Artificial Intelligence (AI) has transformed educational assessment processes by automating grading at scale. Still, there are problems with equity, bias, and large-scope assessment grading. This paper describes a framework for automated grading using AI with Natural Language Processing (NLP) and deep learning to ensure scalability and fairness simultaneously. The system processes multiple question types, from multiple-choice through short answer to essay questions, and uses fairness-aware algorithms to control bias. The experimental tests on a large dataset of students’ responses showed that the AI model performed with 92.5% accuracy and claimed human-like grading precision. Tests on scalability confirm that the assessments are completed five times quicker than the conventional human grading done to one person, thus, making it acceptable for big educational institutions. Moreover, AI fairness tests demonstrated unprecedented 35% bias reduction for grading over alternative methods. All these helps illustrate servable efficiency estimates that AI being able to help reduce educators’ workload exponentially while achieving fair grading results. The next steps for the research plan will aim to make the system more flexible to subjective grading and embed explainability features for trustable transparent AI.