{"id":"W4404896927","doi":"10.5539/hes.v15n1p69","title":"Bibliometric Analysis of Artificial Intelligence in STEM Education","year":2024,"lang":"en","type":"article","venue":"Higher Education Studies","topic":"Engineering Education and Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Higher education; Trend analysis; Statistical analysis; Psychology; Mathematics education; Medical education; Computer science; Political science; Statistics; Medicine; Machine learning; Mathematics","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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.007040243,0.000400233,0.0009925223,0.1415958,0.001303067,0.004720759,0.0007796164,0.0006077674,0.003016136],"category_scores_gemma":[0.0601204,0.0001845305,0.0009363457,0.2048486,0.0007558377,0.004062734,0.001646343,0.0004191555,0.0009066599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003132301,"about_ca_system_score_gemma":0.003765795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006746476,"about_ca_topic_score_gemma":0.007073146,"domain_scores_codex":[0.9865628,0.002387678,0.001867799,0.0007011355,0.008011062,0.0004694662],"domain_scores_gemma":[0.9425552,0.02778899,0.01212921,0.002126231,0.01436577,0.001034653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001647537,0.0001929499,0.5802957,0.004441632,0.0009276224,0.0004119684,0.003446067,0.00499929,0.001242893,0.0264677,0.0249534,0.3524561],"study_design_scores_gemma":[0.00002721665,0.0002008419,0.8368194,0.001686495,0.0005632309,0.0009489247,0.006776698,0.01020719,0.002253002,0.01190534,0.1284792,0.0001323954],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.775627,0.05012976,0.009063379,0.004800587,0.0005929333,0.0004523106,0.03114749,0.0006350742,0.1275515],"genre_scores_gemma":[0.9628792,0.01707773,0.004377768,0.0001941285,0.0005270062,0.0002141133,0.01135704,0.00005752493,0.003315582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8584042,"threshold_uncertainty_score":0.03723282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06939184728679959,"score_gpt":0.3847678541085747,"score_spread":0.3153760068217751,"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."}}