I am seeking an experienced academic researcher to deliver a complete, journal-ready research paper on Explainable AI (XAI) for predicting and interpreting student academic performance. The study must use multiple academic data sources (exams & quizzes, assignments & projects, and class participation records), which I will provide in CSV format. Scope of work: • Clean, preprocess, and align datasets using student IDs and time frames • Perform relevant feature engineering • Build at least one strong predictive model (with optional comparisons) • Apply explainability techniques such as SHAP, LIME, decision trees, or equivalent, with clear justification • Evaluate predictive performance and interpretability • Produce publication-quality visualizations highlighting influential features • Write the full research paper (Abstract, Introduction, Related Work, Methodology, Results, Discussion, Conclusion, References) in IEEE or Springer format • Provide fully annotated Python or R notebooks to reproduce all results, figures, and tables Deliverables: • Cleaned dataset • Complete manuscript (submission-ready) • Reproducible code/notebooks and figures Milestones: 1. Cleaned dataset + baseline model results 2. Final manuscript + complete codebase Requirements: • Prior experience publishing academic papers • Strong background in Explainable AI / Machine Learning • Familiarity with educational or student performance data preferred