Special Session 8

Special Session 8: Artificial Intelligence and Data-Driven Innovation in Personalized and Gamified Educational Technology


 Description 

Artificial intelligence and rich learner interaction data are reshaping how educational technology engages, adapts to, and supports students. This special session focuses on the intersection of AI-driven personalization and gamified learning: systems that use machine learning, learning analytics, and learner or player modeling to tailor content, game elements, feedback, and support to each student. The session welcomes work across the full range of digital learning environments, including Learning Management Systems, MOOCs, mobile learning, and blended classrooms. A central theme is interpretability and pedagogical grounding: as models become more capable, the field needs methods that connect behavioral patterns to established educational and motivational theory, so that personalization and gamification serve measurable learning rather than engagement for its own sake. We invite empirical studies, system and framework proposals, comparative analyses, and position papers on topics ranging from behavior-based learner profiling and player typologies such as the Hexad model, to performance prediction and retention, adaptive content recommendation, intelligent tutoring, and gamification design aligned with curriculum, assessment, and motivation theory. Contributions addressing datasets, reproducibility, privacy, ethics, and responsible deployment are particularly encouraged. The session aims to bring together researchers and practitioners from AI, learning analytics, game-based learning, and instructional design to shape the next generation of personalized and gamified educational technology.

 Session Topics 

The topics of interest include, but are not limited to:
- AI-driven personalization and adaptive learning in LMSs, MOOCs, mobile learning, and blended classrooms
- Learning analytics and educational data mining from learner interaction logs
- Learner modeling, player typologies, and behavior-based profiling, including Hexad-inspired approaches
- Interpretable machine learning for engagement, performance prediction, retention, and learning support
- Intelligent tutoring systems, AI-assisted instruction, and adaptive content recommendation
- Gamification design grounded in motivation theory, feedback, assessment, and curricular alignment
- AI-supported formative assessment, feedback generation, and learner progress visualization
- Datasets, reproducibility, privacy, ethics, and responsible deployment in AI-driven educational technology

 Submission Method 

Submit your Full Paper or your paper abstract-without publication (200-400 words) via Online Submission System, then choose Special Session 8 (Artificial Intelligence and Data-Driven Innovation in Personalized and Gamified Educational Technology)

 Session Organizers 


Asst. Prof. Seifeddine Bouallegue,
University of Doha for Science and Technology, Qatar
 
Dr. Seifeddine Bouallegue is an Assistant Professor and Head of the Software Systems Department at the University of Doha for Science and Technology (UDST), Qatar. He holds a Ph.D. in Computer Science, Telecommunications and Electronics from Université Pierre et Marie Curie (Paris VI), France. His research focuses on educational technology, gamification, AI-driven personalized learning, machine learning, and learning analytics. He is the Lead Principal Investigator of a three-year, USD 700,000 Academic Research Grant from the Qatar National Research Fund on enhancing programming course outcomes through gamification and AI-driven personalized learning. His recent work spans early student performance prediction, learner classification and player typologies, and AI-enhanced e-learning, with publications in venues including IEEE Access, Information, PeerJ Computer Science, Electronics, and Springer LNDECT, as well as the ICETC conference series. Alongside his academic career, he brings industry and innovation experience as Co-Founder and CTO of E-Butler Technologies, as Co-Founder of Enable Tech, and as Digital Innovation Manager at Qatar University. His contributions have been recognized with the UDST Excellence in Research Award (2025). He serves the research community as Co-Chair of the Software Engineering and AI track at the ACM Symposium on Applied Computing (SAC) 2026, as a peer reviewer for IEEE Access, and as a member of IEEE and ACM.

 

Asst. Prof. Vanilson De Arruda Burégio,
University of Doha for Science and Technology, Qatar
 
Dr. Vanilson De Arruda Burégio is an Assistant Professor of Software Engineering in the College of Computing and Information Technology at the University of Doha for Science and Technology (UDST), Qatar. He holds a Ph.D. and an M.Sc. in Computer Science from the Informatics Center of the Federal University of Pernambuco (CIn-UFPE), Brazil. His research interests include software engineering, software architecture, software reuse, APIs, and web-based social systems, an area in which he helped advance the concept of social machines. Before joining UDST, he was an Associate Professor at the Federal Rural University of Pernambuco, Brazil, and combined his academic career with more than a decade of industry experience as a software architect, researcher, and team leader in organizations including SERPRO and C.E.S.A.R. His teaching spans programming, software architecture, component-based development, and software testing.

 

Assoc Prof. Tewfik Ziadi,
University of Doha for Science and Technology, Qatar
 
Dr. Tewfik Ziadi is an Associate Professor of Software Engineering in the College of Computing and Information Technology at the University of Doha for Science and Technology (UDST), Qatar. He received his Ph.D. in Computer Science from the University of Rennes 1, France, and his HDR (Habilitation) from Université Pierre et Marie Curie (Paris VI), France. Before joining UDST, he was an Associate Professor at Sorbonne University in Paris, where he led the MoVe research team at the LIP6 laboratory. His research focuses on software product line engineering, variability management, model-driven engineering, and software reuse, with contributions ranging from behavioral model inference to open-source tools such as But4Reuse. He served as Co-General Chair of the Software Product Line Conference (SPLC 2019), coordinated the European ITEA REVAMP2 research project, and has served on numerous international program committees.