Integrating Natural Language Prompting Tasks in Introductory Programming Courses

TL;DR

This paper investigates integrating natural language prompting activities into introductory programming courses to enhance problem-solving skills and engagement, using four labs over six weeks, and examines their impact on student learning and perceptions.

Contribution

It introduces prompt-focused activities in early programming education, demonstrating their potential to develop new skills and appeal to diverse students by shifting focus from syntax to problem-solving.

Findings

Students found programming difficult, especially with syntax and debugging.

Performance on traditional assessments correlated with perceived difficulty.

Natural language tasks showed weaker correlation with self-reported difficulty.

Abstract

Introductory programming courses often emphasize mastering syntax and basic constructs before progressing to more complex and interesting programs. This bottom-up approach can be frustrating for novices, shifting the focus away from problem solving and potentially making computing less appealing to a broad range of students. The rise of generative AI for code production could partially address these issues by fostering new skills via interaction with AI models, including constructing high-level prompts and evaluating code that is automatically generated. In this experience report, we explore the inclusion of two prompt-focused activities in an introductory course, implemented across four labs in a six-week module. The first requires students to solve computational problems by writing natural language prompts, emphasizing problem-solving over syntax. The second involves students crafting prompts to generate code equivalent to provided fragments, to foster an understanding of the relationship between prompts and code. Most of the students in the course had reported finding programming difficult to learn, often citing frustrations with syntax and debugging. We found that self-reported difficulty with learning programming had a strong inverse relationship with performance on traditional programming assessments such as tests and projects, as expected. However, performance on the natural language tasks was less strongly related to self-reported difficulty, suggesting they may target different skills. Learning how to communicate with AI coding models is becoming an important skill, and natural language prompting tasks may appeal to a broad range of students.