Beyond Marks: NLP-Based Learning Gap Detection and Remediation

Conventional grading in Computer Science education evaluates students mainly through numeric marks.The issue with this approach is that a single score rarely reflects what a student truly understands, partially grasps, misinterprets, or misses entirely.To solve this, this research outlines a domain-specific Artificial Intelligence method leveraging Natural Language Processing (NLP) and semantic analysis to pinpoint exact knowledge gaps within written answers.The framework processes theoretical responses by comparing them against expected concept-level knowledge for a given question, then categorizes a student's comprehension into four distinct states: correctly understood, partially understood, misunderstood, or missing.Using these mapped gaps, the system generates customized learning aids, offering simplified breakdowns, clear examples, curated study resources, and targeted drill questions.To test the system's performance, the NLP-based gap detection will be evaluated against standard score-based grading.Expert review alongside standard statistical metrics specifically accuracy, precision, recall, and F1-score will be applied to measure how reliably the system identifies these gaps.Additionally, a follow-up post-assessment will be run to check if tailored remediation actually enhances student conceptual retention.Centered on Computer Science coursework, this study seeks to show how specialized NLP tools can enable deeper, adaptive, and diagnostic learning beyond basic mark-based testing.