{"id":"W4413525635","doi":"10.5430/wjel.v15n8p228","title":"English Language Learning with AI: Proficiency Gains and Learner Experience","year":2025,"lang":"en","type":"article","venue":"World Journal of English Language","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Natural language processing; Mathematics education; Artificial intelligence; Linguistics; Psychology; Philosophy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001735365,0.0002751807,0.000350973,0.0005431382,0.0003484195,0.001393415,0.0003933903,0.0002721736,0.002193028],"category_scores_gemma":[0.007293267,0.00009665449,0.0002739355,0.0002124052,0.0003539147,0.0008063741,0.0009904238,0.0004226261,0.0006355349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003419101,"about_ca_system_score_gemma":0.0005222567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009954462,"about_ca_topic_score_gemma":0.001206235,"domain_scores_codex":[0.9990109,0.000336292,0.00006586566,0.0001114949,0.000317274,0.0001581461],"domain_scores_gemma":[0.9964513,0.001413333,0.0007064915,0.0001599157,0.0005188988,0.0007500398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003433743,0.02134229,0.520133,0.0006043872,0.0001949491,0.0005966205,0.0390862,0.0008251537,0.02490555,0.0003406055,0.00095568,0.3875818],"study_design_scores_gemma":[0.0001572298,0.0238685,0.9206326,0.0001636186,0.0001665512,0.0006351122,0.03263654,0.001564108,0.0151108,0.0004029061,0.004588473,0.00007353878],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994443,0.00002705989,0.00004902934,0.00001280272,0.000001688727,0.000008737177,0.000008650932,0.000004186319,0.0004435724],"genre_scores_gemma":[0.9990145,0.00005817238,0.0002295607,0.00001534931,0.000002771477,0.00001499865,0.00002333932,0.000002056757,0.0006392127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002193028,"threshold_uncertainty_score":0.009177566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006068407439096328,"score_gpt":0.2747092198437066,"score_spread":0.2686408124046103,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}