{"id":"W4407681595","doi":"10.1145/3641555.3705080","title":"Prompt-Engineering Strategies for Minimizing Bias in Large Language Model Outputs: Applications in Computing Education","year":2025,"lang":"en","type":"article","venue":"","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Software engineering; Programming language; Distributed computing","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":[],"consensus_categories":[],"category_scores_codex":[0.0003988699,0.00009791851,0.0001311305,0.0003083116,0.00006280933,0.0001615444,0.000286578,0.00004158836,4.641661e-7],"category_scores_gemma":[0.00004049067,0.00009667391,0.00003290499,0.0004100177,0.000003245857,0.0002790833,0.00008230378,0.000118713,0.000001794111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009478696,"about_ca_system_score_gemma":0.0002165442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001298844,"about_ca_topic_score_gemma":0.00007437976,"domain_scores_codex":[0.9991514,0.00001867727,0.00026632,0.0002603618,0.00006827201,0.0002349446],"domain_scores_gemma":[0.9995749,0.0001218779,0.00004968364,0.0001854909,0.00004934656,0.00001867647],"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":[6.043096e-7,0.00005380809,0.001512761,0.00008776307,0.00000286657,2.300873e-7,0.002880702,0.1097199,0.0005564296,0.882167,0.00002146209,0.00299644],"study_design_scores_gemma":[0.0001356609,0.000006220768,0.001005976,0.0002326164,0.000001116005,3.189099e-7,0.004525261,0.9903405,0.0004755052,0.001057391,0.002108143,0.0001113353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03708712,0.0002074916,0.959758,0.0001143424,0.0001319575,0.0004844383,7.19089e-7,0.0001095276,0.002106344],"genre_scores_gemma":[0.9017566,9.908614e-7,0.09581783,0.00005900981,0.00003370698,0.0001249405,0.000003963346,0.00000596352,0.002196934],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8811096,"threshold_uncertainty_score":0.3942248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02821525344156647,"score_gpt":0.3064818608003466,"score_spread":0.2782666073587801,"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."}}