{"id":"W4412740694","doi":"10.22329/jtl.v19i2.8932","title":"Integrating AI to Address Generational Characteristics and Educational Needs","year":2025,"lang":"en","type":"article","venue":"Journal of Teaching and Learning","topic":"AI in Service Interactions","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sociology; Data science; Computer science; Political science","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":[0.007989582,0.0002141622,0.0002413702,0.001098449,0.00240968,0.004712287,0.0006216893,0.0005941415,0.001606],"category_scores_gemma":[0.01459111,0.0001961194,0.0002221987,0.000582262,0.002333955,0.003654443,0.006412972,0.001072876,0.0001862863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001821424,"about_ca_system_score_gemma":0.004139242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001770041,"about_ca_topic_score_gemma":0.004024842,"domain_scores_codex":[0.9968342,0.001917891,0.0001338019,0.0002526944,0.000400314,0.0004610635],"domain_scores_gemma":[0.9904752,0.005237577,0.001231711,0.0008245081,0.0009741326,0.001256843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001126074,0.0003765254,0.2480249,0.0003535395,0.00004351193,0.0005263603,0.4584417,0.0003548285,0.00314848,0.03306336,0.001464399,0.2540898],"study_design_scores_gemma":[0.00002964192,0.0007670093,0.2413347,0.0005750883,0.0000789835,0.0009724799,0.6167231,0.001866699,0.00267491,0.04802366,0.08687286,0.00008090219],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9831702,0.0005424035,0.003222028,0.003849483,0.00003825356,0.00007101397,0.0000177426,0.00001570208,0.00907323],"genre_scores_gemma":[0.9965842,0.0002217349,0.001895733,0.0002717382,0.00001471924,0.00005644323,0.00001375551,0.000004365201,0.000937171],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.007989582,"threshold_uncertainty_score":0.04225349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0105907870432151,"score_gpt":0.310347810031819,"score_spread":0.2997570229886039,"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."}}