AI Tools and Resources

This webpage provides a central hub for information, resources, news and events related to genAI across the university. Updates will be provided as tools and guidelines continue to evolve.

USF's CITL offers workshops, courses, and a place for discussion on how to incorporate new AI tools.

USF's CITL guide to incorporating generative AI tools into teaching and learning goals

USF Institute for Artificial Intelligence offers a schedule of seminars and events discussing trends and advancements in AI.

Information for faculty on using and citing generative AI from USF Faculty Success Teaching & Learning

Guidance from the Office of Graduate studies on how generative AI tools and Large Language Models may be used in theses and dissertations.

A guide for the responsible design and implementation of AI systems in the public sector.

This page from the Markkula Center for Applied Ethics at Santa Clara University explores sixteen challenges and opportunities at the intersection of artificial intelligence and ethics.

This is an ethics guide for United States Intelligence Community personnel on how to procure, design, build, use, protect, consume, and manage AI and related data. Answering these questions, in conjunction with your agency-specific procedures and practices, promotes ethical design of AI consistent with the Principles of AI Ethics for the Intelligence Community.

The linked Harvard Gazette article from 2020 discusses the mounting ethical concerns surrounding the increasing involvement of AI in decision-making processes.

The Institute for Ethics in AI brings together world-leading philosophers and other experts in the humanities with the technical developers and users of AI in academia, business and government. The ethics and governance of AI is an exceptionally vibrant area of research at Oxford University and the Institute is an opportunity to take a bold leap forward from this platform.

Artificial intelligence (AI) and robotics are digital technologies that will have significant impact on the development of humanity in the near future. They have raised fundamental questions about what we should do with these systems, what the systems themselves should do, what risks they involve, and how we can control these.

UNESCO has led the international effort to ensure that science and technology develop with strong ethical guardrails for decades. Ethical concerns arise from the potential AI systems have to embed biases, contribute to climate degradation, threaten human rights and more. Such risks associated with AI have already begun to compound on top of existing inequalities, resulting in further harm to already marginalized groups.

The World Health Organization (WHO)'s guidance on the ethics and governance of large multi-modal models (LMMs) – a type of fast growing generative artificial intelligence (AI) technology with applications across health care.

Harvard Business Publishing article on ways to integrate generative AI into classroom environments and curriculum.

Illinois State University
Center for Integrated Professional Development

USF's CITL offers workshops, courses, and a place for discussion on how to incorporate new AI tools.

Tips for including AI concepts and processes in higher education curriculum.

This guide offers practical strategies to help MIT Sloan faculty responsibly integrate generative AI tools into their teaching.

UNC Charlette's Center for Teaching and Learning has launched a stream of programs and resources for faculty and instructional staff to support the increasing role that Generative Artificial Intelligence (AI) is playing in shaping teaching, learning, and classroom learning experiences.

Some links may be proprietary.

Examining the possibilities—and the dangers—of this rapidly developing technology

"AI in Academia: Navigating the Future," is a podcast where the realms of higher education and artificial intelligence converge. Hosted by two colleagues at Bentley University, a faculty member studying AI and the Director of Academic Technologies, this series explores the multifaceted impacts, risks, and growing opportunities of AI in the world of academia.

In this blog post, the editors have compiled a list of ten AI and Machine Learning podcasts.

LinkedIn Learning, available authenticating with your USF credentials, offers several tutorial videos of multiple skill levels on many different aspects of AI and machine learning.

The Conversation, a non-profit news organization enlisting scholars at universities worldwide, has a series of articles probing into the potential benefits and drawbacks of AI tools

"The Berkman Klein Center's mission is to explore and understand cyberspace; to study its development, dynamics, norms, and standards; and to assess the need or lack thereof for laws and sanctions."

Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report

"AI will make our lives better. But AI will also have downstream consequences that we have just the earliest inklings of," Cathryn Carson, chair of UC Berkeley's history department, said in a Q&A.

Arti cial Intelligence (AI) research at the University of Michigan is comprised of a multidisciplinary group of researchers conducting theoretical, experimental, and applied investigations of intelligent systems. Current projects include research in cognitive architectures, distributed systems of multiple agents, machine learning, data mining, computer vision, natural language processing, robotics, computational healthcare, human computing, and computational social science, and others.

The Vector Institute is an independent, not-for-profit corporation dedicated to AI research.

The Institute for Artificial Intelligence + X is a university wide research and education center for Artificial Intelligence. It has a focus on collaboration across disciplines. The goal is to establish a world-class academic research and development (R&D) center at the University of South Florida to conduct externally-funded research in Artificial Intelligence (AI) and associated areas (X = Healthcare, Medicine, Biology, Cybersecurity, Finance, Business, Manufacturing, Transportation), using a transdisciplinary approach across Neuroscience, Cognitive Science, and Computer Science, and work with industry to transition them into products that benefit humanity in an ethical and responsible manner.