AI text detectors are widely deployed in educational institutions to enforce academic integrity, yet they systematically misidentify authentic writing by non-native English speakers as AI-generated, with false positive rates reaching 61-72% on English as a Second Language (ESL) student essays. This is a population-scale harm that falls disproportionately on the most linguistically vulnerable learners. Retraining detectors on learner data cannot close this gap: large language models can be prompted to mimic ESL writing patterns, and single-stream detectors are theoretically bounded to degrade toward random chance under such style-mimicry attacks. We present Meridian, a dual-stream architecture that maps each essay into a bivariate perplexity space with a Native-Expert (DistilGPT-2) axis measuring sub-word predictability and an ESL-Expert (character-level LSTM) axis capturing learner-specific morphological patterns. Meridian then classifies with an interpretable logistic boundary, deployable in under 2 seconds on CPU. Validated across three LLM families and two learner corpora (n=1,590), Meridian reduces wrongful ESL flags by 15.7x on held-out TOEFL11 with zero retraining, achieving 3.9% overall false positive rate and 0.0% for low-proficiency writers, while maintaining 98.3% detection of AI-mimicked ESL text. These results demonstrate that equitable AI text detection for diverse, non-WEIRD populations is achievable through principled multi-stream feature design that encodes complementary linguistic signals rather than a single native-English reference norm.