{"id":"W4416005193","doi":"10.5465/amproc.2025.15510symposium","title":"Artificial Intelligence Meets Academic Integrity: Evaluating AI Tools To Support Literature Reviews","year":2025,"lang":"en","type":"article","venue":"Academy of Management Proceedings","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Process (computing); Systematic review; Trustworthiness; Quality (philosophy); Applications of artificial intelligence; Data quality","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":["metaresearch","research_integrity"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.9008828,0.003461158,0.01090843,0.05766853,0.01277101,0.05997431,0.009751569,0.0194859,0.004589688],"category_scores_gemma":[0.9617496,0.006437361,0.009994567,0.04147726,0.0254319,0.04073375,0.02345739,0.01356188,0.002319322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02458137,"about_ca_system_score_gemma":0.106553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004396611,"about_ca_topic_score_gemma":0.008005959,"domain_scores_codex":[0.02718366,0.8356115,0.1015531,0.003438048,0.03101907,0.00119454],"domain_scores_gemma":[0.01071947,0.8157148,0.05039313,0.03318342,0.08682039,0.003168797],"domain_codex":"methods","domain_gemma":"methods","domain_candidate":"methods","domain_consensus":"methods","study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002690593,0.0004797303,0.01108775,0.1725364,0.01192678,0.0005446009,0.04827618,0.003162198,0.001092404,0.09697291,0.06050763,0.5907229],"study_design_scores_gemma":[0.003450094,0.002815057,0.0117083,0.4024654,0.01477934,0.001064442,0.01987863,0.020261,0.003583447,0.2517626,0.2665927,0.00163908],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01537941,0.1930715,0.3249311,0.3526843,0.0217301,0.05127946,0.001484613,0.002056086,0.03738342],"genre_scores_gemma":[0.1070939,0.03851542,0.7800984,0.02224105,0.00511214,0.04517968,0.0005363559,0.0004584798,0.0007645488],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9805141,"threshold_uncertainty_score":0.1783512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7659018382202408,"score_gpt":0.5922062344857193,"score_spread":0.1736956037345215,"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."}}