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Generative AI in Higher Education: A Global Perspective of Institutional Adoption Policies and Guidelines

Yueqiao Jin, Lixiang Yan, Vanessa Echeverria, Dragan Gašević, Roberto Martinez-Maldonado

TL;DR

This paper tackles the lack of a global, theory-based understanding of how higher education institutions adopt Generative AI through Diffusion of Innovations Theory. It analyzes 40 universities across six regions to map innovation characteristics, communication channels, and social system roles within official policies. The findings reveal a proactive yet uneven policy landscape prioritizing academic integrity, teaching enhancement, and AI literacy, with gaps in comprehensive frameworks and stakeholder engagement. The work provides a structured blueprint for policymakers to craft detailed, globally informed adoption strategies and clearer roles for faculty, students, and administrators.

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.

Generative AI in Higher Education: A Global Perspective of Institutional Adoption Policies and Guidelines

TL;DR

This paper tackles the lack of a global, theory-based understanding of how higher education institutions adopt Generative AI through Diffusion of Innovations Theory. It analyzes 40 universities across six regions to map innovation characteristics, communication channels, and social system roles within official policies. The findings reveal a proactive yet uneven policy landscape prioritizing academic integrity, teaching enhancement, and AI literacy, with gaps in comprehensive frameworks and stakeholder engagement. The work provides a structured blueprint for policymakers to craft detailed, globally informed adoption strategies and clearer roles for faculty, students, and administrators.

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.
Paper Structure (21 sections, 3 figures)

This paper contains 21 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Generative AI Policies in Higher Education
  4. Diffusion of Innovations Theory
  5. Innovation Characteristics.
  6. Communication Channels.
  7. Social System.
  8. Methods
  9. Data Collection
  10. Analysis
  11. Results
  12. RQ1--Compatibility
  13. RQ1--Trialability
  14. RQ1--Observability
  15. RQ2--Communication Channels
  16. ...and 6 more sections

Figures (3)

  • Figure 1: Key themes related the compatibility, trialability, observability of generative AI integration which emerged from the analysed universities' policies and guidelines.
  • Figure 2: Five primary communication channels utilised by higher education institutions to communication information about the adoption policy of generative AI.
  • Figure 3: Roles and Responsibilities of faculties, students, and administrators in the adoption process of generative AI.