Demystify, Use, Reflect: Preparing students to be informed LLM-users

TL;DR

This paper describes how a computer science course was redesigned to include structured, ethical, and practical instruction on Large Language Models (LLMs), enhancing students' understanding and responsible use of AI tools.

Contribution

The paper introduces a novel course framework integrating LLM education with reflection, verification, and ethical considerations for CS students.

Findings

Students' understanding of LLMs became more technical.

Students' use of LLMs shifted to be more discerning.

Students engaged more critically with AI tools.

Abstract

We transitioned our post-CS1 course that introduces various subfields of computer science so that it integrates Large Language Models (LLMs) in a structured, critical, and practical manner. It aims to help students develop the skills needed to engage meaningfully and responsibly with AI. The course now includes explicit instruction on how LLMs work, exposure to current tools, ethical issues, and activities that encourage student reflection on personal use of LLMs as well as the larger evolving landscape of AI-assisted programming. In class, we demonstrate the use and verification of LLM outputs, guide students in the use of LLMs as an ingredient in a larger problem-solving loop, and require students to disclose and acknowledge the nature and extent of LLM assistance. Throughout the course, we discuss risks and benefits of LLMs across CS subfields. In our first iteration of the course, we collected and analyzed data from students pre and post surveys. Student understanding of how LLMs work became more technical, and their verification and use of LLMs shifted to be more discerning and collaborative. These strategies can be used in other courses to prepare students for the AI-integrated future.