[orcid=0009-0003-7309-4984]

[1]

[1]

1]organization=Centre for Learning Analytics at Monash, Faculty of Information
Technology, Monash University, city=Clayton, postcode=3168, state=Victoria,
country=Australia

[1]Corresponding author

[1]Correspondence to Yueqiao Jin, Centre for Learning Analytics at Monash,
Faculty of Information Technology, Monash University, Clayton, Victoria, 3168,
Australia. Email<EMAIL_ADDRESS>
# Generative AI in Higher Education: A Global Perspective of Institutional
Adoption Policies and Guidelines

Yueqiao Jin<EMAIL_ADDRESS>[  Lixiang Yan  Vanessa Echeverria  Dragan
Gašević  Roberto Martinez-Maldonado

###### Abstract

Integrating generative AI (GAI) into higher education is crucial for preparing
a future generation of GAI-literate students. Yet a thorough understanding of
the global institutional adoption policy remains absent, with most of the
prior studies focused on the Global North and the promises and challenges of
GAI, lacking a theoretical lens. This study utilizes the Diffusion of
Innovations Theory to examine GAI adoption strategies in higher education
across 40 universities from six global regions. It explores the
characteristics of GAI innovation, including compatibility, trialability, and
observability, and analyses the communication channels and roles and
responsibilities outlined in university policies and guidelines. The findings
reveal a proactive approach by universities towards GAI integration,
emphasizing academic integrity, teaching and learning enhancement, and equity.
Despite a cautious yet optimistic stance, a comprehensive policy framework is
needed to evaluate the impacts of GAI integration and establish effective
communication strategies that foster broader stakeholder engagement. The study
highlights the importance of clear roles and responsibilities among faculty,
students, and administrators for successful GAI integration, supporting a
collaborative model for navigating the complexities of GAI in education. This
study contributes insights for policymakers in crafting detailed strategies
for its integration.

###### keywords:

Generative Artificial Intelligence Diffusion of Innovations Theory Higher
Education Adoption Policy Global Perspective

##  1 Introduction

The adoption of generative AI (GAI) in higher education has the potential to
transform various educational practices in learning, teaching, and assessment
(Yan et al., 2024b; Kasneci et al., 2023). Emerging research has identified
GAI’s diverse capabilities, including providing comprehensive feedback (Dai et
al., 2023), exceeding the performance on reflective writing of the average
student (Li et al., 2023), enhancing multimedia learning (Vartiainen and
Tedre, 2023), and pioneering the development of adaptive educational content
(Mazzoli et al., 2023). Despite these advancements, concerns regarding the
digital divide have surfaced, highlighting how unequal access to such
technologies might deepen educational disparities (Pontual Falcão et al.,
2020). Moreover, the potential reliance on GAI raises questions about its
influence on students’ critical thinking, creativity, and independence
(Darvishi et al., 2023; Yan et al., 2024a). In light of these challenges and
opportunities, the role of institutional policies becomes critical in
navigating the integration of GAI within higher education (Tsai and Gasevic,
2017; Tsai et al., 2018; Cheng and YIM, 2024).

Following the public release of ChatGPT, many universities initially adopted a
cautious, wait-and-see approach (Wang et al., 2023; Moorhouse et al., 2023;
Cheng and YIM, 2024). However, as GAI tools (e.g., ChatGPT and Midjourney)
have become increasingly accessible to students, the necessity for well-
defined guidelines and policies has become apparent. These policies are
crucial for guiding the integration of GAI into curriculum development,
assessment design, and upholding academic integrity (Xiao et al., 2023; Plata
et al., 2023). While recent research has begun to explore these policies in
relation to how institutions strategise the adoption of GAI (Moorhouse et al.,
2023; Wang et al., 2023; Plata et al., 2023; Sullivan et al., 2023), such
analyses often lack a theoretical grounding. This tendency can lead to a
narrow focus on the benefits and challenges of GAI, as depicted in
institutional policies, while neglecting other critical dimensions. Notably,
aspects like innovation characteristics, communication channels, and the
assignment of roles and responsibilities within the adoption process have been
underexplored attention in prior studies (McDonald et al., 2024; Sullivan et
al., 2023; Xiao et al., 2023). These key components are essential for a
nuanced understanding of how new technologies are adopted in higher education
(Tsai and Gasevic, 2017; Tsai et al., 2018). Additionally, prior studies have
predominantly focused on universities in the Global North (a term often used
to describe wealthier, industrialised countries), resulting in findings that
may not be representative of a broader, global perspective (Wang et al., 2023;
Moorhouse et al., 2023).

This study aims to bridge the gaps identified above by analysing the adoption
policies of GAI within higher education across 40 universities from six global
regions, grounded in the Diffusion of Innovations Theory (DIT) (see Section
2.2 for details) (Rogers et al., 2014). DIT provides a robust framework for
understanding how new technologies, such as GAI, are adopted and integrated
within complex systems like higher education institutions. It offers insights
into the factors influencing the adoption process, enabling a systematic
analysis of how innovations spread across different contexts and cultures.
Specifically, this study investigates the characteristics of GAI innovation,
including compatibility, trialability, and observability. The analysis also
examines the use of communication channels and delineates the roles and
responsibilities outlined in university policies and guidelines. This approach
not only enhances our theoretical understanding of GAI adoption but also
provides a comprehensive view of the dynamics influencing GAI integration in
diverse educational settings globally.

##  2 Background and Related Work

###  2.1 Generative AI Policies in Higher Education

Recent studies have explored university policies, guidelines, and media
coverage to understand the adoption and response to GAI. Top universities in
the U.S. and globally have been analysed for their approach to integrating
generative AI in education, showing a cautious yet open strategy with concerns
about ethics, accuracy, and privacy (McDonald et al., 2024; Moorhouse et al.,
2023; Wang et al., 2023). Plata et al. (2023) examined academic integrity
articles and policies at leading global universities, suggesting a model for
maintaining academic integrity in the GAI era. Sullivan et al. (2023)
investigated the impact of ChatGPT on higher education in Australia, New
Zealand, the U.S., and the U.K. through news articles. A case study by Cheng
and YIM (2024) documented policy adaptations and management strategies at
eight Hong Kong public universities using local newspapers. Xiao et al. (2023)
conducted a quantitative analysis of the top 500 universities worldwide,
focusing on their ChatGPT policies, revealing differences and factors
influencing these policies. However, these studies mainly focus on the Global
North or specific regions, lacking a comprehensive global perspective.
Additionally, most research was completed by mid-2023, and with the rapid
evolution of GAI and policies (Cheng and YIM, 2024), continuous monitoring and
re-evaluation are essential. Notably, there is a gap in theoretical grounding
in these investigations.

###  2.2 Diffusion of Innovations Theory

The Diffusion of Innovations Theory (DIT) offers a framework to understand how
new ideas, practices, or objects, known as innovations, spread across
different cultures and social systems (Rogers et al., 2014). Essential in
sociology, communication, and public health, DIT outlines the mechanisms and
factors influencing the diffusion rate and breadth of innovations (Wejnert,
2002). It examines the complex interaction among four key elements:
innovation, communication channels, the social system, and time. This theory
is particularly useful for analysing the adoption and integration of GAI
technologies in higher education, providing insights into the factors that
affect their spread across educational institutions worldwide (Frei-Landau et
al., 2022; Pinho et al., 2021). For instance, DIT can elucidate the process
through which GAI-enhanced educational technologies become accepted,
highlighting the importance of compatibility with educational objectives and
effective dissemination of benefits across the institution. This understanding
can inform the creation of policy guidelines designed to maximise the benefits
of this new technology while minimising its potential risks. The current study
focuses on innovation, communication channels, and the social system, due to
the challenges in capturing temporal dynamics, especially the difficulty in
accessing archived policy documents, as new policies often replace older ones,
hindering temporal analysis.

####  2.2.1 Innovation Characteristics.

Innovation is a core concept in DIT, encompassing novel ideological,
practical, and technological advancements (Rogers et al., 2014). GAI
represents a significant technological innovation that could also drive
ideological and practical changes in higher education (Chiu, 2023; Swiecki et
al., 2022; Yan et al., 2024b). DIT suggests that an innovation’s adoption
likelihood is influenced by five characteristics: relative advantage,
compatibility, complexity, trialability, and observability (Rogers et al.,
2014). Relative advantage looks at the potential improvements GAI offers in
education. Compatibility assesses how well GAI fits within the existing
educational framework, with institutions more likely to adopt GAI if it aligns
with their goals and curricula. Complexity relates to the ease of adoption and
the need for specific enhancements. Trialability is the extent to which
institutions can experiment with GAI before committing to its full
implementation, reducing risks. Observability focuses on the visibility of the
innovation’s benefits, where clear success metrics and shared results can
encourage wider adoption by demonstrating GAI’s value. Previous studies have
already offered thorough discussions on the concepts of relative advantage and
complexity (Plata et al., 2023; Wang et al., 2023), with UNESCO’s recent in-
depth review in its guidance for GAI in education and research (Miao and
Holmes, 2023) also contributing significantly to this area. However, the
aspects of compatibility, trialability, and observability in the context of
GAI policies within higher education remain under-researched. Understanding
these three aspects of GAI’s innovation characteristics could provide valuable
insights into how higher education institutions are approaching its
integration and the strategies they are developing or implementing for
experimenting with and assessing GAI technologies’ potential impacts on their
educational systems. This identified gap has led to the formulation of the
first research question:

  * •

RQ1: How are GAI’s innovation characteristics, specifically compatibility,
trialability, and observability, represented in higher education institutions’
policies and guidelines?

####  2.2.2 Communication Channels.

Communication Channels play a crucial role in the diffusion of innovations, as
highlighted by DIT (Rogers et al., 2014). These channels, ranging from mass
media to interpersonal networks, significantly influence perceptions and
attitudes towards new innovations (Moolenaar et al., 2014). In higher
education, the significance of communication channels becomes even more
pronounced, requiring a two-way flow of information. This approach facilitates
the active participation of all key stakeholders, including faculty, students,
and administrators, in the decision-making process, thus increasing the
chances of successful innovation adoption (Gasevic et al., 2019; Tsai and
Gasevic, 2017; Tsai et al., 2018). The integration of GAI into higher
education highlights the need for clear communication channels, particularly
in light of concerns about academic integrity, assessment reform, and data
privacy (Rudolph et al., 2023; Winograd, 2023). However, the presence and
effectiveness of these channels within the policy frameworks of educational
institutions are not well-documented. This gap led to the second research
question:

  * •

RQ2: What communication channels are identified in higher education policies
for disseminating updates and facilitating discussions on GAI adoption among
stakeholders?

####  2.2.3 Social System.

The Social System refers to the norms, structures, and the community’s
readiness to adopt new innovations within a given diffusion context (Rogers et
al., 2014). In higher education, this system is characterised by the
institutional culture, policy environment, the network of educators and
researchers, and the broader educational framework (Pinho et al., 2021). The
policies set by educational institutions play a crucial role in demonstrating
the system’s readiness to adopt new technologies. They do this by defining the
roles and responsibilities of key stakeholders in the process of adopting GAI
(Kamal et al., 2011; Okai-Ugbaje et al., 2020). Despite its importance, there
has been limited research on how higher education policies address the
adoption of GAI from the perspective of the social system. This research gap
is the focus of the last research question:

  * •

RQ3: What roles and responsibilities are specified for faculty, students, and
administrators in higher education policies regarding the adoption of GAI?

##  3 Methods

###  3.1 Data Collection

To provide a comprehensive global perspective and avoid a focus solely on the
Global North, this study utilised the QS World University Rankings
2024111https://www.topuniversities.com/world-university-rankings, which
categorises universities into six regions: Africa, Asia, Europe, Latin
America, North America, and Oceania. Using stratified sampling (Neyman, 1992),
the top 10 universities from each region were selected, resulting in a total
of 60 universities. Publicly available information was manually collected from
the official websites of these universities using initial search terms such as
"Generative AI or ChatGPT" and "policy or guideline or guidance or guide."
Additional terms identified during the search, such as "artificial
intelligence or AI tools" and "learning or teaching or assessment," were
included in subsequent searches to improve data collection comprehensiveness
and rigour.

To overcome language barriers, searches were conducted in English and the
official languages of the universities. Bilingual researchers performed
searches in Spanish and Chinese for universities in Latin America and China
(Mainland), respectively. In other regions, searches were conducted in English
and the universities’ primary languages, with translation assistance. All
documents found were translated into English for analysis. The selection
criteria for the search results were: 1) The document must be an official
statement, policy, guideline, or guidance, excluding blogs, commentaries, news
items, training courses, and memos. 2) The document must be issued at the
university level, ensuring institution-wide applicability. 3) The document
must primarily support teaching, learning, and assessment activities,
excluding those intended for other purposes like academic research and
information security.

The search concluded on 20th January 2024. After applying the selection
criteria, 41 out of the 60 universities (68%) had published relevant documents
on their websites. The National University of Singapore was excluded due to
website accessibility issues. Among the remaining 40 universities, 10 were in
Oceania, nine in North America, eight in Europe, six in Africa, four in Asia,
and three in Latin America. The dataset, including the list of universities,
and archived web pages and documents, is available at the provided link.

###  3.2 Analysis

To address the research questions (RQ1–3), we conducted a thematic analysis to
identify relevant themes from the documents (Braun and Clarke, 2019). This
method allowed for an in-depth examination of the innovation characteristics,
communication channels, and social systems related to GAI adoption policies at
universities. Our goal was to offer insights that could assist policymakers in
developing comprehensive GAI adoption strategies. In accordance with best
practices for conducting thematic analysis (Braun and Clarke, 2021), our
initial step was to allow themes to naturally emerge from our dataset. To
achieve this, two researchers independently reviewed the same set of 40 policy
documents, identifying emerging themes pertinent to each RQ. Subsequently,
these researchers convened to discuss their findings, merging similar themes
to create a comprehensive codebook for each RQ (refer to Table LABEL:table-
rq1, LABEL:table-rq2, and LABEL:table-rq3 for further details). Utilising this
codebook, the researchers proceeded to independently code the ten policy
documents. Inter-rater reliability was measured using Cohen’s Kappa for each
identified theme. Themes with a Cohen’s Kappa score below 0.61, indicative of
less than substantial agreement (McHugh, 2012), were subjected to further
discussion. This entailed refining the descriptions of such themes and re-
coding them by both researchers until a substantial agreement (Cohen’s Kappa >
0.61) was achieved. The inter-rater reliability scores for each theme have
been documented in the tables.

For RQ1–Innovation Characteristic, the analysis focused on compatibility,
trialability, and observability as defined in DIT (Rogers et al., 2014). We
identified themes related to compatibility, reflecting how universities
perceive the integration of GAI as aligning with their goals and principles.
For trialability, themes were identified that highlight the approaches
universities intend to use for experimenting with and incrementally
implementing GAI tools in education. Regarding observability, we looked for
themes describing the methods and procedures established for evaluating the
impact and effectiveness of GAI integration within the university environment.
For RQ2–Communication Channels, themes were extracted about the various
communication channels mentioned in the policy documents for disseminating
updates and fostering dialogue on GAI adoption among stakeholders. Concerning
RQ3–Role and Responsibility, themes were separately identified for faculty,
students, and administrators based on their defined roles and
responsibilities.

Table 1: Innovation Characteristics Characteristics |  Themes |  Description |  Example |  N |  Kappa
---|---|---|---|---|---
Compatibility |  Academic Integrity and Ethical Use of AI |  Universities highlight the importance of using Generative AI tools in a manner that maintains academic integrity and aligns with the principles of originality and honesty. |  "Content produced by AI does not represent original content generated by a student and will be considered a form of academic misconduct if used indiscriminately or inappropriately." [University of Cambridge] |  40 |  1.00
|  Enhancing Teaching and Learning |  Universities see GAI and ChatGPT as tools to increase the effectiveness of teaching methodologies, enhance learning experiences, improve educational outcomes, and foster continuous learning. |  "Instead of seeing this as a threat, let it serve as a wake-up call for higher education leaders and faculty to take a fresh look at pedagogical approaches and assessment tools." [Nanyang Technological University] |  38 |  0.79
|  Fostering AI Literacy and Skills for the Future |  Universities emphasize the development of AI literacy and the ability to use AI tools responsibly in professional settings, aiming to prepare students for a future where AI plays a significant role. |  "At Oxford we want to support students and teaching staff to use AI ethically and appropriately in their work, and to regard AI literacy as a key skill… Universities will support students and staff to become AI-literate."[University of Oxford] |  33 |  0.68
|  Information Security and Data Privacy |  This theme focuses on information security, data privacy, compliance with existing policies, and the safe and secure use of AI technologies. |  "At present, any use of ChatGPT should be with the assumption that no personal, confidential, proprietary, or otherwise sensitive information may be used with it." [University of California, Berkeley] |  25 |  0.83
|  Support for Diverse Educational Needs and Equity |  This theme encompasses personalized learning, support for students with disabilities, and ensuring equitable access to educational tools. |  "Another affordance concerns the capacity of AI to absorb indigenous languages datasets. At present, the availability of AI in African languages is scarce at present, … The more languages are shared on AI platforms, the more sophisticated the capacity becomes, in any language, and so what is anticipated is a fair(er) balance between participation in AI generation between the Global North and the Global South." [North-West University] |  10 |  0.82
Trialability |  Integrating AI into Educational Practice |  This theme emphasizes the importance of making AI a part of the educational curriculum, encouraging its use for enhancing content creation, facilitating interactive learning, and adapting teaching and assessment strategies to leverage AI for educational benefits. |  "In this regard, it is suggested to indicate the value that AI can add to certain tasks…Cleaning and querying for problems in programming codes. AI can provide quick responses if we ask about errors that may occur when programming a function…Brainstorming on topics for research projects or others related to the course."(Translated) [Pontificia Universidad Católica de Chile] |  40 |  1.00
|  Critical Evaluation and Human-Centric Perspectives and Competencies |  Universities encourages a critical stance towards AI-generated content and prioritize human-centric perspectives and skills, including the verification of information for accuracy and encouraging students to critically evaluate AI tool outputs. |  "Then the students’ assignment is to engage with ChatGPT’s response, such as by fact-checking it against multiple academic sources, critiquing its responses, evaluating its responses from their vantage point in the Global South or in a particular context, or identifying possible biases, with the ChatGPT response included as part of students’submissions." [the University of Witwatersrand] |  39 |  0.66
|  Transparency and Privacy |  This theme emphasizes the need for transparency and privacy protection in using AI tools in education, covering the disclosure of AI involvement in content creation and assessment, guidelines for citing AI-generated content, respecting privacy, avoiding personal or confidential data use, and discussing ethical implications with students. |  "Report and cite any use you make of ChatGPT or any other artificial intelligence system to perform academic work. In no case do you present, in whole or in part, the work of someone else or an artificial intelligence tool as if it were your own." [Tecnológico de Monterrey]; "…staff must not submit student work into such tools as this may compromise students’ intellectual property and personal data rights." [University College London] |  38 |  0.64
|  Policy Communication |  This theme addresses the need for clear policy communication regarding AI use, ensuring that faculty and students are aware of guidelines and expectations. |  "Whether you’re allowing the use of AI tools for certain assignments or banning them altogether, you and your students will benefit from a clear statement of what role AI should play in the class." [University of Chicago] |  36 |  0.88
|  Assessment Design and Authentic Assessments |  This theme emphasizes designing assessments that evaluate higher-order thinking skills and are resistant to being solved solely by AI, thereby ensuring academic rigor. |  "…it may now be necessary to target forms of knowledge and expression that are more difficult for generative AI technologies - critical thinking, evaluation or creativity" and "Rather than only assessing the final output or ’product’ of students’ work, it is possible to also assess the process through which they produce it." [Monash University] |  35 |  0.72
Observability |  Continuous Evaluation |  Universities are planning to test and evaluate the effectiveness of AI tools through new assessment methods and periodic evaluations involving various stakeholders. |  "HKU will conduct periodic evaluations to assess whether its AI tools are achieving their intended outcomes in terms of enhancing T&L (teaching and learning) … The results of these evaluations will be made publicly available to ensure transparency." [University of Hong Kong] |  5 |  0.80
|  Collaboration and Discussion |  Universities are focusing on fostering collaboration and dialogue among educators, students, and other stakeholders to address the challenges and opportunities presented by AI. |  "This challenge will be addressed through dialogue among educators in every field, transcending academic boundaries … We plan to provide forums for university-wide discussions on this issue, and we hope to exchange ideas and situations with people from every academic discipline." [University of Tokyo] |  4 |  0.68
|  Ongoing Monitoring |  Universities are committing to continuously monitor AI technologies, adapting their use cases, policies, and guidance as their understanding and the technologies themselves evolve. |  "This page will remain a work-in-progress and will be updated as use cases and engagement with ChatGPT technology continues to evolve." [University of California, Berkeley] |  3 |  0.72
Table 2: Communication Channels Communication Channels |  Description |  N |  Kappa
---|---|---|---
Digital Platforms |  This category includes official university websites, dedicated pages for AI guidance for faculty and students, public webpages, and blogs. |  15 |  0.80
Interactive Learning and Engagement Channels |  This category includes live, interactive sessions such as webinars and workshops, as well as forums and discussion panels coordinated by university offices or departments. |  9 |  0.81
Direct and Personalized Communication Channels |  This category covers direct communication channels, email communications, and video conferencing. It includes email updates, direct contact with privacy offices or IT services, memos to teaching faculty, and facilitating discussions via video conferencing platforms. |  7 |  0.78
Collaborative and Social Networks |  This category refers to the development of new social and professional networks, chat groups and dedicated channels within collaboration platforms aimed at facilitating conversations about AI among faculty, students, and other stakeholders. |  3 |  1.00
Advisory, Monitoring, and Feedback Channels |  This category consists of committees or teams formed to guide decisions on the incorporation of AI tools into teaching and learning environments such as advisory and working groups with monitoring and feedback mechanisms. |  3 |  0.84
Table 3: Roles and Responsibilities Roles |  Responsibility |  Description |  N |  Kappa
---|---|---|---|---
Faculty |  Integrating GAI into Curriculum and Assessment |  This theme involves faculties incorporating Generative Artificial Intelligence (GAI) technologies into the curriculum and assessment methods. It reflects efforts to adapt teaching and learning methodologies to include GAI tools, thereby enhancing educational frameworks. |  20 |  0.90
|  Communication and Education on GAI Use |  This theme emphasizes the importance of faculties in communicating clearly about GAI applications and educating students on the careful and responsible use of GAI and detection tools. |  17 |  0.84
|  Setting Guidelines and Policies |  Faculties are tasked with establishing ethical standards, operational protocols, and clear guidelines for GAI usage within their courses. This involves creating a framework within which GAI tools can be used responsibly and effectively. |  9 |  0.78
|  Enhancing Critical Thinking |  Faculties should explore the use of GAI to improve pedagogical outcomes by fostering environments that cultivate intellectual growth and enhance students’ critical thinking abilities. |  4 |  0.88
|  Reviewing Ethical and Security Concerns |  Faculties are responsible for navigating potential risks associated with GAI integration, including concerns related to ethics, privacy, and security. |  3 |  0.64
Student |  Ethical Use and Academic Integrity |  Students are expected to use GAI tools ethically and responsibly, and maintain academic integrity. This theme includes appropriately acknowledging the use of GAI tools in their academic work, complying with university policies, and ensuring responsible use of data. |  27 |  0.94
|  Understanding the capabilities and limitations of GAI |  Students are encouraged to understand the strengths and weaknesses of GAI technology and develop a critical perspective towards it and content generated by these tools. |  6 |  0.75
|  Engagement and Communication |  This theme highlights the importance for students to actively participate in discussions about the ethical and practical implications of GAI in education. |  6 |  0.77
|  Learning Enhancement |  Students are expected to actively adopt GAI tools and leverage its power in supporting and enhancing the learning experience. |  4 |  0.91
Administrator |  Policy Development and Implementation |  This theme involves activities related to creating, updating, and implementing policies and guidelines to govern the use and ensure the effective integration of GAI tools within the university. It includes establishing codes of conduct, advisory committees, and best practices. |  16 |  0.95
|  Support and Resources |  This theme focuses on offering support and resources to faculty and students to guide the proper use of GAI tools and foster AI literacy. It includes providing resources, technical support, and fostering dialogue around GAI. |  16 |  0.80
|  Academic Integrity and Ethical Use |  This theme encompasses measures to ensure academic integrity and the ethical use of GAI tools. It includes detecting improper use of GAI tools and managing breaches of academic integrity. |  7 |  0.78
|  Supervision in Procurement Process of GAI Services |  This theme is specifically focused on the supervision of purchasing GAI tools to minimize institutional risk. It includes overseeing the acquisition of such tools and ensuring they align with university policies and ethical standards. |  3 |  0.84

##  4 Results

###  4.1 RQ1–Compatibility

In the adoption of GAI across 40 universities, as shown in Figure 1, five key
themes emerged to align the technology with their existing institutional goals
and objectives. The most common theme highlighted by all the universities is
academic integrity and ethical use of AI (n=40). These universities suggested
the potential incompatibility between GAI tools and the principles of
originality and honesty. For instance, the University of Cambridge (United
Kingdom) and the University of Sydney (Australia) warned against the
inappropriate use of AI-generated content, considering it a form of academic
misconduct. Similarly, Stanford University (United States) treated the use of
generative AI analogously to assistance from another person without a clear
statement from the instructor. Moreover, the University of Cape Town (South
Africa) highlighted the concerns around plagiarism and cheating from
inappropriate use of GAI tools in its guides for both teaching staff and
students.

The alignment between GAI and universities’ objectives to enhance teaching and
learning (n=38) was identified. Universities see GAI as a tool to increase the
effectiveness of teaching methodologies, enhance learning experiences, improve
educational outcomes, and foster continuous learning. Specifically, Nanyang
Technological University (Singapore) viewed GAI not as a threat but as an
opportunity to re-evaluate pedagogical approaches and assessment tools, aiming
to adapt its educational practices to ongoing advancements in GAI. Likewise,
the Chinese University of Hong Kong (China Hong Kong SAR) encouraged teachers
and students to embrace and utilise appropriate AI tools to enhance their
teaching and learning experiences. The University of Johannesburg (South
Africa) also emphasised the role of GAI as a great learning assistant to
provide personalised support across various contexts, including practical
sessions, tutorials, and collaborative learning.

The adoption of GAI is also being viewed as compatible with the objective of
universities to cultivate essential skills that are vital for preparing
students for the future workforce. Specifically, universities aimed to prepare
students for a future where AI plays a significant role, with an focus on
fostering AI literacy and skills for responsible use in professional settings
(n=33). For example, the University of Oxford (United Kingdom), emphasised the
goal on developing AI literacy as an essential skill for students,
highlighting its commitment to preparing them for a future where AI is
ubiquitous. The University of Hong Kong (China Hong Kong SAR) has recognised
the importance of GAI literacy alongside traditional academic literacies and
committed an action plan for developing GAI literacy among both students and
instructors. A self-paced online module will be made available to students,
with future plans to make it a credit-bearing course. For instructors, the
university encourages the exploration of GAI in pedagogy, provides targeted
online training, and offers personalised support through its GAI Task Force
and Teaching and Learning Innovation Center. Likewise, the Universidad
Nacional Autónoma de México (Mexico) stressed educating students about AI to
foster a critical attitude towards its use and encourage critical evaluation
of these tools’ outputs, further demonstrating the universities’ commitment to
fostering AI literacy as an essential skill for students.

A potential incompatibility was noted between GAI and some universities’
requirements on information security and data privacy (n=25). For instance,
the University of California, Berkeley (United States), emphasised the
importance of assuming that no personal, confidential, proprietary, or
otherwise sensitive information may be used with ChatGPT. Likewise, the
University of Cape Town highlighted the ethical and legal risks associated
with sharing student data by uploading it into detectors managed by third-
party companies with unknown privacy and data usage policies. The Universidad
Nacional Autónoma de México also indicated the challenges and responsibilities
universities face in ensuring data privacy and security when using AI-based
applications. For instance, the university is responsible for providing users
with detailed information about data usage, and ensuring that data collection
is necessary and conforms to reasonable expectation.

Lastly, universities noted the double-edged sword nature of GAI tools in
achieving the goal of supporting diverse educational needs and promoting
equity (n=10) and were taking active measures to mitigate this potential
incompatibility. In one example, Cornell University (United States) encouraged
using AI along with inclusive instructional approaches like Universal Design
for Learning (UDL) to enhance course accessibility. When integrating a GAI
tool in teaching, the University of California, Berkeley, advocated for
alternative assignment options if the specified GAI tool is inaccessible to
students, particularly for those with disabilities. Similarly, North-West
University (South Africa) highlighted the importance of language and cultural
inclusion by leveraging AI to support indigenous languages. Financial
considerations were prioritised by the California Institute of Technology
(United States), the University of Manchester (United Kingdom), and the
University of Hong Kong, focusing on providing equitable access to GAI tools
without additional costs to students, ensuring that no student is
disadvantaged due to financial constraints.

Figure 1: Key themes related the compatibility, trialability, observability of
generative AI integration which emerged from the analysed universities’
policies and guidelines.

###  4.2 RQ1–Trialability

Regarding universities’ strategic exploration into the experimental and phased
integration of GAI tools within educational frameworks, we identified five key
themes (Figure 1). The most prevalent theme was to encourage the integration
of AI into educational practices (n=40), including content creation,
interactive learning, and teaching and assessment strategies. For example,
Pontificia Universidad Católica de Chile (Chile) encouraged students’ use of
GAI for quick responses in programming code issues and brainstorming research
topics, showcasing the practical applications of GAI in enhancing course-
specific skills. From a broader pedagogical perspective, the University of
Cape Town provided nine use cases for both staff and students to explore the
potential benefits for deployment in teaching and learning, such as content
creation, learning analytics, personalised learning support, and automated
grading systems. In contrast, the University of Queensland (Australia) imposed
stricter rules, requiring educators to choose a specific level of GAI use
permitted in their courses and to clearly declare this in the course profile.

Universities’ integration strategy and guidelines also focused on critical
evaluation and human-centric perspectives and competencies (n=39). For
example, Cornell University offered guidance on leveraging GAI platforms to
help students become more discerning and engage with GAI as a tutor to enhance
critical thinking. Likewise, the University of Witwatersrand (South Africa)
proposed a new form of assignment where students actively engage with AI-
generated responses by verifying them against academic sources, and assessing
them from their unique perspectives in the Global South. North-West University
highlighted its mission to raise awareness of the potential limitation of GAI,
noting that human agents remain indispensable for fostering development and
growth in education.

Phased implementation and experimentation of GAI also co-occurred with
regulatory exploration into transparency and privacy (n=38), ensuring the
openness of GAI tools in educational contexts. For instance, Tecnológico de
Monterrey (Mexico) advised students to verify the correct use of AI technology
with their professors and to cite any use of GAI systems in academic work.
Similarly, ETH Zurich (Switzerland) suggested citing the prompt used for
generating the content to maintain academic integrity which would aid the
establishment of norms for the citation of AI-generated content. On the other
hand, University College London (United Kingdom) emphasised the importance of
not uploading personal data to AI systems without considering data protection
requirements, highlighting the need for approvals and consents overseen by a
staff supervisor. Likewise, the University of Edinburgh (United Kingdom)
advised treating information given to AI tools as if it were posted on a
public site. This advice aimed to bring caution against sharing personal,
confidential, copyrighted, or restricted information, preventing privacy
issues and the potential for misuse of AI tools.

The importance of clear policy communication regarding GAI (n=36) during
phased implementation and GAI experimentation was also identified. The
University of Auckland (New Zealand) considers communication with students to
be "critical" for managing their expectations. Therefore, instructors need to
be explicit about the permitted tools to prevent students from unintentionally
engaging in academic misconduct. Similarly, the University of Chicago (United
States) advocated for a clear statement in class syllabi about the role of
GAI, offering guidance for syllabus statements on GAI tool usage to benefit
both instructors and students. The University of Sydney published guidelines
on the use of GAI in education, research, and operations, while recommended
open discussions with students about GAI. This approach was echoed by the
University of Edinburgh’s plans to establish a site for sharing information on
GAI use and a group to update and share best practices.

Lastly, universities also encouraged instructors to conduct trials and
experiments with novel assessment designs and authentic assessments (n=35)
that target high-order cognitive abilities and can remain impervious to GAI
solutions. For instance, the University of Adelaide (Australia) emphasised the
importance of designing assessments that require genuine student effort and
creativity, rather than tasks that can be easily completed by GAI. Similarly,
Monash University (Australia) and the University of Pretoria (South Africa)
advised instructors on modifying questions to be more applicable to individual
student contexts and incorporating real-life problem-solving into assessments,
examining students’ applied knowledge and their critical thinking and
analytical skills. Monash University also emphasised the necessity of
assessing the process rather than merely the final output of students’ work.

###  4.3 RQ1–Observability

The results reveal that seven universities were adopting a proactive approach
towards the integration and monitoring of GAI technologies within their
ecosystems, focusing on three main themes: continuous evaluation,
collaboration and discussion, and ongoing engagement and updates (Figure 1).
Firstly, continuous evaluation (n=5) is a part of universities’ strategies to
monitor the outcomes and effectiveness of GAI integration. The University of
New South Wales (Australia) and the University of Hong Kong, for example, were
actively testing new assessment methods and conducting periodic evaluations
with multiple stakeholders to assess the impact of AI tools on teaching and
learning. These evaluations aimed to ensure transparency and adaptability in
the use of AI within educational settings.

Secondly, collaboration and discussion (n=4) emerged as an evaluation approach
across institutions such as the University of Tokyo (Japan) and Cornell
University. The University of Tokyo highlighted the necessity of dialogue
among instructors across various fields to address the challenges presented by
AI, planning to provide forums for university-wide discussions. Similarly,
Cornell University encouraged collaborative review of course assessment plans
with colleagues and the Center for Teaching Innovation, emphasising the
importance of faculty consultations on teaching and learning in the context of
AI. Lastly, some universities (n=3) emphasised the importance of ongoing
monitoring with AI technologies, promising continuous adaptation to their
policies and guidance as their understanding of these technologies evolves.
For instance, the University of California, Berkeley committed to regularly
adapting the work-in-progress guidance as new use cases emerge and further
engagement with the GAI technology develops.

These themes reflect a comprehensive strategy for integrating GAI in
university ecosystems, balancing innovation with critical assessment and
collaboration to navigate the challenges and opportunities presented by these
technologies.

###  4.4 RQ2–Communication Channels

In addressing RQ2, our analysis identified a diverse range of communication
channels within higher education policies (22/40 universities) aimed at
conveying updates and stimulating conversations about GAI adoption among
stakeholders. As shown in Figure 2, these channels are broadly categorised
into five main groups. Firstly, digital platforms (n=15) emerged as the most
prevalent channel, comprising official university websites, AI guidance pages,
and blogs. For example, the University of Cape Town utilised its official
website for policy and guidance document updates, serving as a central
repository for accessible information dissemination. Similarly, the University
of Edinburgh planned to host an information-sharing site, providing a platform
for the latest research and best practices in GAI.

Secondly, interactive learning and engagement channels (n=9), including
webinars, workshops, forums, and discussion panels, were designed to foster
live, interactive sessions. The University of Witwatersrand, for instance,
coordinated webinars and workshops to engage stakeholders in discussions on AI
in teaching and learning, while the University of Sydney hosted staff forums
and student panels to explore the impact of GAI on education. Thirdly, direct
and personalised communication channels (n=7), such as email communications,
direct contacts, and video conferencing, offered stakeholders a direct line
for inquiries. The University of California, Berkeley, provides direct contact
for privacy concerns and hosts workshops, whereas Princeton University (United
States) facilitated Zoom discussions for teaching faculty, allowing for
personalised advice and engagement on AI tools.

Additionally, collaborative and social networks (n=3), including social and
professional networks, collaboration platforms, and chat groups, supported
real-time communication. The University of New South Wales leveraged Microsoft
Teams groups for faculty members to share information about respective news,
academic literature, workshops, and events. Lastly, advisory, monitoring, and
feedback channels (n=3), through advisory committees and feedback processes,
played a crucial role in guiding AI tool incorporation decisions. The
University of Hong Kong, for example, planned to establish an advisory
committee to decide which GAI tools should be incorporated into their teaching
and learning environment, ensuring ethical use and collecting community
feedback to inform policy and practice.

These findings highlight the multifaceted approach higher education
institutions are adopting to communicate about GAI, emphasizing the importance
of diverse and interactive channels in fostering an informed and engaged
academic community.

Figure 2: Five primary communication channels utilised by higher education
institutions to communication information about the adoption policy of
generative AI.

###  4.5 RQ3–Role and Responsibility

####  4.5.1 Faculties

As shown in Figure 3, we identified five distinct roles and responsibilities
assigned to faculty across 36 institutions. Faculties were advised to
integrate GAI into curriculum and assessment (n=20), reflecting efforts to
incorporate GAI technologies within educational frameworks, thereby
transforming teaching and learning methodologies. Specifically, the University
of Melbourne encouraged faculties leveraging GAI tools to improve various
aspects of assessment design, which indicates a strategic incorporation of GAI
into curriculum and evaluation. Another major faculty role involved
communicating and educating students on GAI use (n=17), highlighting the
importance of clear communication about GAI applications and the careful use
of GAI detection tools. The University of Cape Town, for example, accentuated
the responsibility for instructors to clarify the appropriate use of GAI tools
at the beginning of the courses. Furthermore, setting guidelines and policies
was identified as a critical responsibility (n=9) by some universities, such
as the University of Pennsylvania (United States) and ETH Zurich, with
faculties being advised to focus on establishing ethical standards and
operational protocols that aligns with the Principles of Responsible Conduct
for GAI usage for their courses. The role of enhancing students’ critical
thinking (n=4) emphasised the strategic use of GAI to improve pedagogical
outcomes and foster environments that cultivate intellectual growth. For
example, The University of Pretoria provided recommendations for faculty to
utilise ChatGPT to enhance students’ critical thinking abilities, suggesting
the use of GAI as a tool to foster deeper intellectual engagement and problem-
solving skills. Lastly, attention to reviewing ethical and security concerns
(n=3) highlighted the role of navigating potential risks, including
accessibility, data privacy, and security, associated with GAI integration.

####  4.5.2 Students

Students were primarily charged with the duty of ensuring ethical and
responsible use of GAI tools and maintaining academic integrity (n=27). The
documents from these universities frequently mentioned several best practices
for honesty and transparency within academic settings. These include
acknowledging the use of AI tools, complying with university policies and
course instructions, and exercising caution in data usage when deploying these
tools. Additionally, the responsibilities extended to understanding the
capabilities and limitations of GAI (n=6), which is crucial for fostering a
critical perspective towards technology among students. The University of
Edinburgh detailed this into three aspects, in which students are expected to
understand the limitations of AI systems, check the factual accuracy of AI-
generated content, and avoid over-reliance on AI tools as a single key source.
The roles of learning enhancement (n=6) and engagement and communication (n=4)
reflected a proactive stance towards the adoption of GAI in educational
contexts, encouraging students to leverage GAI to support their learning
experience and participate in meaningful discussions regarding the ethical and
practical implications of GAI in education. Specifically, the Technical
University of Munich (Germany) elaborated on the scenarios where students can
use ChatGPT to support their learning processes or to use the chatbot as a
peer, proofreader, or brainstorming partner for learning enhancement. In
addition, the University of Sydney has actively engaged students in
collaborating with staff to develop a dedicated Canvas site that serves as a
hub for AI-related educational resources.

####  4.5.3 Administrators

Administrators were primarily tasked with policy development and
implementation (n=16), a responsibility that encompasses a range of
activities, including the establishment of conduct codes, advisory committees,
and best practices. These activities aimed to facilitate the creation,
updating, and implementation of policies, as well as the effective integration
of GAI tools within university processes. A significant focus was also placed
on providing guidance and support to both faculty and students (n=16), aiming
to foster AI literacy and guide the proper use of GAI tools. For instance, the
Centre for Learning and Teaching at the American University in Cairo (Egypt)
has been instrumental in integrating AI tools. It serves as a central
repository and contact point for faculty and students. Moreover,
administrators play a crucial role in ensuring academic integrity and ethical
use of GAI (n=7), through measures such as detecting improper use of GAI tools
and managing breaches of integrity. Notably, the motivation to detect misuse
of these tools is distinct from deploying AI detection tools themselves. For
instance, Imperial College London has committed to staff training to enhance
the understanding and identification of AI usage, prompted by concerns about
the maturity and accuracy of AI detection tools. Lastly, administrators were
also tasked with the responsibility of supervising GAI tool procurement to
minimise institutional risk (n=3). For instance, at Yale University, efforts
to support procurement practices that align shared interests and minimise
institutional risks exemplify the supervision of GAI tool procurement. This
ensures that the adoption of these technologies adheres to the university’s
ethical standards and risk management policies.

Figure 3: Roles and Responsibilities of faculties, students, and
administrators in the adoption process of generative AI.

##  5 Discussion

In our study, we examined GAI adoption policies in higher education, analysing
data from 40 universities across six global regions through the Diffusion of
Innovations Theory (DIT) framework. For research question one (RQ1), we
focused on the innovation characteristics of GAI, particularly its
compatibility, trialability, and observability within higher education
policies. Our findings highlighted a universal concern for academic integrity
and the ethical use of AI, underscoring institutions’ dedication to
maintaining core educational values amidst technological progress (Moorhouse
et al., 2023; Wang et al., 2023). The positive perception of GAI as a tool to
enhance teaching and learning effectiveness indicated a strategic shift
towards leveraging technology to meet educational objectives, highlighting its
potential to complement and augment traditional teaching methodologies (Xiao
et al., 2023; Cheng and YIM, 2024). Moreover, the compatibility between GAI
and the goal of fostering innovation and 21st-century skills stressed the need
to integrate these tools into educational practices, ensuring that
institutions remain relevant in an AI-influenced future (Yan et al., 2024a;
Darvishi et al., 2023). Although less common, a focus on promoting equity
revealed a growing effort among some institutions to mitigate inequalities and
ensure GAI benefits a broad student demographic, preventing further
educational disparities (Pontual Falcão et al., 2020).

Our analysis revealed a strategic focus on trialability, with higher education
institutions actively promoting the integration of GAI into educational
practices. This included emphasising critical evaluation, human-centric
competencies, and advocating for a phased implementation that explores
regulatory aspects of transparency and privacy. These findings highlighted the
proactive approach of universities in leveraging GAI’s potential while
addressing its ethical and practical challenges. Universities were actively
integrating GAI into their curricula and pedagogical strategies, recognising
its transformative potential to enhance educational outcomes, foster critical
thinking, and prepare students for future careers. The focus on critical
evaluation and human-centric competencies suggested a balanced view of GAI as
a complement to human intellect and creativity, rather than a replacement (Yan
et al., 2024a; Darvishi et al., 2023). This approach aligns with findings from
previous studies that analysed documents, such as policies and new articles,
from both quantitative and qualitative perspectives (Xiao et al., 2023; Cheng
and YIM, 2024).

The observability of GAI integration within higher education, as revealed by
our findings, underscores a nuanced landscape where a subset of universities
is taking significant strides toward embedding GAI technologies within their
educational ecosystems. The identified themes of ongoing engagement,
collaboration, and continuous evaluation reflect a proactive and iterative
approach to adopting GAI, highlighting the importance of adaptability,
transparency, and community involvement. However, the relatively small number
of universities (n=7) actively engaging in evaluation measures for GAI’s
impact signals a gap in the comprehensive policy development and
implementation across the higher education sector. This gap not only raises
questions about the readiness of the majority of institutions to adapt to and
fully leverage GAI technologies but also points to potential disparities in
the resources available for such endeavors, such as institutional
infrastructures and staff skills (King and Boyatt, 2015). The limited
engagement suggests a need for a more widespread and structured approach to
understanding and harnessing the benefits and challenges of GAI, as well as a
more robust framework for sharing best practices and learning across
institutions (Cheng and YIM, 2024; Kezar, 2011). Moreover, the emphasis on
continuous evaluation, collaboration, and ongoing monitoring by a minority of
universities (n=5, n=4, and n=3, respectively) raises critical questions about
the scalability and sustainability of such practices. While these intended
approaches are commendable, their impact and effectiveness may be limited if
they are not adopted more broadly across the higher education sector. This
situation highlights the need for a more inclusive dialogue on GAI
integration, one that encompasses a wider array of institutions, including
those that may lack the resources or infrastructure to engage in such
proactive measures (Soomro et al., 2020).

Regarding communication channels (RQ2), our analysis showed that approximately
a half of the universities employed a varied approach to foster dialogue and
share information among educational stakeholders. This strategy included
digital platforms, interactive learning environments, direct communication
methods, collaborative networks, and advisory mechanisms, highlighting the
complex and multifaceted process of integrating GAI into education. The use of
digital platforms for policy updates, alongside interactive and direct
communication channels, can be a balanced strategy for broad outreach and
individual engagement. This method is consistent with prior research
advocating for active stakeholder participation and diverse communication
channels to facilitate successful innovation adoption in higher education
(Tsai and Gasevic, 2017; Tsai et al., 2018). However, the fact that less than
half of the universities implemented these channels indicated a critical need
for more focused efforts in disseminating and communicating GAI adoption
policies, given the technology’s rapid evolution (Yan et al., 2024b; Kasneci
et al., 2023). Moreover, a limited emphasis on collaborative and social
networks (n=3) contrasts with literature highlighting their role in promoting
a community-oriented, innovative climate (Moolenaar et al., 2014), suggesting
an area for enhancement.

For the final research question (RQ3), our findings revealed that the majority
of universities (n=36) have established clear policies defining the roles and
responsibilities of faculty, students, and administrators in GAI adoption.
This structured approach facilitated the integration of GAI into educational
practices. Faculty were tasked with incorporating GAI into teaching and
ensuring its ethical use by students. Students were responsible for using GAI
ethically and participating in critical discussions about its implications.
Administrators were charged with crafting and implementing policies that
ensure GAI’s integration aligns with institutional values and academic
integrity.

While previous studies have examined the roles of faculty and students in GAI
adoption (Moorhouse et al., 2023; Wang et al., 2023), our research provided a
more detailed perspective by also considering administrators’ roles, offering
insights into the comprehensive stakeholder responsibilities in GAI’s context.
Our findings highlighted the importance of a collaborative effort among all
educational ecosystem stakeholders to effectively manage GAI integration’s
complexities. This approach suggested that successful GAI adoption in higher
education depends not just on the technology but also on the community’s
preparedness and involvement. This perspective supports existing literature on
the significance of clearly defined roles and responsibilities in adopting
educational technologies (Kamal et al., 2011; Okai-Ugbaje et al., 2020),
emphasising the need for a structured approach to fully leverage GAI’s
potential.

The current study has several limitations, including potential sample
selection bias towards well-resourced institutions due to reliance on QS World
University Rankings, language and cultural interpretation challenges despite
efforts to include bilingual researchers, and the dynamic nature of GAI
policies development, which may have evolved beyond the study’s cutoff date.
Similarly, choosing the top 10 universities from each region may not
accurately reflect the situation of all universities globally, particularly
those with limited resources. Future research could benefit from qualitative
investigations involving educational stakeholders at these less-resourced
institutions to better comprehend their strategies for adopting GAI. Such
investigations could be critical to understand the impacts of GAI adoption on
the digital divide and existing inequalities within the higher education
sector. Lastly, the focus on official policy documents excludes informal
communications and may not fully capture the practical implementation of GAI
or the perspectives of key stakeholders like students and faculty. Future
research could benefit from a broader sample, qualitative insights from direct
stakeholders, and ongoing updates to remain relevant.

##  6 Conclusion

This study identified the strategic and proactive measures higher education
institutions globally are adopting towards the integration of GAI, guided by a
commitment to academic integrity, teaching, learning enhancement, and equity.
Our analysis, framed by the DIT, revealed a universal emphasis on GAI’s
compatibility with educational values, its potential for fostering innovation
and critical thinking, and the importance of trialability and observability in
its adoption. Despite the optimism surrounding GAI’s capabilities, the
findings also highlighted significant gaps in comprehensive policy
development, communication strategies, and the equitable distribution of
resources for GAI integration. The identified need for a structured approach
to stakeholder engagement, especially in defining clear roles and
responsibilities, underscores the necessity of a collaborative and inclusive
model that navigates the complexities of GAI adoption in education, ensuring
its alignment with institutional values and the broader educational mission.

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