Prompts First, Finally

TL;DR

The paper argues that Generative AI catalyzes a natural shift toward problem solving in natural language, proposing a Prompts First approach in Computer Science Education. It traces the historical rise of abstractions from machine code to objects and contends that GenAI makes natural-language problem solving a practical and scalable abstraction level. The authors review empirical work on LLMs in education, discuss opportunities for resource generation and feedback, and address risks such as over-reliance and non-deterministic outputs. They call for precise vocabulary and strengthened debugging skills to harness GenAI’s benefits while mitigating drawbacks, presenting a roadmap for pedagogy that centers on prompt engineering and domain-focused communication.

Abstract

Generative AI (GenAI) and large language models in particular, are disrupting Computer Science Education. They are proving increasingly capable at more and more challenges. Some educators argue that they pose a serious threat to computing education, and that we should ban their use in the classroom. While there are serious GenAI issues that remain unsolved, it may be useful in the present moment to step back and examine the overall trajectory of Computer Science writ large. Since the very beginning, our discipline has sought to increase the level of abstraction in each new representation. We have progressed from hardware dip switches, through special purpose languages and visual representations like flow charts, all the way now to ``natural language.'' With the advent of GenAI, students can finally change the abstraction level of a problem to the ``language'' they've been ``problem solving'' with all their lives. In this paper, we argue that our programming abstractions were always headed here -- to natural language. Now is the time to adopt a ``Prompts First'' approach to Computer Science Education.