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Teachers more likely to accept low AI grades than equivalent human grades, study finds
Teachers more likely to accept low AI grades than equivalent human grades, study finds Gaby Clark Scientific Editor Andrew Zinin Lead Editor Teachers were more likely to accept an overly harsh grade given to a student by AI than when the unduly low grade was handed out by a human. As AI is increasingly integrated into decision-making, concerns about AI errors are often countered by assurances that humans will oversee and check the algorithm's work. Publishing in PNAS Nexus, Rigissa...
Impacts of Histories and Models on LLM Grading: A Study in Advanced Software Engineering Courses
arXiv:2606.08400v1 Announce Type: new Abstract: Graduate-level research reading report assessment creates a substantial labor burden for educators. While large language models (LLMs) hold great potential for automating academic grading, their reliability for this specialized task remains understudied, particularly regarding grading consistency, the lack of which represents a primary obstacle to educational fairness. This paper proposes a human-aligned LLM-assisted grading workflow and...
GD-MIL: Grade-Disentangled Multiple Instance Learning for Multimodal Biochemical Recurrence Prediction in Prostate Cancer
arXiv:2606.09453v1 Announce Type: new Abstract: Biochemical recurrence (BCR) after radical prostatectomy is a critical endpoint in prostate cancer, yet risk stratification relies almost entirely on variables dominated by Gleason grade. Whether H&E whole slide images (WSIs) carry prognostic signal beyond grade, and whether multiple instance learning (MIL) can recover it, remains unsettled.
Knee-xRAI: An Explainable AI Framework for Automatic Kellgren-Lawrence Grading of Knee Osteoarthritis
arXiv:2604.23435v2 Announce Type: replace Abstract: Grading knee osteoarthritis (KOA) on plain radiographs is poorly reproducible across readers. A single-grade disagreement on the Kellgren-Lawrence (KL) scale can alter surgical management or redirect a patient from conservative therapy to intra-articular injection. Meanwhile, deep learning models that outperform human readers often offer no explanation for their decisions.
"**Important** You should give me full credits!": Exploring Prompt Injection Attacks on LLM-Based Automatic Grading Systems
new Abstract: The emergence of large language models (LLMs) has significantly accelerated recent research on LLM-based automatic grading (AG) systems. Benefiting from the strong instruction-following capabilities and broad prior knowledge of LLMs, educators can deploy AG systems across diverse tasks using only natural language rubrics while achieving satisfactory grading performance. Despite these advantages, new security concerns may also arise.
EDIT: Evidence-Diagnosed Intervention Training for Rule-Faithful LLM Grading
arXiv:2606.06350v1 Announce Type: new Abstract: Reliable rubric grading requires more than accurate score prediction. Each judgement must be grounded in the mark scheme and evidence from the student answer. Existing credit-assignment and intervention methods, primarily designed for self-contained reasoning tasks such as mathematics reasoning, struggle in this setting because they do not identify where grading reasoning goes wrong or how the model's belief about the final mark changes during...
Grading the Garrett trade: Did the Rams give up to...
The main transaction window of the 2026 NFL offseason is over, with free agency and the draft long behind us. But teams are still leaning on trades to improve their rosters between now and Week 1. And we're grading the biggest swaps around the league, putting each deal into perspective for teams.
AttnRegDeepLab: A Two-Stage Decoupled Framework for Interpretable Embryo Fragmentation Grading
arXiv:2511.18454v4 Announce Type: replace Abstract: Assessing embryo fragmentation is crucial for predicting IVF success, yet manual grading is prone to subjectivity, and existing AI models struggle with clinical interpretability and segmentation errors. We propose AttnRegDeepLab, a Multi-Task Learning (MTL) framework designed to solve these challenges. The model enhances a DeepLabV3+ decoder with Attention Gates to filter out cytoplasmic noise and retain sharp contour details.
Grading the first month for all 15 WNBA teams
It's evaluation time. Which WNBA teams have the best report card almost four weeks into the 2026 season? We've graded all 15 teams based on how they've performed over the first month while also considering the varying expectations each faced early.
AttnRegDeepLab: A Two-Stage Decoupled Framework for Interpretable Embryo Fragmentation Grading
arXiv:2511.18454v3 Announce Type: replace Abstract: Embryo fragmentation is a morphological indicator critical for evaluating developmental potential in In Vitro Fertilization (IVF). However, manual grading is subjective and inefficient, while existing deep learning solutions often lack clinical explainability or suffer from accumulated errors in segmentation area estimation. To address these issues, this study proposes AttnRegDeepLab (Attention-Guided Regression DeepLab), a framework...