{"id":"W4412870837","doi":"10.24908/pceea.2025.19712","title":"Thriving in the Age of AI: A Model Curriculum for Developing Competencies in Artificial Intelligence for K-12","year":2025,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ontario Institute of Technology","keywords":"Thriving; Psychology; Curriculum; Artificial intelligence; Mathematics education; Gerontology; Computer science; Pedagogy; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007636732,0.0003530163,0.0002191395,0.0004921195,0.002276942,0.001945623,0.001422717,0.0006133225,0.007279681],"category_scores_gemma":[0.001240066,0.000231648,0.0002766668,0.0006736728,0.001055516,0.000932876,0.00159088,0.000915817,0.001542958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01228165,"about_ca_system_score_gemma":0.04895228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2038294,"about_ca_topic_score_gemma":0.548334,"domain_scores_codex":[0.9995926,0.00003998836,0.00001320066,0.00005496455,0.0001488144,0.0001503732],"domain_scores_gemma":[0.9982724,0.00006167057,0.00008244788,0.0000752296,0.000375663,0.001132559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004558633,0.0126774,0.06610844,0.000879715,0.00001776397,0.000717067,0.04792997,0.00523708,0.02955355,0.04543805,0.1941396,0.5968454],"study_design_scores_gemma":[0.0001663328,0.001966604,0.2269478,0.0007046182,0.0000313788,0.0005961204,0.02044127,0.01058004,0.01244521,0.01834839,0.7076457,0.0001264592],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7724426,0.0003358831,0.02139419,0.005742232,0.0002724464,0.002733401,0.001302055,0.000826371,0.1949508],"genre_scores_gemma":[0.784263,0.001048122,0.09866998,0.001422497,0.00005415226,0.001810104,0.002114986,0.0001374403,0.1104797],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2038294,"threshold_uncertainty_score":0.405286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01603359224176316,"score_gpt":0.2684659725152737,"score_spread":0.2524323802735106,"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."}}