Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
Published August 3, 2026, the peer-reviewed article presents a rubric designed to help instructors evaluate generative AI feedback on student writing assignments. The rubric assesses five dimensions and is illustrated with comparative data from multiple GenAI models applied to student work in a science writing course.
A multicenter study of 556 young adults with acute poisoning compared 14 machine learning algorithms to traditional logistic regression for predicting 28-day mortality. Using six LASSO-selected factors including herbicide poisoning, white blood cell count and shock, logistic regression matched the best machine learning model on discrimination and calibration, leading authors to build a nomogram for early risk stratification.
A meta-analysis of 110 studies up to June 2026 evaluated AI algorithms for diagnosing urological cancers on CT, MRI, and ultrasound, pooling sensitivity, specificity, and AUC and comparing to clinician performance where reported.
By August 2026, researchers had developed and externally validated a two-layer Stacking ensemble that integrates baseline clinical data, neurological assessments, and cervical MRI features to predict one-year outcomes after traumatic cervical spinal cord injury. In 340 patients analyzed, the model predicted AIS grade with AUC 0.85 and predicted continuous motor and independence scores with R8 above 0.986.
A peer-reviewed study published April 17, 2026 reviewed literature from 2020 to 2025 on AI in the labour market, examining changes in job roles, skill requirements, and HR practices through technological, organisational, and institutional lenses.
On 2026-04-02, a peer-reviewed paper presented a general equilibrium model of AI-driven automation with heterogeneous workers and irreversible skill investments. It finds automation reduces demand and wages for low-skilled workers in routine tasks while enhancing productivity where AI complements high-skilled labor, leading to higher aggregate output and total welfare but a higher skill premium and wider inequality.
As of May 2026, generative AI mediates core parts of learning, prompting a peer-reviewed conceptual paper to propose criteria for distinguishing legitimate pedagogical uses from manipulative and deceptive ones. It introduces three principles — moral legitimacy, developmental integrity, and value preservation — to assess influence and protect reflective judgement.
Published February 28 2026, this peer-reviewed study examined how art students negotiate creative identity when working with generative AI. Forty students from visual arts, design, music, and animation sorted 42 statements about collaboration, authorship, ethics, and control, and factor analysis revealed five distinct perspectives ranging from human-centered direction to enthusiastic exploration.
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