Correction: Ethical and regulatory challenges of Generative AI in education: a systematic review
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".