{"id":"W4399913501","doi":"10.1145/3627043.3659566","title":"Initial results on personalizing explanations of AI hints in an ITS","year":2024,"lang":"en","type":"article","venue":"","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Natural language processing","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":[0.003796064,0.0007191271,0.0004787575,0.0005573138,0.0003721094,0.001092046,0.0007631531,0.0009550992,0.00501041],"category_scores_gemma":[0.05439804,0.0003266213,0.0004324746,0.000341679,0.0004078401,0.00127663,0.0009230983,0.000927764,0.0005597328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005318317,"about_ca_system_score_gemma":0.0004561508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001213957,"about_ca_topic_score_gemma":0.001687391,"domain_scores_codex":[0.9967894,0.001817252,0.000336408,0.0003095157,0.00056241,0.0001849739],"domain_scores_gemma":[0.9170622,0.07033973,0.001794671,0.004007779,0.005850163,0.0009455497],"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":[0.01031481,0.01037418,0.06141433,0.00783323,0.0003866867,0.0007540932,0.02627512,0.01881609,0.205967,0.001487388,0.003229153,0.6531479],"study_design_scores_gemma":[0.001803196,0.04937835,0.3442182,0.001087605,0.002643591,0.001801796,0.009976629,0.1057653,0.4456322,0.002841251,0.0344672,0.0003846085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.974395,0.0001723848,0.01979164,0.0001731841,0.00002470352,0.0006766247,0.000213405,0.001206173,0.003346863],"genre_scores_gemma":[0.9478824,0.0002230445,0.04870205,0.000137058,0.00002192535,0.0003336981,0.0003434431,0.000125696,0.002230808],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00501041,"threshold_uncertainty_score":0.0200758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09974566932648732,"score_gpt":0.386394807670818,"score_spread":0.2866491383443306,"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."}}