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.

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 paper explores the use of deep reinforcement learning (DRL) to enable autonomous decision-making and strategy optimization in dynamic graphical games. The proposed approach consists of several key components. First, local performance metrics are defined to reduce computational complexity and minimize information exchange among agents. Second, an online iterative algorithm is developed, leveraging Deep Neural Networks to solve dynamic graphical games with input constraints. This algorithm employs an Actor-Critic framework, where the Actor network learns optimal policies and the Critic network estimates value functions. Third, a distributed policy iteration mechanism allows each intelligent agent to make decisions based solely on local information. Finally, experimental results validate the effectiveness of the proposed method. The findings show that the DRL-based online iterative algorithm significantly improves decision accuracy and convergence speed, reduces computational complexity, and demonstrates strong performance and scalability in addressing optimal control problems in dynamic graphical intelligent games.