Use of AI Tools: Guidelines to Maintain Academic Integrity in Computing Colleges
🤖 AI Summary
This study addresses the significant challenge posed by the widespread adoption of AI tools such as ChatGPT to academic integrity in computing education. It systematically evaluates the susceptibility of various assessment formats in computing courses to AI-generated responses and introduces a novel framework for guiding AI use, integrating general pedagogical principles with assessment-specific strategies. Furthermore, the work proposes a formal mathematical model to quantify the authenticity of student submissions when AI assistance is employed. Drawing on educational assessment theory, policy design, and formal modeling, this research establishes a practical set of instructional guidelines that effectively balance the pedagogical benefits of AI augmentation against the risks to academic integrity, all while preserving core learning objectives.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
AI tools
academic integrity
computing education
academic misconduct
assessment
Innovation
Methods, ideas, or system contributions that make the work stand out.
academic integrity
AI-assisted assessment
formal evaluation model
computing education
pedagogical guidelines
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