing efficient goal-oriented prompt from inefficient ones, their critical evaluation of AI responses, and their ability to generate follow-up questions to regulate learning in alignment with their informational needs. Findings reveal a pattern of over-reliance: students struggled to discriminate between prompt types, failed to detect vague AI explanations, and frequently terminated inquiry prematurely, without follow-up. Consequently, task performance remained moderate despite unrestricted AI access and high self-reported prior knowledge. Notably, positive AI attitudes were negatively associated with interaction quality, suggesting a disconnect between perceived and actual competence, whereas higher metacognitive skills predicted superior sensitivity to prompt quality. These results underscore the necessity for AI literacy interventions that move beyond technical understanding to explicitly train metacognitive regulation strategies, required for meaningful and sustainable QA-based learning with GenAI.

cs.CY↗

arXiv2025

Improved Performances and Motivation in Intelligent Tutoring Systems: Combining Machine Learning and Learner Choice

Large class sizes challenge personalized learning in schools, prompting the use of educational technologies such as intelligent tutoring systems. To address this, we present an AI-driven personalization system, called ZPDES, based on the Learning Progress Hypothesis - modeling curiosity-driven learning - and multi-armed bandit techniques. It sequences exercises that maximize learning progress for each student. While previous studies demonstrated its efficacy in enhancing learning compared to hand-made curricula, its impact on student motivation remained unexplored. Furthermore, ZPDES previously lacked features allowing student choice, a limitation in agency that conflicts with its foundation on models of curiosity-driven learning. This study investigates how integrating choice, as a gamification element unrelated to exercise difficulty, affects both learning outcomes and motivation. We conducted an extensive field study (265 7-8 years old children, RCT design), comparing ZPDES with and without choice against a hand-designed curriculum. Results show that ZPDES improves both learning performance and the learning experience. Moreover adding choice to ZPDES enhances intrinsic motivation and further strengthens its learning benefits. In contrast, incorporating choice into a fixed, linear curriculum negatively impacts learning outcomes. These findings highlight that the intrinsic motivation elicited by choice (gamification) is beneficial only when paired with an adaptive personalized learning system. This insight is critical as gamified features become increasingly prevalent in educational technologies.

cs.CY↗

arXiv2024

Interactive environments for training children's curiosity through the practice of metacognitive skills: a pilot study

Curiosity-driven learning has shown significant positive effects on students' learning experiences and outcomes. But despite this importance, reports show that children lack this skill, especially in formal educational settings. To address this challenge, we propose an 8-session workshop that aims to enhance children's curiosity through training a set of specific metacognitive skills we hypothesize are involved in its process. Our workshop contains animated videos presenting declarative knowledge about curiosity and the said metacognitive skills as well as practice sessions to apply these skills during a reading-comprehension task, using a web platform designed for this study (e.g. expressing uncertainty, formulating questions, etc). We conduct a pilot study with 15 primary school students, aged between 8 and 10. Our first results show a positive impact on children's metacognitive efficiency and their ability to express their curiosity through question-asking behaviors.

cs.CY↗

arXiv2023

Generative AI in the Classroom: Can Students Remain Active Learners?

Generative Artificial Intelligence (GAI) can be seen as a double-edged weapon in education. Indeed, it may provide personalized, interactive and empowering pedagogical sequences that could favor students' intrinsic motivation, active engagement and help them have more control over their learning. But at the same time, other GAI properties such as the lack of uncertainty signalling even in cases of failure (particularly with Large Language Models (LLMs)) could lead to opposite effects, e.g. over-estimation of one's own competencies, passiveness, loss of curious and critical-thinking sense, etc. These negative effects are due in particular to the lack of a pedagogical stance in these models' behaviors. Indeed, as opposed to standard pedagogical activities, GAI systems are often designed to answers users' inquiries easily and conveniently, without asking them to make an effort, and without focusing on their learning process and/or outcomes. This article starts by outlining some of these opportunities and challenges surrounding the use of GAI in education, with a focus on the effects on students' active learning strategies and related metacognitive skills. Then, we present a framework for introducing pedagogical transparency in GAI-based educational applications. This framework presents 1) training methods to include pedagogical principles in the models, 2) methods to ensure controlled and pedagogically-relevant interactions when designing activities with GAI and 3) educational methods enabling students to acquire the relevant skills to properly benefit from the use of GAI in their learning activities (meta-cognitive skills, GAI litteracy).

cs.CY↗

arXiv2023

GPT-3-driven pedagogical agents for training children's curious question-asking skills

In order to train children's ability to ask curiosity-driven questions, previous research has explored designing specific exercises relying on providing semantic and linguistic cues to help formulate such questions. But despite showing pedagogical efficiency, this method is still limited as it relies on generating the said cues by hand, which can be a very costly process. In this context, we propose to leverage advances in the natural language processing field (NLP) and investigate the efficiency of using a large language model (LLM) for automating the production of the pedagogical content of a curious question-asking (QA) training. We study generating the said content using the "prompt-based" method that consists of explaining the task to the LLM in natural text. We evaluate the output using human experts annotations and comparisons with hand-generated content. Results suggested indeed the relevance and usefulness of this content. We also conduct a field study in primary school (75 children aged 9-10), where we evaluate children's QA performance when having this training. We compare 3 types of content : 1) hand-generated content that proposes "closed" cues leading to predefined questions; 2) GPT-3-generated content that proposes the same type of cues; 3) GPT-3-generated content that proposes "open" cues leading to several possible questions. We see a similar QA performance between the two "closed" trainings (showing the scalability of the approach using GPT-3), and a better one for participants with the "open" training. These results suggest the efficiency of using LLMs to support children in generating more curious questions, using a natural language prompting approach that affords usability by teachers and other users not specialists of AI techniques. Furthermore, results also show that open-ended content may be more suitable for training curious question-asking skills.

cs.CL↗

arXiv2022

Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation

Large Language Models (LLMs) have in recent years demonstrated impressive prowess in natural language generation. A common practice to improve generation diversity is to sample multiple outputs from the model. However, there lacks a simple and robust way of selecting the best output from these stochastic samples. As a case study framed in the context of question generation, we propose two prompt-based approaches to selecting high-quality questions from a set of LLM-generated candidates. Our method works under the constraints of 1) a black-box (non-modifiable) question generation model and 2) lack of access to human-annotated references -- both of which are realistic limitations for real-world deployment of LLMs. With automatic as well as human evaluations, we empirically demonstrate that our approach can effectively select questions of higher qualities than greedy generation.

cs.CL↗

arXiv2022

Conversational agents for fostering curiosity-driven learning in children

Curiosity is an important factor that favors independent and individualized learning in children. Research suggests that it is also a competence that can be fostered by training specific metacognitive skills and information-searching behaviors. In this light, we develop a conversational agent that helps children generate curiosity-driven questions, and encourages their use to lead autonomous explorations and gain new knowledge. The study was conducted with 51 primary school students who interacted with either a neutral agent or an incentive agent that helped curiosity-driven questioning by offering specific semantic cues. Results showed a significant increase in the number and the quality of the questions generated with the incentive agent. This interaction also resulted in longer explorations and stronger learning progress. Together, our results suggest that the more our agent is able to train children's curiosity-related metacognitive skills, the better they can maintain their information-searching behaviors and the more new knowledge they are likely to acquire.

cs.HC↗

arXiv2022

Language-biased image classification: evaluation based on semantic representations

Humans show language-biased image recognition for a word-embedded image, known as picture-word interference. Such interference depends on hierarchical semantic categories and reflects that human language processing highly interacts with visual processing. Similar to humans, recent artificial models jointly trained on texts and images, e.g., OpenAI CLIP, show language-biased image classification. Exploring whether the bias leads to interference similar to those observed in humans can contribute to understanding how much the model acquires hierarchical semantic representations from joint learning of language and vision. The present study introduces methodological tools from the cognitive science literature to assess the biases of artificial models. Specifically, we introduce a benchmark task to test whether words superimposed on images can distort the image classification across different category levels and, if it can, whether the perturbation is due to the shared semantic representation between language and vision. Our dataset is a set of word-embedded images and consists of a mixture of natural image datasets and hierarchical word labels with superordinate/basic category levels. Using this benchmark test, we evaluate the CLIP model. We show that presenting words distorts the image classification by the model across different category levels, but the effect does not depend on the semantic relationship between images and embedded words. This suggests that the semantic word representation in the CLIP visual processing is not shared with the image representation, although the word representation strongly dominates for word-embedded images.

cs.CV↗