PHUONG BAO TRAN NGUYEN ,TRAN NAM PHUONG NGUYEN
DOI: https://doi.org/ChatGPT and similar large language models hold transformative potential for education, serving as tutors, writing assistants, and administrative aids. However, their deployment raises critical ethical questions. This paper provides an in-depth analysis of ethical and responsible AI use in education, focusing on ChatGPT. We review current ethical frameworks guiding AI in education, such as UNESCO’s human-centered principles and industry initiatives, and examine key concerns including bias and fairness in grading and tutoring, transparency and explainability of model outputs, and data privacy. Technical measures for responsible use are discussed, from prompt-level content filtering and red-teaming to bias evaluations and user-guided policies. We highlight case studies like Khan Academy’s Khanmigo pilot, which illustrate practical approaches (e.g. Socratic tutoring, audit logs, age limits) to ensure AI tools benefit students equitably while mitigating risks. Our findings indicate broad consensus on core values – fairness, non-discrimination, transparency, privacy, and accountability – but also reveal ongoing challenges in practice. The paper concludes with recommendations for multi-stakeholder collaboration, continuous model auditing, and the importance of maintaining human oversight in AI-assisted education to ensure these technologies enhance learning in an ethical, inclusive manner.
Purpose: Generative AI is rapidly moving into educational practice, raising urgent questions about fairness, transparency, data protection, and instructional integrity. This article proposes a sector‑attuned framework for responsible classroom AI and synthesizes emerging evidence on guardrails for ChatGPT‑style tools.
Method: We conducted a structured scoping review of peer‑reviewed and gray literature (higher education and K‑12), searching major education and social‑science databases (e.g., ERIC, Scopus, Web of Science) and policy repositories between January 2023 and August 2025. Screening followed PRISMA principles (transparent criteria, dual screening where feasible, and data charting of policy, risks, and safeguards). We complemented the review with a targeted case analysis of a supervised tutoring deployment (Khanmigo) and triangulated with institutional guidance documents.
Findings: Across both sectors, the most consistently endorsed practices are: human‑in‑the‑loop supervision, explicit disclosure/norms of use, bias and toxicity checks before rollout, data‑minimization and age‑appropriate access, assessment redesign to reduce detector reliance, and audit‑friendly logging/appeals. Higher education foregrounds data governance and integrity; K‑12 foregrounds child protection and teacher mediation.
Contribution: We offer (i) a consolidated policy framing aligned with international guidance, (ii) a cross‑walk from risks to implementable safeguards, (iii) a short case table mapping features to guardrails, and (iv) a practitioner toolkit (checklist, model course policy, and a minimum‑viable guardrails table). We conclude with an adoption pathway for institutions.
