It remains unclear whether LLM capabil- ity gains translate into measurable student learning. We analyze 137 educational NLP papers (2022–2025) across four sectors— assessment and grammatical error correc- tion, feedback and writing support, content and question generation, and tutoring and dialogue—introducing three metrics: Valid- ity Gap (VG, share of studies not evaluat- ing learning outcomes), Deployment Readi- ness Index (DRI, real-world evaluation ex- tent), and Multilingual Equity Index (MEI, language coverage). We find a high validity gap (VG = 0.927), low deployment readiness (DRI = 0.126), and limited multilingual cov- erage (MEI = 0.126); grammatical error cor- rection shows no learning-improvement evi- dence at all (VG = 1.000). DRI correlates with learning-outcome evaluation (ρ = 0.425, p < 0.001). Our taxonomy and metrics offer the first quantitative audit of educational NLP in the LLM era.