arXiv (All)
open access
Machine--human agreement in marking undergraduate assignments is specific to model, prompt and institution
Large language models (LLMs) promise to automate one of higher education's most resource-intensive activities: assessing student writing. Existing evaluations are typically limited to a single model, a single prompt and a single institution. Using 761 undergraduate psychology assignments from three
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
- Architecture- and data-constrained LLMs don't produce childlike language#1079018
- Do as I Say, Not as I Do: Instruction-Induction Conflict in LLMs#821453
- Task Competence Is Not Instruction Following: Evaluating Instruction-Conflicting Behavior in Small Language Models#1342563
- A retrospective analysis on the use of LLMs to study infant syntax learning#1062429
- Tasks over Application Manuals: Revealing Gaps in Long-Horizon Procedural Reasoning for Language Models#1070513
Record · ID 1343216
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