Adapting University Policies for Generative AI: Opportunities, Challenges, and Policy Solutions in Higher Education
- URL: http://arxiv.org/abs/2506.22231v1
- Date: Fri, 27 Jun 2025 13:49:02 GMT
- Title: Adapting University Policies for Generative AI: Opportunities, Challenges, and Policy Solutions in Higher Education
- Authors: Russell Beale,
- Abstract summary: The rapid proliferation of generative artificial intelligence (AI) tools has ushered in a transformative era in higher education.<n>This article critically examines the opportunities offered by generative AI, explores the multifaceted challenges it poses, and outlines robust policy solutions.
- Score: 1.2691047660244332
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: The rapid proliferation of generative artificial intelligence (AI) tools - especially large language models (LLMs) such as ChatGPT - has ushered in a transformative era in higher education. Universities in developed regions are increasingly integrating these technologies into research, teaching, and assessment. On one hand, LLMs can enhance productivity by streamlining literature reviews, facilitating idea generation, assisting with coding and data analysis, and even supporting grant proposal drafting. On the other hand, their use raises significant concerns regarding academic integrity, ethical boundaries, and equitable access. Recent empirical studies indicate that nearly 47% of students use LLMs in their coursework - with 39% using them for exam questions and 7% for entire assignments - while detection tools currently achieve around 88% accuracy, leaving a 12% error margin. This article critically examines the opportunities offered by generative AI, explores the multifaceted challenges it poses, and outlines robust policy solutions. Emphasis is placed on redesigning assessments to be AI-resilient, enhancing staff and student training, implementing multi-layered enforcement mechanisms, and defining acceptable use. By synthesizing data from recent research and case studies, the article argues that proactive policy adaptation is imperative to harness AI's potential while safeguarding the core values of academic integrity and equity.
Related papers
- A Review of Generative AI in Computer Science Education: Challenges and Opportunities in Accuracy, Authenticity, and Assessment [2.1891582280781634]
This paper surveys the use of Generative AI tools, such as ChatGPT and Claude, in computer science education.<n>Generative AI raises concerns such as AI hallucinations, error propagation, bias, and blurred lines between AI-assisted and student-authored content.
arXiv Detail & Related papers (2025-06-17T19:20:58Z) - The AI Imperative: Scaling High-Quality Peer Review in Machine Learning [49.87236114682497]
We argue that AI-assisted peer review must become an urgent research and infrastructure priority.<n>We propose specific roles for AI in enhancing factual verification, guiding reviewer performance, assisting authors in quality improvement, and supporting ACs in decision-making.
arXiv Detail & Related papers (2025-06-09T18:37:14Z) - To Deepfake or Not to Deepfake: Higher Education Stakeholders' Perceptions and Intentions towards Synthetic Media [0.0]
Deepfake technologies use generative artificial intelligence to mimic a person's likeness or voice.<n>This study investigated stakeholder perceptions and intentions regarding deepfakes in higher education.<n>We found that academic stakeholders demonstrated a relatively low intention to adopt these technologies.
arXiv Detail & Related papers (2025-02-25T10:32:19Z) - An Overview of Large Language Models for Statisticians [109.38601458831545]
Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence (AI)<n>This paper explores potential areas where statisticians can make important contributions to the development of LLMs.<n>We focus on issues such as uncertainty quantification, interpretability, fairness, privacy, watermarking and model adaptation.
arXiv Detail & Related papers (2025-02-25T03:40:36Z) - Analyzing the Impact of AI Tools on Student Study Habits and Academic Performance [0.0]
The research focuses on how AI tools can support personalized learning, adaptive test adjustments, and provide real-time classroom analysis.<n>Student feedback revealed strong support for these features, and the study found a significant reduction in study hours alongside an increase in GPA.<n>Despite these benefits, challenges such as over-reliance on AI and difficulties in integrating AI with traditional teaching methods were also identified.
arXiv Detail & Related papers (2024-12-03T04:51:57Z) - Auto-assessment of assessment: A conceptual framework towards fulfilling the policy gaps in academic assessment practices [4.770873744131964]
We surveyed 117 academics from three countries (UK, UAE, and Iraq)
We identified that most academics retain positive opinions regarding AI in education.
For the first time, we propose a novel AI framework for autonomously evaluating students' work.
arXiv Detail & Related papers (2024-10-28T15:22:37Z) - Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants [176.39275404745098]
We evaluate whether two AI assistants, GPT-3.5 and GPT-4, can adequately answer assessment questions.<n>GPT-4 answers an average of 65.8% of questions correctly, and can even produce the correct answer across at least one prompting strategy for 85.1% of questions.<n>Our results call for revising program-level assessment design in higher education in light of advances in generative AI.
arXiv Detail & Related papers (2024-08-07T12:11:49Z) - The Evolution of Learning: Assessing the Transformative Impact of Generative AI on Higher Education [0.0]
Generative Artificial Intelligence models such as ChatGPT have experienced a surge in popularity.
This research paper investigates the impact of GAI on university students and Higher Education Institutions.
arXiv Detail & Related papers (2024-04-16T13:19:57Z) - Evaluating Large Language Models on the GMAT: Implications for the
Future of Business Education [0.13654846342364302]
This study introduces the first benchmark to assess the performance of seven major Large Language Models (LLMs)
Our analysis shows that most LLMs outperform human candidates, with GPT-4 Turbo not only outperforming the other models but also surpassing the average scores of graduate students at top business schools.
While AI's promise in education, assessment, and tutoring is clear, challenges remain.
arXiv Detail & Related papers (2024-01-02T03:54:50Z) - Human-Centric Multimodal Machine Learning: Recent Advances and Testbed
on AI-based Recruitment [66.91538273487379]
There is a certain consensus about the need to develop AI applications with a Human-Centric approach.
Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes.
We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
arXiv Detail & Related papers (2023-02-13T16:44:44Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z) - Personalized Education in the AI Era: What to Expect Next? [76.37000521334585]
The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to meet her desired goal.
In recent years, the boost of artificial intelligence (AI) and machine learning (ML) has unfolded novel perspectives to enhance personalized education.
arXiv Detail & Related papers (2021-01-19T12:23:32Z)
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.