{"id":"W7112032944","doi":"","title":"Reclaiming Time for Learning Through Strategic AI Integration","year":2025,"lang":"en","type":"article","venue":"ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam)","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Guelph","keywords":"Perspective (graphical); Feature (linguistics); Key (lock); Context (archaeology)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001336006,0.000364352,0.000423827,0.0003313888,0.001258618,0.0008495417,0.0008794533,0.0001810896,0.000007811],"category_scores_gemma":[0.0003264525,0.0003346292,0.0002666998,0.0006585867,0.00004209725,0.000751779,0.0001596866,0.0008659427,0.00007451246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001860004,"about_ca_system_score_gemma":0.0002382162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005767645,"about_ca_topic_score_gemma":0.00000556313,"domain_scores_codex":[0.9973053,0.0002965753,0.0007026239,0.0007447411,0.0003108492,0.0006399445],"domain_scores_gemma":[0.9982561,0.0005207103,0.0003174881,0.0004420112,0.0003885091,0.00007510932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000424262,0.0000767278,0.0002960618,0.00008931466,0.0001554191,0.00002696683,0.006793898,0.005532084,0.08315285,0.8864974,0.0009565501,0.01638029],"study_design_scores_gemma":[0.002021854,0.001138947,0.0001952381,0.004462803,0.0001664478,0.000138736,0.005166169,0.4826848,0.1693676,0.1098323,0.2227169,0.002108082],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06611049,0.0003715077,0.9207437,0.001722074,0.001357054,0.000392094,0.000001644261,0.0005290778,0.008772369],"genre_scores_gemma":[0.9247439,0.00003792935,0.04595517,0.0006422136,0.0005281664,0.00005313903,0.00001963877,0.00004712454,0.02797273],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8747885,"threshold_uncertainty_score":0.9999106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02415041080383879,"score_gpt":0.2816621651310986,"score_spread":0.2575117543272598,"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."}}