Chatbot-Based Assessment of Code Understanding in Automated Programming Assessment Systems
Summary: arXiv:2604.07304v1 Announce Type: cross
In recent years, the rise of Large Language Models (LLMs) has posed significant challenges to conventional automated programming assessment methods. With the ability to generate functionally correct code, students can now submit solutions without adequately demonstrating their understanding of the underlying concepts. This phenomenon has led to an increasing need for innovative assessment strategies that can effectively gauge a student’s comprehension of coding practices.
Key Contributions of the Paper
This paper makes two primary contributions to the discourse surrounding automated programming assessments:
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Saturation-based Scoping Review: The authors conducted a comprehensive review of conversational assessment approaches in programming education. This review revealed three predominant architectural families that define the landscape of automated coding assessments:
- Rule-based or template-driven systems.
- LLM-based systems.
- Hybrid systems that blend multiple approaches.
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Hybrid Socratic Framework: Building upon the findings of the review, the paper introduces a Hybrid Socratic Framework aimed at integrating conversational verification into Automated Programming Assessment Systems (APASs). This innovative framework encompasses:
- Deterministic code analysis.
- A dual-agent conversational layer for interaction.
- Knowledge tracking mechanisms to monitor student progress.
- Scaffolded questioning techniques to deepen understanding.
- Guardrails that align prompts with runtime facts to ensure relevance.
Challenges and Limitations
While conversational agents show promise for scalable feedback and deeper probing into code understanding, the literature highlights several critical limitations:
- Hallucinations: LLMs may produce plausible-sounding but incorrect information.
- Over-reliance: Users may depend too heavily on the conversational agents, undermining their learning.
- Privacy Concerns: Handling sensitive student data poses significant risks.
- Integrity Issues: Ensuring the authenticity of student submissions remains a challenge.
- Deployment Constraints: Technical and logistical barriers may hinder effective implementation.
Practical Safeguards Proposed
To address these concerns, the paper discusses several practical safeguards that can be implemented to enhance the reliability of LLM-generated explanations:
- Proctored Deployment Modes: Ensuring assessments are conducted under supervision to maintain integrity.
- Randomized Trace Questions: Implementing variability in question prompts to discourage rote responses.
- Stepwise Reasoning: Encouraging students to engage in reasoning tied to specific execution states.
- Local-model Deployment Options: Utilizing local models for privacy-sensitive settings to protect student data.
Conclusion
Ultimately, the Hybrid Socratic Framework is designed not to replace traditional testing methods but to serve as a complementary layer. By verifying students’ understanding of the code they submit, this approach aims to enhance the educational experience and ensure that learners are not only capable of coding but also comprehending the principles that underpin their work.