Special Session 10: AI-Driven Curriculum Engineering: Intelligent Design, Learning Analytics, and Educational Quality Assurance

 Description 

Artificial intelligence is transforming education beyond tutoring systems and personalized learning by enabling the systematic design, evaluation, and continuous improvement of curricula and courses. Educational institutions increasingly require evidence-based approaches to ensure that curricula align with learning outcomes, industry needs, accreditation standards, and student success while maintaining high instructional quality.
This special session explores the emerging field of AI-driven curriculum engineering, where artificial intelligence, natural language processing, software engineering, learning analytics, and educational data mining are integrated to model, evaluate, and optimize educational ecosystems. The session welcomes research on intelligent methods for curriculum design, syllabus analysis, course quality evaluation, learning management system (LMS) analytics, competency mapping, instructional effectiveness, and automated educational quality assurance.
The session particularly encourages work that moves beyond traditional performance metrics such as student grades to develop comprehensive frameworks for understanding learning processes. Topics include semantic analysis of syllabi, curriculum alignment, assessment-content mapping, identification of course and curriculum bottlenecks, learning pathway optimization, educational knowledge graphs, explainable educational AI, and data-driven decision support for educators and administrators.
The objective is to bring together researchers, educators, instructional designers, AI scientists, software engineers, and educational technology practitioners to discuss innovative methodologies, intelligent tools, and practical applications that improve educational quality, learner success, and curriculum sustainability.

 Session Topics 

The topics of interest include, but are not limited to:
- AI for Curriculum Engineering
- Course Design and Instructional Quality
- Intelligent syllabus generation and evaluation
- Automated course quality assessment
- Learning Analytics and LMS Intelligence
- Educational Bottleneck Analysis
- Educational Software Engineering
- Assessment and Learning Alignment
- Explainable and Trustworthy Educational AI

 Submission Method 

Submit your Full Paper or your paper abstract-without publication (200-400 words) via Online Submission System, then choose Special Session 10 (AI-Driven Curriculum Engineering: Intelligent Design, Learning Analytics, and Educational Quality Assurance)

 Session Organizers 

Asst. Prof. Md Nour Hossain,
University at Albany, State University of New York, USA
 
Dr. Md Nour Hossain is an Assistant Professor in the Department of Information Sciences and Technology at the University at Albany, State University of New York (SUNY), where he directs the Kernel of Nexus (KofN) AI-Driven Innovation Center. His research focuses on artificial intelligence, educational data mining, learning analytics, natural language processing, software engineering, explainable AI, immersive learning technologies, and AI-driven educational systems. His work emphasizes designing intelligent frameworks that improve curriculum development, course quality, student success, and evidence-based educational decision making. Dr. Hossain has published extensively in AI, educational technology, software engineering, and learning sciences and actively collaborates with academia and industry on advancing trustworthy and impactful AI solutions for education.