{"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":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.0002058995,0.00008730059,0.0001886291,0.07290994,0.00002386958,0.00006058195,0.0002914303,0.00004499209,0.00002993105],"category_scores_gemma":[0.00002328711,0.00008230644,0.00005683036,0.1913538,0.00003949784,0.0002131873,0.0000627945,0.00008617986,0.00003587033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001134492,"about_ca_system_score_gemma":0.0003319052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003104572,"about_ca_topic_score_gemma":0.00001014462,"domain_scores_codex":[0.9991308,0.00002513373,0.0003176786,0.0002687714,0.0001434173,0.0001142017],"domain_scores_gemma":[0.9991857,0.0001631136,0.00005435443,0.0003205575,0.0002495211,0.00002673638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[3.72459e-7,0.0002037941,0.002196865,0.0000659242,0.0001790186,2.015349e-7,0.0007575615,0.0001467614,0.00006238259,0.6615785,0.001466504,0.3333421],"study_design_scores_gemma":[0.00004252448,0.0001101044,0.8166963,0.0004008898,0.000518953,0.000006179022,0.007541715,0.01900505,0.003914683,0.055657,0.09535648,0.0007501491],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7766168,0.08827851,0.1040454,0.009482617,0.01849776,0.00028289,0.000003074898,0.0006382562,0.002154734],"genre_scores_gemma":[0.991489,0.0004722227,0.006383693,0.00008727729,0.00006257845,0.00008881861,0.000002980344,0.000005013862,0.001408401],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8144994,"threshold_uncertainty_score":0.9375979,"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."}}