Autonomous driving paper index
Bridging Inclusive Education and AI: A Technology-Centric Taxonomy and Systematic Literature Review
One-line summary
We propose a novel, requirement-driven taxonomy grounded in six SEND categories: Disability, Sociocultural, Socioeconomic, Geographic, Giftedness, and Lifestage.
Engineering notes
Key topics: autonomous driving. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Inclusive education aims to dismantle barriers for learners with Special Educational Needs and Disabilities (SEND), and the emergence of Artificial Intelligence (AI) presents transformative potential for advancing educational equity. However, extant research in the field of AI for Inclusive Education has predominantly adopted humanities-based perspectives and often remains limited in literature scale. Through a technology-centric lens, this review synthesizes 248 publications to bridge the gap between technical capabilities and pedagogical requirements. We propose a novel, requirement-driven taxonomy grounded in six SEND categories: Disability, Sociocultural, Socioeconomic, Geographic, Giftedness, and Lifestage. Furthermore, our analysis elucidates the functional patterns of AI systems across three primary stakeholders in the educational community—students, teachers, and administrators—while identifying current research challenges and proposing potential solutions. This paper aims to provide systematic design guidelines for practitioners and researchers seeking to contribute to this emerging field, thereby accelerating the development of robust and inclusive digital learning ecosystems.
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