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A University Framework for the Responsible use of Generative AI in Research

Shannon Smith, Melissa Tate, Keri Freeman, Anne Walsh, Brian Ballsun-Stanton, Mark Hooper, Murray Lane

TL;DR

This paper presents a four layer strategic framework to govern the responsible use of generative AI in research, grounded in experiences from two Australian universities. It defines context, creates a university position, outlines implementation, and establishes a review mechanism to keep policies current amid rapid AI change. Key contributions include detailed guidance on research integrity, ethics, data management, sector policies, and terms of service, plus practical steps for consultation, training, and infrastructure. The work aims to reduce risks such as data privacy breaches, IP uncertainties, and diminished trust, while enabling researchers to leverage AI responsibly and ethically across institutional governance structures. Its practical, adaptable model serves as a road map for large research institutions seeking to balance innovation with integrity in the era of generative AI.

Abstract

Generative Artificial Intelligence (generative AI) poses both opportunities and risks for the integrity of research. Universities must guide researchers in using generative AI responsibly, and in navigating a complex regulatory landscape subject to rapid change. By drawing on the experiences of two Australian universities, we propose a framework to help institutions promote and facilitate the responsible use of generative AI. We provide guidance to help distil the diverse regulatory environment into a principles-based position statement. Further, we explain how a position statement can then serve as a foundation for initiatives in training, communications, infrastructure, and process change. Despite the growing body of literature about AI's impact on academic integrity for undergraduate students, there has been comparatively little attention on the impacts of generative AI for research integrity, and the vital role of institutions in helping to address those challenges. This paper underscores the urgency for research institutions to take action in this area and suggests a practical and adaptable framework for so doing.

A University Framework for the Responsible use of Generative AI in Research

TL;DR

This paper presents a four layer strategic framework to govern the responsible use of generative AI in research, grounded in experiences from two Australian universities. It defines context, creates a university position, outlines implementation, and establishes a review mechanism to keep policies current amid rapid AI change. Key contributions include detailed guidance on research integrity, ethics, data management, sector policies, and terms of service, plus practical steps for consultation, training, and infrastructure. The work aims to reduce risks such as data privacy breaches, IP uncertainties, and diminished trust, while enabling researchers to leverage AI responsibly and ethically across institutional governance structures. Its practical, adaptable model serves as a road map for large research institutions seeking to balance innovation with integrity in the era of generative AI.

Abstract

Generative Artificial Intelligence (generative AI) poses both opportunities and risks for the integrity of research. Universities must guide researchers in using generative AI responsibly, and in navigating a complex regulatory landscape subject to rapid change. By drawing on the experiences of two Australian universities, we propose a framework to help institutions promote and facilitate the responsible use of generative AI. We provide guidance to help distil the diverse regulatory environment into a principles-based position statement. Further, we explain how a position statement can then serve as a foundation for initiatives in training, communications, infrastructure, and process change. Despite the growing body of literature about AI's impact on academic integrity for undergraduate students, there has been comparatively little attention on the impacts of generative AI for research integrity, and the vital role of institutions in helping to address those challenges. This paper underscores the urgency for research institutions to take action in this area and suggests a practical and adaptable framework for so doing.
Paper Structure (28 sections, 1 figure, 1 table)

This paper contains 28 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Scope
  3. Strategic Framework
  4. Context
  5. Research integrity
  6. Trust in Research
  7. Human research ethics
  8. Research Degree requirements
  9. Assessment
  10. How can an assessment demonstrate learning success?
  11. Research data management
  12. Inputs: Privacy, unauthorised IP access, and Permission
  13. Outputs: Copyright and IP sharing concerns
  14. Sector policies
  15. Publishers
  16. ...and 13 more sections

Figures (1)

  • Figure 1: A framework for institutions to support the responsible use of generative AI in research. We have made this available to use under a Creative Commons CC-BY 4.0 Licence, accessible via https://osf.io/9b5an/.