Imagine getting a grade on a test and instantly understanding exactly why you got that score, pinpointing your strengths and weaknesses. That's the promise of AI-powered short-answer grading with personalized feedback. Researchers have built a system – and a new dataset called EngSAF (Engineering Short Answer Feedback) – to do just that. EngSAF contains thousands of student answers to engineering questions, each paired with a reference answer and feedback explaining the grade. But what makes this different from traditional grading? It's not just about a score. The team used large language models (LLMs) to generate detailed, content-focused feedback that explains *why* an answer is right or wrong. Think "You nailed the core concept, but missed a key application" instead of just "Partially correct." Testing this in a real-world exam setting at IIT Bombay revealed that the AI grader wasn't just accurate; students also found its feedback more helpful and less discouraging than traditional feedback. This points toward a future where AI could play a key role in improving learning and assessment, offering personalized feedback at scale. But this technology also faces challenges. Creating nuanced feedback requires massive, high-quality datasets. EngSAF is a significant step, but we need more diverse datasets from different fields. And, as always with AI, ethical considerations are crucial. We need to ensure AI graders are fair and avoid biases that could impact students' learning.