As AI becomes more embedded in education, urgent ethical concerns—bias, privacy, transparency—demand scholarly attention. This systematic review examines 34 peer-reviewed studies on AI ethics in education (2020–2024), using the PRISMA framework and a multi-dimensional coding scheme. It identifies stakeholder-specific ethical tensions and highlights gaps in empirical validation, geographic diversity, and policy implementation. Findings cluster into four solution domains: AIED Ethical Frameworks, Ethical Assessment Frameworks, AIED Literacy Frameworks, and Literacy Assessment Frameworks. These are mapped against stakeholder groups and educational levels to expose misalignments and oversights. Visual tools—frequency summaries and a stakeholder–tension Sankey diagram—deepen the analysis. This study offers a structured typology and actionable, context-sensitive recommendations to support equitable and responsible AI integration across K–12 and higher education settings.

With the rapid development of Generative AI technology, businesses need their marketers to adopt it to assist in completing their job tasks. However, marketers are not entirely optimistic and may feel anxious and concerned about it. This research explores how AI anxiety affects marketers' AI-generated content intention. Based on social cognitive theory, this study empirically examines the impact of anxiety on marketers' intention to use AI-generated content, including the mediating role of AI trust and the moderating effect of AI self-efficacy. Using survey data from 495 marketers, we found that AI privacy, bias, and opacity anxiety negatively correlate with AI trust and weaken marketers' intention to use AI-generated content. Furthermore, this relationship is moderated by AI self-efficacy, where the negative relationship is significantly mitigated when marketers possess AI self-efficacy. These findings enrich the research on Generative AI in marketing and help organizations focus on marketers' anxious resistance to adopting Generative AI and reduce the adverse effects of AI anxiety.

The integration of artificial intelligence (AI) into education requires frameworks that are not only technically robust but also ethically and pedagogically grounded. This paper proposes the Generalist Education Artificial Intelligence (GEAI) framework—a conceptual blueprint designed to enable privacy-preserving, personalized, and multimodal AI-supported learning in educational contexts. GEAI features a Trusted Domain architecture that supports secure, voluntary multimodal data collection via multimedia registration devices (MM Devices), edge-based AI inference, and institutional data sovereignty. Drawing on principles from constructivist pedagogy and regulatory standards such as GDPR and FERPA, GEAI supports adaptive feedback, engagement monitoring, and learner-centered interaction while addressing key challenges in ethical data governance, transparency, and accountability. To bridge theory and application, we outline a staged validation roadmap informed by technical feasibility assessments and stakeholder input. This roadmap lays the foundation for future prototyping and responsible deployment in real-world educational settings, positioning GEAI as a forward-looking contribution to both AI system design and education policy alignment.