{"id":"W3103420222","doi":"10.1007/s11423-020-09858-2","title":"Machine learning for human learners: opportunities, issues, tensions and threats","year":2020,"lang":"en","type":"article","venue":"Educational Technology Research and Development","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":105,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"King's College London","keywords":"Accountability; Artificial intelligence; Computer science; Educational technology; Curriculum; Legislature; Machine learning; Knowledge management; Engineering ethics; Psychology; Mathematics education; Pedagogy; Political science; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.037713,0.0003830107,0.0005596731,0.001468585,0.005357139,0.01615734,0.00199628,0.006822989,0.007315683],"category_scores_gemma":[0.05484431,0.00027801,0.0003740118,0.001169812,0.01808491,0.02938077,0.009841763,0.0106768,0.001260304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004048216,"about_ca_system_score_gemma":0.004972769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00115231,"about_ca_topic_score_gemma":0.001132266,"domain_scores_codex":[0.9739429,0.0189404,0.0005408589,0.001176155,0.003917717,0.001482056],"domain_scores_gemma":[0.9259213,0.05709653,0.002759572,0.003849091,0.005806109,0.004567422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004159852,0.0001241277,0.002508993,0.0002232318,0.00001497318,0.0002028388,0.006034101,0.0008656043,0.0001573013,0.8296511,0.03648014,0.123696],"study_design_scores_gemma":[0.00001308766,0.00005349757,0.0007972555,0.001060197,0.000006232334,0.0002512128,0.01415887,0.002742016,0.000362434,0.8552395,0.1252806,0.00003507596],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01370754,0.01416262,0.01402453,0.9061486,0.001146187,0.00003119517,0.00003020177,0.00007562609,0.05067354],"genre_scores_gemma":[0.8768516,0.02158531,0.01782224,0.06354944,0.004097667,0.0002568068,0.00005840649,0.0001437121,0.01563489],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.037713,"threshold_uncertainty_score":0.1994478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3509394530848094,"score_gpt":0.4315278848171519,"score_spread":0.08058843173234254,"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."}}