{"id":"W3042974867","doi":"10.1016/j.artint.2021.103503","title":"Toward personalized XAI: A case study in intelligent tutoring systems","year":2021,"lang":"en","type":"preprint","venue":"Artificial Intelligence","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Personalization; Computer science; Context (archaeology); Constraint (computer-aided design); Perception; Intelligent tutoring system; Value (mathematics); Cognition; Human–computer interaction; World Wide Web; Psychology; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003223149,0.001241779,0.001653423,0.00112152,0.0004407057,0.003581283,0.004131929,0.0006912538,0.0001414938],"category_scores_gemma":[0.0009083512,0.001375356,0.0005466761,0.002489767,0.0003286798,0.001136629,0.005013151,0.002478084,0.0004394885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001166841,"about_ca_system_score_gemma":0.00115796,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03058961,"about_ca_topic_score_gemma":0.007532299,"domain_scores_codex":[0.9888144,0.001328188,0.003141784,0.003420521,0.001554988,0.001740118],"domain_scores_gemma":[0.9934475,0.0008187037,0.0007760078,0.003408144,0.001008734,0.0005408448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001475721,0.005953325,0.003271599,0.0009929003,0.0005341726,0.1755485,0.2561969,0.2035496,0.00101266,0.1947969,0.00009730096,0.1578986],"study_design_scores_gemma":[0.0001113785,0.0006426095,0.00005429628,0.001330811,0.0001290532,0.003584981,0.2576248,0.6766872,0.03476988,0.02136424,0.0005548911,0.003145864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4296071,0.002238943,0.5581073,0.0003735643,0.006158232,0.002550744,0.000009436049,0.0004914535,0.0004633037],"genre_scores_gemma":[0.9920617,0.0002528371,0.005732002,0.0001314014,0.000568956,0.000866484,0.00001475424,0.0001044825,0.0002674049],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5624546,"threshold_uncertainty_score":0.9998232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1953536433642545,"score_gpt":0.3621619238272482,"score_spread":0.1668082804629937,"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."}}