FoL 2026
AIED

LLM-based Automated Grading

Tue Jun 30, 10:45 AM–12:00 PM · North 201
Automated Assessment & Scoring Generative AI & Large Language Models
★ Notable speakers
Joseph Krajcik ★★ — Project-based learning in K-12 science education, NGSS writing team leader, learning progressions, educative curriculum materials
Jiliang Tang ★★ — Graph neural networks; deep learning on graphs; data mining; educational data mining; knowledge tracing
Zhongzhou Chen ★ — Mastery/adaptive online learning; MOOC A/B experimentation; physics education research
Shashank Sonkar ★ — Pedagogical alignment of LLMs; cognitive modeling of student reasoning; AI in education at scale
Hendrik Drachsler ★ — Learning analytics; recommender systems for personalized learning; data privacy (DELICATE checklist)
Ulf Kroehne ★ — Technology-based assessment; log/process data analysis; computer-adaptive testing; mode effects in PISA and NEPS
Yasemin Copur-Gencturk ★ — Mathematics teacher professional development; teacher knowledge measurement; technology-enhanced teacher learning
Kevin Haudek ★ — Automated NLP/ML scoring of student constructed responses in science education; director of MSU AACR group

AIED26 (TL) | Technical | Long-paper session

3 talks in this session

When Can We Trust LLM Graders? Calibrating Confidence for Automated Assessment
Robinson Ferrer, Zhongzhou Chen, Shashank Sonkar
Generative AI & Large Language Models Automated Assessment & Scoring Explainable & Trustworthy AI
Confidence Estimation in Automatic Short Answer Grading with LLMs
Longwei Cong, Sonja Hahn, Sebastian Gombert, Leon Camus, Hendrik Drachsler
Generative AI & Large Language Models Automated Assessment & Scoring NLP & Discourse Analysis
Optimizing In-Context Demonstrations for LLM-based Automated Grading
Yucheng Chu, Hang Li, Kaiqi Yang, Yasemin Copur-Gencturk, Kevin Haudek
Generative AI & Large Language Models Automated Assessment & Scoring Prompt Engineering & RAG