# Explaining Code with a Purpose: An Integrated Approach for Developing Code Comprehension and Prompting Skills

arXiv: https://arxiv.org/abs/2403.06050 · HTML: https://mlsanity.com/paper/2403.06050/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2024.

## Built on by (most cited first, 11 of 11)

- [Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learning](https://mlsanity.com/paper/2410.08922/) (2024)
- [Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools](https://mlsanity.com/paper/2412.14732/) (2024)
- [From Prompts to Propositions: A Logic-Based Lens on Student-LLM Interactions](https://mlsanity.com/paper/2504.18691/) (2025)
- [Breaking the Programming Language Barrier: Multilingual Prompting to Empower Non-Native English Learners](https://mlsanity.com/paper/2412.12800/) (2024)
- [Integrating Natural Language Prompting Tasks in Introductory Programming Courses](https://mlsanity.com/paper/2410.03063/) (2024)
- [Concept Attractors in LLMs and their Applications](https://mlsanity.com/paper/2601.11575/) (2025)
- [AI In Cybersecurity Education -- Scalable Agentic CTF Design Principles and Educational Outcomes](https://mlsanity.com/paper/2603.21551/) (2026)
- [Comparing the Utility, Preference, and Performance of Course Material Search Functionality and Retrieval-Augmented Generation Large Language Model (RAG-LLM) AI Chatbots in Information-Seeking Tasks](https://mlsanity.com/paper/2410.13326/) (2024)
- [Exploring the Effectiveness of Using LLMs for Automated Assessment of Student Self Explanations in Programming Education](https://mlsanity.com/paper/2605.21614/) (2026)
- [Commenting with Copilot: A Taxonomy and Multi-Year Analysis of Student Code-Generation Specifications](https://mlsanity.com/paper/2607.10674/) (2026)
- [Is Solving Better Than Evaluating GenAI Solutions?](https://mlsanity.com/paper/2607.27586/) (2026)

## Abstract (arXiv)

Reading, understanding and explaining code have traditionally been important skills for novices learning programming. As large language models (LLMs) become prevalent, these foundational skills are more important than ever given the increasing need to understand and evaluate model-generated code. Brand new skills are also needed, such as the ability to formulate clear prompts that can elicit intended code from an LLM. Thus, there is great interest in integrating pedagogical approaches for the development of both traditional coding competencies and the novel skills required to interact with LLMs. One effective way to develop and assess code comprehension ability is with ``Explain in plain English'' (EiPE) questions, where students succinctly explain the purpose of a fragment of code. However, grading EiPE questions has always been difficult given the subjective nature of evaluating written explanations and this has stifled their uptake. In this paper, we explore a natural synergy between EiPE questions and code-generating LLMs to overcome this limitation. We propose using an LLM to generate code based on students' responses to EiPE questions -- not only enabling EiPE responses to be assessed automatically, but helping students develop essential code comprehension and prompt crafting skills in parallel. We investigate this idea in an introductory programming course and report student success in creating effective prompts for solving EiPE questions. We also examine student perceptions of this activity and how it influences their views on the use of LLMs for aiding and assessing learning.