I’m preparing a research paper that demonstrates how explainable AI can predict and interpret student academic grades using a mix of exams and quizzes, assignments and projects, plus class participation records. I already have raw datasets in CSV form; what I need is the complete experimental pipeline and a well-structured manuscript ready for journal submission. Here’s what I’m expecting: • Clean and engineer the three data sources so they align on student IDs and time frames. • Build at least one solid predictive model—feel free to compare alternatives—but tie every result back to a clearly articulated explainability layer (e.g., decision trees, SHAP, LIME or any other method you justify). • Evaluate accuracy and, just as important, highlight which features most influence grades; visualised explanations should be publication-quality. • Draft the full paper: abstract, introduction, related work, methodology, results, discussion, conclusion and references. Follow IEEE or Springer format; I’ll confirm the venue once we start. • Provide annotated Python or R notebooks so I can reproduce every figure and table. I’ll review progress in two milestones: first the cleaned dataset with baseline results, then the finished manuscript with code. If this scope excites you and you have prior publications in educational data mining or XAI, let’s talk timelines.