The article examines how AI‑assisted item generation is filtered by a computational evaluator before expert review, focusing on representation, structural screening, and candidate‑form dependence. Through two in‑silico studies of 32,000 Big Five items, the authors show that subtle differences in semantic representation and structural evaluation lead to divergent item selections, even when overall content coverage appears stable. The findings reveal that the evaluator, often treated as a neutral technical step, actually shapes the evidence and wording that psychometricians ultimately review, highlighting its role as a revisable component of measurement design.
arXiv:2608. 06609v1 Announce Type: new Abstract: Automated item evaluation (AIE) refers to the use of computational methods to assess item quality without requiring manual expert review or field testing of the items under evaluation.
arXiv:2608.20385v1 Announce Type: new Abstract: Systematic reviews rely on quality appraisal of included studies, a process that is time-consuming and sensitive to ambiguity in checklist criteria. Al...
arXiv:2608.21374v1 Announce Type: new Abstract: Literature reviews are essential to scientific progress, but rigorously evaluating automatically generated reviews remains difficult because many aspec...
arXiv:2607. 16989v1 Announce Type: cross Abstract: Introduction.
arXiv:2608. 03659v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate scientific reviews, yet existing evaluations rarely examine whether different providers align with both conference decisions and human reviewing priorities within the same controlled setting.
arXiv:2608. 10385v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as relevance assessors in information retrieval (IR) evaluation, raising questions about how assessor framing affects judgment reliability and downstream system comparison.
The paper introduces a dual‑dimensional framework called Automated Item Similarity Analysis (AISA) that uses Large Language Models to assess incidental content similarity in large‑scale assessments. It combines Structured Decomposition and Semantic Relatedness to capture both structural and semantic nuances that traditional metrics miss. Psychometric validation shows that LLM‑derived metrics better align with construct‑irrelevant local dependence and produce more coherent item groupings, and simulations in Computerized Adaptive Testing demonstrate improved estimation stability and reduced bias with minimal efficiency loss.
arXiv:2606. 15887v1 Announce Type: cross Abstract: Large language model (LLM) systems are increasingly proposed to assist peer review, yet most evaluations judge the prose of machine-generated review text, not the validity of the numeric score a system assigns.
arXiv:2510. 02027v2 Announce Type: replace Abstract: Scholarly publishing requires scalable scrutiny supported by auditable evidence.
arXiv:2606. 27383v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as research assistants, yet it remains unclear whether they can calibrate research takeaways to the strength and scope of the supporting evidence.
arXiv:2606. 30256v1 Announce Type: new Abstract: Safety benchmarks often buy scalability by fixing the prompt, the language, and the turn structure.
arXiv:2608. 04549v1 Announce Type: cross Abstract: Frontier LLMs are increasingly put to use on open-ended complex questions, different in nature from the ones they are typically evaluated on.