Md Shakib Hasan (韩文韬)

Md Shakib Hasan is a Bangladeshi researcher and engineer, born November 17, 2002. He is currently pursuing an M.S. in Computer Technology at Central China Normal University (Wuhan), funded by the Chinese Government Scholarship. His work bridges technical AI with pedagogical insights — focusing on Explainable AI (XAI), early dropout prediction, and zero-shot skill diagnostic frameworks in STEM education.

Educational Researcher - AIEDTech Research Forum

• Lead advanced statistical modeling & data visualization for AIED projects.
• Published 2 Q1 journal papers (first author) + 1 IEEE conference paper.
• Spearheaded XAI-driven dropout prediction frameworks with fairness constraints.

R&D Intern - Nanchong Chuangnian 3D Technology

• Collaborated on 3D tech solutions and embedded system prototyping.
• Documented technical workflows and supported engineering troubleshooting.

Brand Promoter - BESTYBD

• Social media strategy & market analysis, boosted sales by 25% (2022 report).

M.S. in Computer Technology

Chinese Government Scholarship (MOE, P.R. China)

Focus: Explainable AI in education, learning analytics, ethical AI frameworks.

B.E. in Electronic Information Engineering

Awards: Best Graduate (Batch 2021), Best Research Student Award (2024), Sichuan Provincial Scholarship.

Thesis: Optimizing Traffic Sign Recognition for Autonomous Vehicles Using Deep Learning.

Harnessing blockchain for emotion identification: A novel edge-compatible privacy-preserving framework (2026)

Journal of King Saud University - Computer & Information Sciences (Springer)

The Adaptive Lab Mentor (ALM): An AI-Driven IoT Framework for Real-Time Personalized Guidance in Engineering Education (2025)

Sensors (MDPI), 25(24), 7688

Optimizing Traffic Sign Recognition for Autonomous Vehicles Using SSD-MobileNet_v2 (2025)

10th ISCTT Conference, IEEE

XAI Based Dropout Prediction

Designing interpretable early-warning models for student retention, integrating fairness metrics and trustworthy AI.

Strucin-X: Diagnostic Framework for Zero-Shot Skill Identification

Structural primitive extraction for mathematics education, enabling adaptive interventions without labeled skill data.