Summary of Automated Assessment Of Multimodal Answer Sheets in the Stem Domain, by Rajlaxmi Patil et al.
Automated Assessment of Multimodal Answer Sheets in the STEM domain
by Rajlaxmi Patil, Aditya Ashutosh Kulkarni, Ruturaj Ghatage, Sharvi Endait, Geetanjali Kale, Raviraj Joshi
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents an innovative approach to automating grading processes in STEM education using Artificial Intelligence (AI). The research focuses on developing efficient and reliable methods for evaluating textual answers and diagram-based assessments. A robust system is designed for evaluating textual answers by leveraging sample answers and advanced algorithms, while a Large Language Model (LLM) is used to enhance diagram evaluation, transforming diagrams into textual representations for nuanced assessment. By integrating models such as CRAFT, YoloV5, and Mistral-7B, the methodology facilitates comprehensive assessment of multimodal answer sheets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI helps revolutionize grading practices in STEM education by developing efficient and reliable methods for evaluating textual answers and diagram-based assessments. The research focuses on improving grading processes using AI-driven approaches, which could have a significant impact on education. |
Keywords
» Artificial intelligence » Large language model
