HFEPX · Eval paper review
Momoka Furuhashi, Kouta Nakayama, Noboru Kawai, Takashi Kodama +2 more
Published
Feb 12, 2026
Citations
0
Trust level
Low
Usefulness score
40/100 (Low)
Extraction confidence
45% (Low)
Derived from extracted protocol signals and abstract evidence.
Rater population
Not reported
Signals refreshed
Aug 12, 2026
This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.
Use this as background context only. Do not make protocol decisions from this page alone.
Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.
Best use
Background context only
Use if you need
Background context only.
What to verify
Read the full paper before copying any benchmark, metric, or protocol choices.
Main weakness
The available metadata is too thin to trust this as a primary source.
Treat as adjacent context, not a core eval-method reference.
If you are doing eval pipeline work, start here
Although large language models (LLMs) show promise for generating educational feedback, it remains unclear how feedback should be designed (e.g., tone and information coverage) to support answer revision and learner acceptance across diverse learner profiles. We define six feedback designs for multiple-choice biology questions, including a baseline design and variants with additional feedback elements, and conduct an empirical study with high school students. We evaluate feedback using immediate revision performance and six subjective evaluation criteria, and analyze how feedback preferences vary across learner profiles based on personality traits. Our results show that feedback with clear and comprehensive guidance improves revision performance and receives favorable evaluations across learner profiles, whereas informational novelty and affective framing vary across profiles. These findings suggest that learner profiles should be considered when designing LLM-generated feedback.
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.
Pairwise Preference, Critique Edit
Directly usable for protocol triage.
"Although large language models (LLMs) show promise for generating educational feedback, it remains unclear how feedback should be designed (e.g., tone and information coverage) to support answer revision and learner acceptance across diverse learner profiles."
None explicit
Validate eval design from full paper text.
"Although large language models (LLMs) show promise for generating educational feedback, it remains unclear how feedback should be designed (e.g., tone and information coverage) to support answer revision and learner acceptance across diverse learner profiles."
Not reported
No explicit QC controls found.
"Although large language models (LLMs) show promise for generating educational feedback, it remains unclear how feedback should be designed (e.g., tone and information coverage) to support answer revision and learner acceptance across diverse learner profiles."
Not extracted
No benchmark anchors detected.
"Although large language models (LLMs) show promise for generating educational feedback, it remains unclear how feedback should be designed (e.g., tone and information coverage) to support answer revision and learner acceptance across diverse learner profiles."
Not extracted
No metric anchors detected.
"Although large language models (LLMs) show promise for generating educational feedback, it remains unclear how feedback should be designed (e.g., tone and information coverage) to support answer revision and learner acceptance across diverse learner profiles."
No benchmark or dataset names were extracted from the available abstract.
No metric terms were extracted from the available abstract.
Although large language models (LLMs) show promise for generating educational feedback, it remains unclear how feedback should be designed (e.g., tone and information coverage) to support answer revision and learner acceptance across diverse learner profiles.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Human feedback protocol is explicit
Detected: Pairwise Preference, Critique Edit
Evaluation mode is explicit
No clear evaluation mode extracted.
Quality control reporting appears
No calibration/adjudication/IAA control explicitly detected.
Benchmark or dataset anchors are present
No benchmark/dataset anchor extracted from abstract.
Metric reporting is present
No metric terms extracted.
