Use of AI Tools: Guidelines to Maintain Academic Integrity in Computing Colleges

TL;DR

This paper discusses how computing colleges can responsibly integrate AI tools like ChatGPT into education by providing guidelines and a formal model to uphold academic integrity amidst their growing use.

Contribution

It offers a set of general and assessment-specific guidelines for instructors to balance AI benefits with academic honesty, including a formal evaluation model.

Findings

Classified assessment techniques impacted by AI tools

Proposed guidelines for responsible AI integration

Introduced a formal model for evaluating assessments with AI

Abstract

The rapid adoption of AI tools such as ChatGPT has significantly transformed academic practices, offering considerable benefits for both students and faculty in computing disciplines. These tools have been shown to enhance learning efficiency, academic self-efficacy, and confidence. However, their increasing use also raises pressing concerns regarding the preservation of academic integrity -- an essential pillar of the educational process. This paper explores the implications of widespread AI tool usage within computing colleges, with a particular focus on how to align their use with the principles of academic honesty. We begin by classifying common assessment techniques employed in computing education and examine how each may be impacted by AI-assisted tools. Building on this foundation, we propose a set of general guidelines applicable across various assessment formats to help instructors responsibly integrate AI tools into their pedagogy. Furthermore, we provide targeted, assessment-specific recommendations designed to uphold educational objectives while mitigating risks of academic misconduct. These guidelines serve as a practical framework for instructors aiming to balance the pedagogical advantages of AI tools with the imperative of maintaining academic integrity in computing education. Finally, we introduce a formal model that provides a structured mathematical framework for evaluating student assessments in the presence of AI-assisted tools.