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

arXiv:2604.1111162.7h-index: 17
Predicted impact top 23% in CY · last 90 daysOriginality Synthesis-oriented
AI Analysis

For computing educators, this provides practical guidelines to address academic integrity challenges posed by AI tools, though it is an incremental contribution.

The paper proposes guidelines for maintaining academic integrity when using AI tools in computing education, including a formal model for evaluating assessments. It classifies assessment techniques and provides recommendations to balance AI benefits with integrity concerns.

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.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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