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.

This systematic review synthesizes empirical evidence on the role of Artificial Intelligence (AI) in inclusive education. The review aimed to examine (1) the impact of AI technologies on learning outcomes and engagement among students with special needs, (2) barriers and enablers influencing AI adoption by educators, and (3) the role of theoretical frameworks in guiding AI-based interventions. A comprehensive search was conducted in Scopus, Web of Science, DOAJ, and Google Scholar for English-language, peer-reviewed studies published between 2020 and 2025. Sixteen (16) studies met the inclusion criteria and were appraised using the Mixed Methods Appraisal Tool (MMAT). Findings indicate that AI tools enhance personalization, accessibility, and engagement, particularly for learners with disabilities, while barriers such as infrastructure gaps and low digital literacy persist. Enablers include institutional support and teacher training, though theoretical frameworks were inconsistently applied. Limitations include the exclusion of grey literature and reliance on short-term studies. AI can advance inclusive education when integrated with ethical, pedagogical, and institutional strategies, while future research should prioritize longitudinal, theory-driven, and culturally responsive models.

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.

This mixed-method case study explores the implementation and impact of a grade-wide literacy intervention program through a professional learning community (PLC) at a middle school in Norwalk, California. A team of sixth-grade teachers who had adopted the principles of DuFour, Fullan and Quinn sought to create a PLC that could lead to positive change in school culture and literacy rates across the curriculum. The research questions are: What does a systemic grade-wide literacy-focused PLC intervention look like? What is its impact on reading comprehension, literacy practices, and student confidence? How does this effort influence the school system and stakeholder buy-in? Data was collected through pre- and post-surveys from students and staff, meeting agendas, notes, and correspondences, along with i-Ready scores to measure reading comprehension growth. Initially, 103 students were reading below the fourth-grade level; by the end of the year, this number decreased by 18.4% to 84 students. Surveys and reflections indicated improved teacher-student relationships, with students feeling more supported. Additionally, students exhibited positive literacy practices and increased confidence as readers. The initiative positively impacted the wider school culture, leading other grade-level teams to adopt similar practices. This study highlights the managerial and organizational benefits of PLCs in educational settings.

This study aims at examining if TOEFL score, demographic characteristics and environmental factors predict the academic success (through GPA) of selected Chinese international students. Using the cognitive predictor model of Molnár and Kocsis as theoretical framework and Astin’s Input-Environment-Outcomes (I-E-O) model as conceptual paradigm, the roles of other input variables (demographic characteristics) and environmental factors (self-confidence, study habits, social skills, previous learning experiences) may as well impact the Chinese international students’ GPA, independently from TOEFL. A total of 201 survey participants were recruited through snowball sampling. Results revealed that majority of the participants gained a TOEFL score of 90 and above with majority earning a GPA of 3.0 and above. This indicated a good academic performance among the participants. Pearson correlation analysis found a moderate positive correlation between TOEFL score and GPA (r = 0.30, p < 0.001) while each of the subsections of TOEFL demonstrated statistically significant positive correlations with GPA: reading (r = 0.287, p < 0.001), listening (r = 0.217, p = 0.002), speaking (r = 0.155, p = 0.028), and writing (r = 0.361, p < 0.001). Years living in the US, Major and Institution were found to predict the participants’ GPA while two indicators of self-confidence (confidence in finishing the program and confidence in earning 3.0 GPA) also predicted their GPA. Likewise, one indicator of study habits (habits are adequate to maintain good grades) is also a predictor of GPA. A Hierarchical Linear Modeling (HLM) analysis revealed that Input variables (Years Living in the US, Institution, Major) explained 7.7% of the variance in students’ GPA. Environmental variables (Self-Confidence, Study Habit) explained an additional 11.5% when controlling Input factors. Thus, a total of 19% of the variation in GPA was explained by Input and Environment factors alone. HLM analysis revealed that the TOEFL score was the strongest factor predicting GPA but only explains 6.9% of the total variation.