Does GPT Really Get It? A Hierarchical Scale to Quantify Human vs AI's Understanding of Algorithms
Project Overview
The document explores the role of generative AI, particularly large language models (LLMs) like GPT-4, in education, emphasizing their understanding of algorithms compared to human students in computer science. It introduces a hierarchical scale for assessing levels of understanding, tested through surveys involving both students and LLMs. The findings reveal that while LLMs exhibit a functional grasp of algorithms, they do not match human reasoning capabilities, especially in mathematical contexts. Additionally, the document underscores the importance of evaluating the understanding of AI in educational environments and cautions against the potential risks associated with an over-reliance on these AI tools. Overall, it highlights the need for a balanced approach to integrating AI in education, acknowledging its capabilities while remaining aware of its limitations.
Key Applications
AI-Assisted Learning in Programming and Algorithms
Context: Undergraduate and graduate students in computer science, including novice programmers and software developers, utilizing AI models like GPT-4 and code generation tools such as GitHub Copilot.
Implementation: Implemented AI technologies to assess understanding of algorithms (e.g., Euclidean and Ford-Fulkerson) and assist in coding tasks through practical applications. Surveys and practical coding environments were used to evaluate the efficacy of these tools.
Outcomes: Demonstrated that GPT-4 performs comparably to advanced human learners in algorithm understanding while improving productivity and learning for novice programmers. However, limitations were noted in mathematical reasoning and generating accurate examples.
Challenges: Both AI models and tools risk generating flawed code or security vulnerabilities, and they show weaknesses in reasoning with mathematical tasks.
Implementation Barriers
Ethical/Legal
Concerns regarding the safety, legality, and ethical implications of relying on LLMs.
Proposed Solutions: Establish rigorous criteria and guidelines for evaluating AI outputs.
Technical
Limitations of LLMs, such as hallucinations and lack of true understanding, impacting their reliability.
Proposed Solutions: Continuous research and development to improve AI's reasoning and understanding capabilities.
Project Team
Mirabel Reid
Researcher
Santosh S. Vempala
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Mirabel Reid, Santosh S. Vempala
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