arXiv (All)
open access
Using Evidence-Centered Design to Guide Prompt Engineering in Measurement: An Illustration with Automated Essay Scoring
Generative artificial intelligence (GenAI) models, particularly large language models (LLMs), are increasingly used in educational settings for tasks such as item generation, automated scoring, and feedback delivery. The quality of these outputs depends not only on the AI model but also on how users
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- From Efficiency to Sufficiency: A Measurement-Informed Framework for Cost-Aware Automated Essay Scoring#302245
- Chem-AI: Reframing AI-Assisted Research Through Structured, Retrieval-Based Scientific Literature Analysis#1263494
- Benchmarking Prompt Optimization of Large Language Models With Chess#1155351
- Overview of the NTCIR-19 Automatic Evaluation of LLMs 2 (AEOLLM-2) Task#1248878
- Do Your Own Research: Learning to Forecast by Learning to Search#1161059
Record · ID 1326481
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive