Skip to content
OpenTrain AIFor AI Companies

HFEPX · Eval paper review

LLM Essay Scoring Under Holistic and Analytic Rubrics: Prompt Effects and Bias

Filip J. Kucia, Anirban Chakraborty, Anna Wróblewska

Published

Mar 31, 2026

Citations

0

Trust level

High

Usefulness score

77/100 (High)

Extraction confidence

80% (High)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Mar 31, 2026

Open arXiv Find implementation Shortlist (0) RSS feed

Tags

General Human Eval Rubric Rating

Should you rely on this paper?

This paper has strong direct human-feedback and evaluation protocol signal and is suitable as a primary eval pipeline reference.

Use this as a practical starting point for protocol research, then validate against the original paper.

Best use

Primary benchmark and eval reference

Use if you need

A concrete protocol example with enough signal to inform rater workflow design.

What to verify

Validate the exact study setup in the full paper before operational use.

Main weakness

No major weakness surfaced.

Human feedback signal
Detected
From extracted signals
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
77/100
High-confidence candidate

Use this as a primary source when designing or comparing eval protocols.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

Despite growing interest in using Large Language Models (LLMs) for educational assessment, it remains unclear how closely they align with human scoring. We present a systematic evaluation of instruction-tuned LLMs across three open essay-scoring datasets (ASAP 2.0, ELLIPSE, and DREsS) that cover both holistic and analytic scoring. We analyze agreement with human consensus scores, directional bias, and the stability of bias estimates. Our results show that strong open-weight models achieve moderate to high agreement with humans on holistic scoring (Quadratic Weighted Kappa about 0.6), but this does not transfer uniformly to analytic scoring. In particular, we observe large and stable negative directional bias on Lower-Order Concern (LOC) traits, such as Grammar and Conventions, meaning that models often score these traits more harshly than human raters. We also find that concise keyword-based prompts generally outperform longer rubric-style prompts in multi-trait analytic scoring. To quantify the amount of data needed to detect these systematic deviations, we compute the minimum sample size at which a 95% bootstrap confidence interval for the mean bias excludes zero. This analysis shows that LOC bias is often detectable with very small validation sets, whereas Higher-Order Concern (HOC) traits typically require much larger samples. These findings support a bias-correction-first deployment strategy: instead of relying on raw zero-shot scores, systematic score offsets can be estimated and corrected using small human-labeled bias-estimation sets, without requiring large-scale fine-tuning.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

strong

Rubric Rating

Directly usable for protocol triage.

"Despite growing interest in using Large Language Models (LLMs) for educational assessment, it remains unclear how closely they align with human scoring."

Evaluation Modes

strong

Human Eval

Includes extracted eval setup.

"Despite growing interest in using Large Language Models (LLMs) for educational assessment, it remains unclear how closely they align with human scoring."

Quality Controls

strong

Inter Annotator Agreement Reported, Adjudication

Calibration/adjudication style controls detected.

"Despite growing interest in using Large Language Models (LLMs) for educational assessment, it remains unclear how closely they align with human scoring."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Despite growing interest in using Large Language Models (LLMs) for educational assessment, it remains unclear how closely they align with human scoring."

Reported Metrics

strong

Kappa, Agreement

Useful for evaluation criteria comparison.

"We analyze agreement with human consensus scores, directional bias, and the stability of bias estimates."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

kappaagreement
Human feedback details
Uses human feedback
Yes
Feedback types
Rubric Rating
Rater population
Not reported
Unit of annotation
Multi Dim Rubric
Expertise required
General
Evaluation details
Evaluation modes
Human Eval
Agentic eval
None
Quality controls
Inter Annotator Agreement Reported, Adjudication
Evidence quality
High
Use this page as
Primary benchmark and eval reference

Research brief

Metadata summary

Despite growing interest in using Large Language Models (LLMs) for educational assessment, it remains unclear how closely they align with human scoring.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Despite growing interest in using Large Language Models (LLMs) for educational assessment, it remains unclear how closely they align with human scoring.
  • We present a systematic evaluation of instruction-tuned LLMs across three open essay-scoring datasets (ASAP 2.0, ELLIPSE, and DREsS) that cover both holistic and analytic scoring.
  • We analyze agreement with human consensus scores, directional bias, and the stability of bias estimates.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Rubric rating evaluation Agreement / Kappa

Contribution summary

  • Despite growing interest in using Large Language Models (LLMs) for educational assessment, it remains unclear how closely they align with human scoring.
  • We present a systematic evaluation of instruction-tuned LLMs across three open essay-scoring datasets (ASAP 2.0, ELLIPSE, and DREsS) that cover both holistic and analytic scoring.
  • Our results show that strong open-weight models achieve moderate to high agreement with humans on holistic scoring (Quadratic Weighted Kappa about 0.6), but this does not transfer uniformly to analytic scoring.

Why it matters for eval

  • We present a systematic evaluation of instruction-tuned LLMs across three open essay-scoring datasets (ASAP 2.0, ELLIPSE, and DREsS) that cover both holistic and analytic scoring.
  • Our results show that strong open-weight models achieve moderate to high agreement with humans on holistic scoring (Quadratic Weighted Kappa about 0.6), but this does not transfer uniformly to analytic scoring.

Researcher checklist

  • Human feedback protocol is explicit

    Detected: Rubric Rating

  • Evaluation mode is explicit

    Detected: Human Eval

  • Quality control reporting appears

    Detected: Inter Annotator Agreement Reported, Adjudication

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    Detected: kappa, agreement