Correction: Ethical and regulatory challenges of Generative AI in education: a systematic review
Notice bibliographique
Résumé
Introduction: Generative Artificial Intelligence (GenAI) is transforming education by enabling personalized learning and more efficient teaching practices. However, it raises critical ethical concerns, including data privacy, algorithmic bias, and educational inequality, requiring comprehensive regulatory frameworks and pedagogical strategies.Methods: A Systematic Literature Review (SLR) was conducted, analyzing 53 peer-reviewed articles published between 2020 and 2024. The search was performed in Scopus and Web of Science using defined inclusion criteria focused on GenAI applications in education. Data were synthesized thematically and supported by theoretical frameworks from ethics, regulation, and learning sciences.Results: The findings reveal that while GenAI enhances personalized feedback, instructional automation, and learning accessibility, it simultaneously introduces risks such as loss of cognitive autonomy, institutional misuse of student data, and lack of regulatory oversight. Case studies from Stanford and the University of Toronto illustrate both opportunities and limitations of GenAI adoption in higher education.Discussion: GenAI can benefit education if implemented within ethical, legal, and pedagogical boundaries. The study highlights the urgency of designing inclusive regulatory frameworks, strengthening digital literacy, and integrating GenAI tools with constructivist and self-determined learning models. This review offers practical recommendations for educators, policymakers, and technologists aiming to use GenAI responsibly in educational environments.GenAI refers to AI systems capable of creating new content, such as text, images, and even educational materials, based on patterns learned from vast datasets. Unlike traditional AI models that focus on prediction and classification, GenAI actively engages in knowledge generation, making it a transformative tool in education. However, its unique characteristics also introduce ethical, regulatory, and pedagogical challenges that require careful evaluation. This study explores both the opportunities and risks of GenAI, considering its potential to personalize learning while addressing concerns related to academic integrity, algorithmic bias, and equitable access to technology.The research question guiding this study is: What are the ethical challenges, regulatory frameworks, and opportunities for improvement in educational quality associated with the implementation of GenAI in education? The importance of this question lies in the need to provide empirical evidence and structured analysis on a topic that, although emerging, has a direct impact on the future of global education (Camacho-Zuñiga et al., 2024). This proposes a systematic approach that contrasts with previous studies, allowing a more in-depth and contextualized analysis.Unlike other similar studies and reviews that have limited themselves to exploring isolated aspects, such as the technical benefits of GenAI or its overall impact on education, this work offers a comprehensive and comparative approach. The trends identified in the graphs generated from the analysis of keywords are highlighted, which were contextualized with recent studies to confirm their validity and relevance. In addition, a detailed discussion is provided that links the quantitative findings with the theoretical constructs, allowing to identify existing gaps in the literature and suggest strategies to address the remaining challenges (Gajjar, 2024). The approach sets the stage for a more thorough assessment of the regulatory, ethical and educational aspects of GenAI.This study takes a comprehensive approach, combining quantitative and qualitative analysis to examine the ethical, regulatory, and educational challenges of generative AI in education. Unlike earlier research that looked at these areas separately, this paper offers a holistic perspective that ties empirical trends to strong theoretical frameworks. It also provides practical guidance for educators, policymakers, and technologists aiming to implement GenAI ethically and effectively in learning environments.Through an SLR, it was possible to synthesize empirical and conceptual evidence that reinforces the relevance of key issues such as privacy, equity, and legislative adaptability. This approach not only validates observed trends, but also connects these findings with concrete proposals to ensure ethical and efficient use of GenAI (Camacho-Zuñiga et al., 2024; Wu and Wang, 2024). In addition, the importance of developing dynamic and collaborative regulatory frameworks that balance technological innovation with the protection of individual rights is emphasized.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».