Pensieve Grader: An AI-Powered, Ready-to-Use Platform for Effortless Handwritten STEM Grading
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Abstract (arXiv)
Grading handwritten, open-ended responses remains a major bottleneck in large university STEM courses. We introduce Pensieve (https://www.pensieve.co), an AI-assisted grading platform that leverages large language models (LLMs) to transcribe and evaluate student work, providing instructors with rubric-aligned scores, transcriptions, and confidence ratings. Unlike prior tools that focus narrowly on specific tasks like transcription or rubric generation, Pensieve supports the entire grading pipeline-from scanned student submissions to final feedback-within a human-in-the-loop interface. Pensieve has been deployed in real-world courses at over 20 institutions and has graded more than 300,000 student responses. We present system details and empirical results across four core STEM disciplines: Computer Science, Mathematics, Physics, and Chemistry. Our findings show that Pensieve reduces grading time by an average of 65%, while maintaining a 95.4% agreement rate with instructor-assigned grades for high-confidence predictions.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 4 of its 10 references that are in the map. First posted on arXiv 2 Jul 2025.
Builds on
4 of 10 references.
References · 4
- 2023Applying Large Language Models and Chain-of-Thought for Automatic Scoringcited by 69
- 2024A LLM-Powered Automatic Grading Framework with Human-Level Guidelines Optimizationcited by 16
- 2025LLM-based Automated Grading with Human-in-the-Loopcited by 13
- 2025SteLLA: A Structured Grading System Using LLMs with RAGcited by 3
Built on by
Citing papers per year: 2025: 1; 2026: 2.
3 papers.
- 2025Rubric-Conditioned LLM Grading: Alignment, Uncertainty, and Robustnesscited by 5
- 2026EDU-CIRCUIT-HW: Evaluating Multimodal Large Language Models on Real-World University-Level STEM Student Handwritten Solutionscited by 0
- 2026Agentic AI Ecosystems in Higher Education: A Perspective on AI Agents to Emerging Inclusive, Agentic Multi-Agent AI Framework for Learning, Teaching and Institutional Intelligencecited by 0
- 2026Agentic AI Ecosystems in Higher Education: A Perspective on AI Agents to Emerging Inclusive, Agentic Multi-Agent AI Framework for Learning, Teaching and Institutional Intelligence14 May 2026
- 2026EDU-CIRCUIT-HW: Evaluating Multimodal Large Language Models on Real-World University-Level STEM Student Handwritten Solutions23 Jan 2026
- 2025Rubric-Conditioned LLM Grading: Alignment, Uncertainty, and Robustness21 Dec 2025