{"id":"W4413069729","doi":"10.1007/978-3-031-98414-3_29","title":"Personalizing Explanations of AI-Driven Hints to Users’ Characteristics: An Empirical Evaluation","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Empirical research; Human–computer interaction; Information retrieval; Epistemology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001856125,0.0003981596,0.0005142973,0.001390808,0.000273407,0.0004299666,0.003071795,0.0002534962,0.00005903496],"category_scores_gemma":[0.000568376,0.0004060867,0.0001122331,0.001037779,0.000331724,0.0009897659,0.0007403007,0.0005744759,0.00004856637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005917292,"about_ca_system_score_gemma":0.001379609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007195498,"about_ca_topic_score_gemma":0.0002521149,"domain_scores_codex":[0.995687,0.0001331955,0.0007163098,0.00141549,0.001514698,0.000533294],"domain_scores_gemma":[0.996352,0.0006019586,0.0002853504,0.001338169,0.001198557,0.0002239742],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002151444,0.0001238681,0.00133525,0.00007549613,0.00002833531,0.00004697687,0.01266756,0.05464603,0.001510585,0.04876282,0.0001465922,0.880635],"study_design_scores_gemma":[0.00008621463,0.0002260602,0.001122806,0.0004754078,0.00001831246,0.00001092622,0.000003116094,0.9414288,0.004329453,0.05116665,0.0006475451,0.0004846683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001972991,0.00005096654,0.992395,0.002160459,0.001317509,0.0006409738,0.00001619341,0.00009218202,0.00135372],"genre_scores_gemma":[0.6581281,0.00001201226,0.3366158,0.004497362,0.0003630656,0.00004052186,0.00002825318,0.00003009109,0.0002848406],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8867828,"threshold_uncertainty_score":0.9998391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06512646958833557,"score_gpt":0.3558005587983916,"score_spread":0.2906740892100561,"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."}}