SBSC: Step-By-Step Coding for Improving Mathematical Olympiad Performance
Project Overview
The document explores the transformative role of generative AI in education, particularly through the introduction of the Step-by-Step Coding (SBSC) framework, which utilizes Large Language Models (LLMs) for solving complex math problems, including Olympiad-level challenges. By breaking down problems into smaller sub-tasks and iteratively generating solutions, SBSC significantly enhances accuracy and error correction compared to existing methods. Additionally, the document highlights the broader applications of generative AI in educational settings, emphasizing its ability to personalize learning experiences, support teachers, and engage students across various academic disciplines. Through diverse use cases, it illustrates how AI integration can streamline educational processes, facilitate problem-solving, and foster enhanced engagement, ultimately demonstrating the potential of generative AI to revolutionize educational practices and outcomes.
Key Applications
AI-Assisted Problem Solving and Code Generation
Context: Educational contexts focused on K-12 and higher education students learning mathematics and preparing for math competitions. The implementation aids students by generating Python code to solve mathematical problems and providing step-by-step solutions for advanced problem-solving.
Implementation: Implemented as a multi-turn reasoning framework leveraging LLMs like Claude-3.5-Sonnet and GPT-4o. The AI generates Python code based on prompts from students and breaks down complex mathematical problems into manageable sub-tasks, facilitating step-by-step reasoning and coding.
Outcomes: Students receive immediate feedback and solutions, leading to significant improvements in performance on math competition benchmarks (10.7% on AMC12, 8% on AIME, and 12.6% on MathOdyssey) and enhancing their coding and problem-solving skills.
Challenges: Challenges include the need for extensive computational resources, potential biases in training data that may affect performance on complex problems, and the risk that dependence on AI may reduce critical thinking and independent problem-solving skills.
Implementation Barriers
Technical Barrier
Existing math prompting strategies struggle with complex problem-solving due to limitations in handling multiple reasoning steps and dynamic task decomposition. Additionally, access to reliable AI tools and platforms may be limited for some students and institutions.
Proposed Solutions: SBSC addresses complex problem-solving by allowing for granular control through multi-turn interactions and dynamic sub-task generation. Develop partnerships with tech companies to provide resources and training for educators and students.
Data Limitations
State-of-the-art models show bias towards easier problems and struggle with advanced reasoning tasks due to insufficient representation in training datasets.
Proposed Solutions: Improving dataset diversity and incorporating more complex problem types in training sets can mitigate this issue.
Pedagogical
Teachers may lack the knowledge or training to effectively integrate AI tools into their curriculum.
Proposed Solutions: Provide professional development and training sessions on using AI in educational contexts.
Project Team
Kunal Singh
Researcher
Ankan Biswas
Researcher
Sayandeep Bhowmick
Researcher
Pradeep Moturi
Researcher
Siva Kishore Gollapalli
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Kunal Singh, Ankan Biswas, Sayandeep Bhowmick, Pradeep Moturi, Siva Kishore Gollapalli
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai