{"id":"W4416019805","doi":"10.1080/02619768.2025.2584283","title":"Artificial intelligence in the context of teacher education: emerging themes and critical issues","year":2025,"lang":"en","type":"article","venue":"European Journal of Teacher Education","topic":"Artificial Intelligence in Education","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Council for Canadian Studies","keywords":"Context (archaeology); Context effect; Higher education; Teacher education; Teaching method","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002366221,0.00012427,0.0001646107,0.0003428639,0.0001012841,0.0001714836,0.0009186097,0.00002858364,0.00004171047],"category_scores_gemma":[0.001189075,0.00009716968,0.00006046123,0.0005378189,0.0001718475,0.0005561765,0.00007626335,0.0003570124,0.00001495773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006882642,"about_ca_system_score_gemma":0.0006627944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005995736,"about_ca_topic_score_gemma":0.00001110399,"domain_scores_codex":[0.9976793,0.0008939721,0.0008219709,0.0001997547,0.0002516446,0.0001533474],"domain_scores_gemma":[0.9986102,0.0002507904,0.0003104064,0.0003633228,0.0004124462,0.00005280796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.000009769106,0.0009731328,0.002505504,0.00002482791,0.000007101376,0.000001158141,0.03092535,0.000009740308,0.0004532948,0.1913488,0.003702153,0.7700391],"study_design_scores_gemma":[0.0001291409,0.0007057117,0.1266194,0.002040886,0.0001657066,0.0002553825,0.5036728,0.00433624,0.02910313,0.2450145,0.08709719,0.0008599331],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.624434,0.01406645,0.1969536,0.1263462,0.007748067,0.000535194,7.039705e-7,0.00004327761,0.02987255],"genre_scores_gemma":[0.9901248,0.00008404332,0.008453223,0.0004694313,0.0004561151,0.000004713397,6.580863e-7,0.000008341251,0.0003986452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7691792,"threshold_uncertainty_score":0.3962464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.047493950265949,"score_gpt":0.3817126463168193,"score_spread":0.3342186960508703,"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."}}