Developing a Multi-Agent System to Generate Next Generation Science Assessments with Evidence-Centered Design
AI4STEM Education Center, University of Georgia（AI4STEM教育中心，佐治亚大学）；Department of Educational Psychology, University of Georgia（教育心理学系，佐治亚大学）；School of Computing, University of Georgia（计算学院，佐治亚大学）；Department of Mathematics, Science, and Social Studies Education, University of Georgia（数学、科学与社会科学教育系，佐治亚大学）
Yaxuan Yang, Jongchan Park, Yifan Zhou, Xiaoming Zhai
Contemporary science education reforms such as the Next Generation Science Standards (NGSS) demand assessments to understand students' ability to use science knowledge to solve problems and design solutions. To elicit such higher-order ability, educators need performance-based assessments, which are challenging to develop. One solution that has been broadly adopted is Evidence-Centered Design (ECD), which emphasizes interconnected models of the learner, evidence, and tasks. Although ECD provides a framework to safeguard assessment validity, its implementation requires diverse expertise (e.g., content and assessment), which is both costly and labor-intensive. To address this challenge, this study proposed integrating the ECD framework into Multi-Agent Systems (MAS) to generate NGSS-aligned assessment items automatically. This integrated MAS system ensembles multiple large language models with varying expertise, enabling the automation of complex, multi-stage item generation workflows traditionally performed by human experts. We examined the quality of AI-generated NGSS-aligned items and compared them with human-developed items across multiple dimensions of assessment design. Results showed that AI-generated items have overall comparable quality to human-developed items in terms of alignment with NGSS three-dimensional standards and cognitive demands. Divergent patterns also emerged: AI-generated items demonstrated a distinct strength in inclusivity, while also exhibiting limitations in clarity, conciseness, and multimodal design. AI- and human-developed items both showed weaknesses in evidence collectability and student interest alignment. These findings suggest that integrating ECD into MAS can support scalable and standards-aligned assessment design, while human expertise remains essential.
Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges
Hamad Bin Khalifa University（哈马德·本·哈利法大学）；The Hashemite University（哈希姆大学）；Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology（巴基斯坦-奥地利应用科学与技术学院）；Qatar University（卡塔尔大学）；Information Technology University（信息技术大学）
LLMs as World Models: Data-Driven and Human-Centered Pre-Event Simulation for Disaster Impact Assessment
University of South Florida（佛罗里达州立大学）；Arizona State University（亚利桑那州立大学）；Massachusetts Institute of Technology（麻省理工学院）；New York University（纽约大学）；University of Alabama（阿拉巴马大学）；Texas Tech University（德克萨斯科技大学）
A Review of Generative AI in Computer Science Education: Challenges and Opportunities in Accuracy, Authenticity, and Assessment
School of Computer Science and Engineering, California State University, San Bernardino, USA（加州州立大学圣伯纳迪诺分校计算机科学与工程学院）；School of Information Systems and Technology, San Jose State University, USA（旧金山州立大学信息系统与技术学院）；University of California, San Diego, USA（加州大学圣地亚哥分校）
EarthOL: A Proof-of-Human-Contribution Consensus Protocol -- Addressing Fundamental Challenges in Decentralized Value Assessment with Enhanced Verification and Security Mechanisms
