Integrating Natural Language Prompting Tasks in Introductory Programming Courses
Project Overview
The document explores the role of generative AI in education, particularly through the implementation of Natural Language Prompting tasks in introductory programming courses. It underscores how these tasks can transform the learning experience by shifting the focus from rote syntax mastery to a more engaging problem-solving approach, thereby making programming accessible to a broader and more diverse range of students. The findings reveal that students who typically face challenges in traditional programming assessments tend to perform better with natural language tasks, suggesting a significant pedagogical shift is necessary to adapt to the evolving educational landscape influenced by AI technologies. This evidence highlights the potential of generative AI not only to enhance learning outcomes but also to foster inclusivity and engagement among students who may otherwise struggle with conventional programming methodologies. Overall, the document advocates for a reevaluation of teaching strategies in the context of AI advancements to better support student learning and success.
Key Applications
Natural Language Prompting Tasks for coding
Context: Introductory programming course for engineering students with varying levels of prior experience.
Implementation: Incorporated two types of prompt-focused activities into lab sessions: EiPE (Explain in Plain English) and Prompt Problems.
Outcomes: Students reported improved understanding of code logic and communication about programming tasks. Performance on natural language tasks was less correlated with self-reported difficulty, suggesting they assess different skills compared to traditional assessments.
Challenges: Students initially found crafting effective prompts challenging. Some perceived the tasks as gimmicky.
Implementation Barriers
Skill barrier
Students struggled to form successful prompts and understand generated code, which hinders effective interaction with LLMs.
Proposed Solutions: Explicit teaching of prompt engineering and iterative learning strategies.
Perception barrier
Some students viewed natural language tasks as gimmicky and less effective than traditional programming tasks.
Proposed Solutions: Positive feedback from other students suggests reinforcing the pedagogical value of these tasks.
Project Team
Chris Kerslake
Researcher
Paul Denny
Researcher
David H Smith IV
Researcher
James Prather
Researcher
Juho Leinonen
Researcher
Andrew Luxton-Reilly
Researcher
Stephen MacNeil
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Chris Kerslake, Paul Denny, David H Smith IV, James Prather, Juho Leinonen, Andrew Luxton-Reilly, Stephen MacNeil
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