most citedLLM Essay Scoring Under Holistic and Analytic Rubrics: Prompt Effects and Bias

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.HC2026

From Surface Learning to Deep Understanding: A Grounded AI Tutoring System for Moodle

Anna Ostrowska, Michał Kukla, Gabriela Majstrak +4

This demo paper describes the development of the AI Teaching \& Learning Assistant, a modular Moodle plugin that leverages Retrieval-Augmented Generation (RAG) to deliver high-qual…

cs.CL2026

Clickbait detection: quick inference with maximum impact

Soveatin Kuntur, Panggih Kusuma Ningrum, Anna Wróblewska +2

We propose a lightweight hybrid approach to clickbait detection that combines OpenAI semantic embeddings with six compact heuristic features capturing stylistic and informational c…

cs.CL2026

Graph Neural Networks for Misinformation Detection: Performance-Efficiency Trade-offs

Soveatin Kuntur, Maciej Krzywda, Anna Wróblewska +4

The rapid spread of online misinformation has led to increasingly complex detection models, including large language models and hybrid architectures. However, their computational c…

cs.CL20261 cited

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

Filip J. Kucia, Anirban Chakraborty, Anna Wróblewska

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 eva…

cs.CL2026

On the Effectiveness of LLM-Specific Fine-Tuning for Detecting AI-Generated Text

Michał Gromadzki, Anna Wróblewska, Agnieszka Kaliska

The rapid progress of large language models has enabled the generation of text that closely resembles human writing, creating challenges for authenticity verification in education,…