AI Institute
Practical AI Tools
Practical AI Tools for Research Writing and Academic Success
This practical short course provides an end-to-end guide to using AI tools across the research workflow, including literature review, paper reading, data analysis, visualization, academic writing, peer review, and ethical AI use.
SAIB
STAR AI Bulletin
Monthly Insights on Artificial Intelligence in Research,
Higher Education and Scholarly Publishing
Artificial Intelligence is rapidly transforming the academic landscape from teaching and learning to scientific research, scholarly publishing, peer review, research integrity, and institutional decision-making. Keeping pace with these developments requires more than following headlines; it requires understanding their impact on researchers, educators, universities, students, and scholarly publishers.
AI Guidelines
Research Integrity | Transparency | Public Good
STAR AI Guidelines for Responsible Scholarship
The STAR Scholars Network presents a principled and practical framework to guide the ethical, rigorous, and socially responsible use of artificial intelligence in research and scholarship. Grounded in stewardship of the public good, trust through ethical practice, accountability to society, and transparency of process, these guidelines support institutions and scholars in harnessing AI’s potential while preserving the foundations of academic integrity.
We invite universities, research centers, scholarly associations, and global partners to adopt, adapt, and implement this framework within their local contexts. Together, we can advance innovation without compromising rigor, equity, or public trust. Engage with us to shape a shared global standard for responsible AI-enabled scholarship.
The STAR Research Framework
S — Stewardship of the Public Good
Purpose • Social Responsibility • Human Flourishing • Civic Mission • Knowledge in Service to Society
T — Trust Through Ethical Practice
Integrity • Honesty • Professional Responsibility • Credibility • Moral Accountability
A — Accountability to Impact on Society
Responsibility • Consequence Awareness • Ethical Judgment • Public Answerability • Measurable Impact
R — Record of Integrity in Process and Evidence
Transparency • Traceability • Documentation • Methodological Rigor • Archival Responsibility
Executive Summary
This framework articulates a principled and practical response to the growing use of generative artificial intelligence (GenAI) in research and scholarship. Reflecting the vision and mission of the STAR Scholars Network, a global network of scholars who support each other and advocate for research driven by a social-impact mission, it addresses an urgent question facing universities worldwide: how to harness the potential benefits of AI tools while preserving the ethical, epistemic, and social foundations of research as a public mission.
GenAI can enhance many aspects of scholarly work, but institutions and scholars must also find ways to preserve the foundations of expertise, advance inquiry grounded in reality, sustain ethical practices, and take accountability to society when using this emerging technology. As probabilistic language technologies increasingly mediate research and publication workflows, they risk aggravating existing weaknesses in the global knowledge ecosystem, including shortcuts scholarship, erosion of trust, epistemic inequality, and misalignment between research productivity and social purpose.
This framework shows institutions and researchers how to update the terms of rigor, transparency, and responsibility when using GenAI. It is anchored in four Foundational Commitments that define research integrity in the age of AI: Stewardship of the Public Good; Trust Through Ethical Practice; Accountability to Impact on Society; and Record of Integrity in Process and Evidence.
The framework offers practical guidelines for scholars, mentors, and institutions. It delineates responsible and inappropriate uses of AI across research stages, emphasizes discipline-specific judgment, and stresses that AI may assist organization and exploration but must not replace interpretation, synthesis, or argumentation. Particular attention is given to training the next generation of scholars through modeling ethical boundaries, integrating critical AI literacy, and preserving writing-to-learn and inquiry-based education grounded in real-world data and social purpose. The STAR Scholars Network positions this framework as an adaptable resource for institutions seeking to preserve research integrity, advance social justice, and steward the global knowledge ecosystem in the age of AI.
Co-Creating, Co-arising
This framework is a living document, shaped through global dialogue and collective reflection. In the spirit of co-creating and co-arising knowledge, we continue to refine these guidelines with scholars, institutions, and communities worldwide. Stay engaged as we expand practical tools, institutional models, and collaborative pathways that advance ethical, rigorous, and socially responsible AI-enabled scholarship.