Enterprise AI Analysis
A Chinese Elementary Science Question Dataset in Problem-Solving Process Generation
This paper introduces the Chinese Science Question (CSQ) dataset, designed to advance personalized science education and evaluate LLMs' capabilities in generating grade-appropriate problem-solving processes. CSQ features 12,000 high-quality samples across diverse scientific disciplines and grade levels at the Chinese primary school.
Key Business Impact Metrics
Fine-tuning LLMs on the CSQ dataset significantly enhances their performance, offering substantial improvements in accuracy and text quality, crucial for educational AI applications.
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High-Quality Samples
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The CSQ dataset focuses on generating problem-solving processes that are appropriate for students' grade levels, enabling LLMs to learn relevant science knowledge and generate adaptive, curriculum-aligned responses.
CSQ comprises 12,000 examples with detailed annotations, including problem information, discipline properties (subject, topic, grade, knowledge, scientific skills), and solutions (answer, problem-solving thought). This rich structure supports fine-tuning for educational AI.
Experiments show that fine-tuning LLMs on CSQ significantly improves accuracy and text quality, especially for life sciences and elementary-level questions. GPT-4o performs well, but CSQ-tuned open-source models close the gap.
While CSQ is tailored for the Chinese elementary science education system, its core scientific concepts and structured annotation framework are universal. With minor adaptations, it can be applied to other Chinese-speaking contexts and potentially serve as a paradigm for developing educationally adaptive LLM benchmarks globally.
Enterprise Process Flow
Data Collection & Source Verification
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Question Design & Adaptation
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Annotation of Problem Information
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Manual Annotation of Discipline Properties & Solutions
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Cross-Validation & Quality Calibration
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Refinement & Iterative Optimization
12,000
High-Quality Samples in CSQ Dataset
| Feature | CSQ Dataset | Existing Datasets (e.g., AI2D, ARC) |
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| Domain Diversity |
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| Annotation Depth |
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| Curriculum Alignment |
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Case Study: Fine-tuning Yi1.5-9B for Grade-Appropriate Responses
Challenge: Original LLMs often produce problem-solving explanations that exceed the student's grade level, making them impractical for elementary education.
Solution: Fine-tuning the Yi1.5-9B model on the CSQ dataset, incorporating its rich discipline properties and grade-level-aligned problem-solving thoughts.
Outcome: The fine-tuned Yi1.5-9B model significantly improved in generating problem-solving processes that are accurate, relevant, complete, and most importantly, appropriate for the specific student grade level, as confirmed by human evaluations.
This demonstrates CSQ's effectiveness in enabling LLMs to acquire science knowledge and pedagogical reasoning aligned with curriculum standards, bridging the gap between advanced AI capabilities and practical educational needs.
Calculate Your Potential AI ROI
Estimate the efficiency gains and cost savings your organization could achieve with AI-powered educational solutions, based on your operational profile.
Estimated Annual Savings
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Your AI Implementation Roadmap
Our phased approach ensures a seamless integration of AI-powered educational tools, tailored to your specific needs and curriculum, from initial assessment to full-scale deployment and continuous improvement.
Phase 1: Needs Assessment & Customization
Collaborate to understand your specific educational objectives, curriculum requirements, and student demographics. Customize the AI solution to align with your pedagogical goals and technical infrastructure.
Phase 2: Data Integration & Model Fine-tuning
Integrate relevant existing educational data and fine-tune AI models using datasets like CSQ to ensure optimal performance in generating grade-appropriate problem-solving processes and responses.
Phase 3: Pilot Program & Feedback Loop
Implement a pilot program with a select group of teachers and students. Gather feedback to identify areas for refinement and ensure the AI tool meets practical classroom needs and enhances learning outcomes.
Phase 4: Full-Scale Deployment & Training
Roll out the AI-powered solution across your institution. Provide comprehensive training for educators to maximize their use of the new tools and integrate them effectively into their teaching methodologies.
Phase 5: Continuous Improvement & Support
Offer ongoing support, monitoring, and updates to ensure the AI solution evolves with your educational needs. Continuously evaluate impact and implement enhancements for sustained effectiveness.
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