{"id":"W7118051256","doi":"","title":"Artificial Intelligence for All? Brazilian Teachers on Ethics, Equity, and the Everyday Challenges of AI in Education","year":2025,"lang":"","type":"article","venue":"ArXiv.org","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Foreign, Commonwealth and Development Office; Fundação de Amparo à Pesquisa do Estado de São Paulo; International Development Research Centre; Government of the United Kingdom; Spencer Foundation","keywords":"Enthusiasm; Inclusion (mineral); Curriculum; Literacy; Perception; The Internet; Digital divide; Digital literacy; Educational technology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004937578,0.0001995896,0.0003132023,0.0009345009,0.00430496,0.003811456,0.0003059145,0.001000035,0.002143172],"category_scores_gemma":[0.0103509,0.0002487741,0.0001880015,0.0008011639,0.00711831,0.002485234,0.002553813,0.001753807,0.0002431125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00324769,"about_ca_system_score_gemma":0.007671626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03201801,"about_ca_topic_score_gemma":0.03983356,"domain_scores_codex":[0.9964433,0.001749794,0.0001302607,0.0002568593,0.0008158013,0.0006039058],"domain_scores_gemma":[0.9937143,0.002860235,0.001354904,0.0002954434,0.0007636248,0.001011483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00002738799,0.0000874312,0.07023446,0.0003338173,0.00001081209,0.000728816,0.7935144,0.00009102076,0.002432605,0.05168572,0.007070981,0.07378255],"study_design_scores_gemma":[0.00001273271,0.00009311958,0.08355109,0.001337171,0.00002006833,0.0007645267,0.6307978,0.0003202626,0.0007380095,0.01180062,0.2705224,0.00004218746],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8197271,0.007541491,0.001955574,0.07533503,0.0003209229,0.00005216473,0.00003544974,0.00003125199,0.09500107],"genre_scores_gemma":[0.9928942,0.001985902,0.0003073439,0.002127718,0.00002473197,0.00001400188,0.000006589279,0.000006738145,0.002632718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03201801,"threshold_uncertainty_score":0.0636633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2175861817633958,"score_gpt":0.4849086981780081,"score_spread":0.2673225164146123,"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."}}