A Comparative Analysis of Institutional and Course Generative AI Policies within Higher Education: Implications for Instruction in Computing Education

• URL: http://arxiv.org/abs/2607.12296v1
• Date: Tue, 14 Jul 2026 03:08:53 GMT
• Title: A Comparative Analysis of Institutional and Course Generative AI Policies within Higher Education: Implications for Instruction in Computing Education
• Abstract summary: Higher education institutions (HEIs) have released a range of guidelines and policies to direct adoption within their institutions.<n>In computer science (CS) courses GenAI adoption is especially high and the implications for student learning are significant.<n>We compare institutional and computing course level guidance to better understand this terrain.
• Score: 4.973919181995083
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: With the increased use of generative AI (GenAI) applications such as ChatGPT, higher education institutions (HEIs) have released a range of guidelines and policies to direct adoption within their institutions. In computer science (CS) courses GenAI adoption is especially high and the implications for student learning are significant. At the same time, instructors have also been forced to address the use of GenAI as students have started to use it for a range of functions. Currently, comparative analysis of guidance provided by institutions and its uptake in instruction is lacking. In this paper we bridge this gap by comparing institutional and computing course level guidance to better understand this terrain. We utilize secondary analysis of institutional and course syllabi guidelines from higher education institutions in the U.S. classified as research-intensive. Our findings reveal that although institutional guidance is more pro-use, at the course-level the uptake is still guarded. We discuss the implications and propose an instructor-centered framework to guide future adoption of GenAI.

Related papers

• Implementing GenAI-Supported Learning in Software Engineering and Computer Science Education using Bloom's Taxonomy [0.4306143768014156]
  Generative AI adoption in software engineering education raises opportunities for learning support alongside concerns about superficial learning and academic integrity.<n>This study investigates how explicit instructional guidance aligned with Bloom's taxonomy supports responsible GenAI use in SE/CS education.
  arXiv  Detail & Related papers  (2026-06-24T10:44:07Z)
• Beyond Detection: Redesigning Assessment and Governande of Generative AI at the Universidad Politécnica de Madrid (UPM) [39.92037610044293]
  Most institutions frame the debate around AI detection, plagiarism, academic integrity and a presumed drop in student effort as defensive.<n>Other group of pioneering universities is pursuing deeper adoption, and assuming that any policy built on prevention or sanction will not hold.<n>What universities need instead is a coordinated effort to set clear, course-by-course rules for GenAI use.<n>UPM is developing a strategic and sustainable AI policy and adoption framework structured around six dimensions.
  arXiv  Detail & Related papers  (2026-06-04T18:04:50Z)
• From Early Adoption to Sustained Use: Understanding GenAI Usage Among Software Developers in Italian SMEs [41.13700651273253]
  This study investigates factors associated with developers' intentions to continue using GenAI tools.<n>In voluntary professional contexts, sustained use is driven primarily by individual-level factors rather than by social and organisational support.
  arXiv  Detail & Related papers  (2026-05-25T15:47:46Z)
• Knowing the Rules Is Not Enough: Student Regulatory Awareness and Use of GenAI in Higher Education [4.908297240926983]
  This study investigates how students awareness of GenAI regulations relates to their perceived compliance and actual usage behavior.<n>Our research objective is to examine the association between regulatory knowledge, GenAI use, and perceived rule conformity among students in computer science related study programs.
  arXiv  Detail & Related papers  (2026-05-16T10:53:44Z)
• A Systematic AI Adoption Framework for Higher Education: From Student GenAI Usage to Institutional Integration [4.908297240926983]
  This study investigates how students utilize generative artificial intelligence tools in computer science-oriented disciplines.<n>It develops a structured, lightweight framework supporting institutional adaptation to pervasive GenAI usage.
  arXiv  Detail & Related papers  (2026-04-23T19:46:15Z)
• From Recall to Reasoning: Automated Question Generation for Deeper Math Learning through Large Language Models [44.99833362998488]
  We investigated the first steps for optimizing content creation for advanced math.<n>We looked at the ability of GenAI to produce high-quality practice problems that are relevant to the course content.
  arXiv  Detail & Related papers  (2025-05-17T08:30:10Z)
• Early Adoption of Generative Artificial Intelligence in Computing Education: Emergent Student Use Cases and Perspectives in 2023 [38.83649319653387]
  There is limited prior research on computing students' use and perceptions of GenAI. We surveyed all computer science majors in a small engineering-focused R1 university. We discuss the impact of our findings on the emerging conversation around GenAI and education.
  arXiv  Detail & Related papers  (2024-11-17T20:17:47Z)
• Analysis of Generative AI Policies in Computing Course Syllabi [3.7869332128069773]
  Since the release of ChatGPT in 2022, Generative AI (GenAI) is increasingly being used in higher education computing classrooms across the U.S. We collected 98 computing course syllabi from 54 R1 institutions in the U.S. and studied the GenAI policies they adopted and the surrounding discourse. Our analysis shows that 1) most instructions related to GenAI use were as part of the academic integrity policy for the course and 2) most syllabi prohibited or restricted GenAI use, often warning students about the broader implications of using GenAI.
  arXiv  Detail & Related papers  (2024-10-29T17:34:10Z)
• Environment Scan of Generative AI Infrastructure for Clinical and Translational Science [35.90108933392196]
  This study reports a comprehensive scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions. This research explores the current status of GenAI integration, focusing on stakeholder roles, governance structures, and ethical considerations.
  arXiv  Detail & Related papers  (2024-09-28T01:53:13Z)
• Generative AI in Higher Education: Seeing ChatGPT Through Universities' Policies, Resources, and Guidelines [11.470910427306569]
  This study analyzes academic policies and guidelines established by top-ranked U.S. universities regarding the use of GenAI. Results show that the majority of these universities adopt an open but cautious approach towards GenAI. Findings provide four practical implications for educators in teaching practices.
  arXiv  Detail & Related papers  (2023-12-08T18:33:11Z)
• An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates [53.04281982845422]
  We present an experience report of teaching an AI course to business executives in the United Arab Emirates (UAE) Rather than focusing only on theoretical and technical aspects, we developed a course that teaches AI with a view to enabling students to understand how to incorporate it into existing business processes.
  arXiv  Detail & Related papers  (2022-02-02T20:59:53Z)
• Towards Fairness Certification in Artificial Intelligence [31.920661197618195]
  We propose a first joint effort to define the operational steps needed for AI fairness certification. We will overview the criteria that should be met by an AI system before coming into official service and the conformity assessment procedures useful to monitor its functioning for fair decisions.
  arXiv  Detail & Related papers  (2021-06-04T14:12:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.