{"id":"W4413888947","doi":"10.1007/978-3-032-03870-8_20","title":"Improving Text Readability to Support Student Comprehension and Learning: An LLM-Powered Approach","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Text Readability and Simplification","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Readability; Computer science; Comprehension; Multimedia; Human–computer interaction; Artificial intelligence; Natural language processing; Programming language","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":[0.002002278,0.0005524621,0.0006377014,0.0007020242,0.0005215004,0.0009042854,0.002846066,0.0003584537,0.000009262714],"category_scores_gemma":[0.0002391474,0.0005131533,0.0000939298,0.0006505515,0.0005450262,0.0007533061,0.002452664,0.0011432,0.00001216007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000382859,"about_ca_system_score_gemma":0.0005976862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001013516,"about_ca_topic_score_gemma":0.00008983329,"domain_scores_codex":[0.99484,0.0001583285,0.0006718723,0.002690022,0.001008744,0.0006310627],"domain_scores_gemma":[0.9964285,0.0005370273,0.0002534095,0.002074724,0.0003526818,0.0003536135],"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":[0.00001225534,0.0001411317,0.0004220759,0.00009343112,0.000006071011,0.000007954408,0.002248604,0.00660773,0.0004344474,0.007899589,0.000008890102,0.9821178],"study_design_scores_gemma":[0.0005072603,0.002241154,0.01307076,0.0003166903,0.00003128133,0.0001006881,0.000009131225,0.9362803,0.001010632,0.03918095,0.005363008,0.001888215],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003705928,0.000103991,0.9910895,0.0004676253,0.0005986723,0.0009156077,0.000004183736,0.0002544283,0.002860116],"genre_scores_gemma":[0.5550436,0.0000128556,0.4431705,0.0009787931,0.000146668,0.0000230153,0.00002135237,0.00002397677,0.0005791657],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9802296,"threshold_uncertainty_score":0.999732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0208712578425082,"score_gpt":0.2735095957058067,"score_spread":0.2526383378632985,"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."}}