Description: Students play with an AI that can react to their movement, and choreograph something that uses this tool. They can show off their choreography to the rest of the class
Skills/knowledge you'll gain: Movement, collaboration
Length: 1-4 hours

Curriculum aligns to:
- NGSS Engineering standards
- ISTE standards
- Common Core ELA/Literacy standards
- Also maps to CSTA standards

Description: AI can create realistic-looking images and videos that were never actually filmed or taken as a photo. These images and videos are called deepfakes. This is a quick lesson requiring no background where students learn about deepfakes, as well as ways to verify information that they see
Skills/knowledge you'll gain: Critical thinking
Length: 1 hour

Curriculum aligns to:
- NGSS Engineering standards
- ISTE standards
- Common Core ELA/Literacy standards
- Also maps to CSTA standards

Description: A quick and fun way to become familiar with some core ideas of modern AI while playing with Google Quick, Draw
Skills/knowledge you'll gain: Understanding of what AI is, privacy and bias concerns
Length: 1 hour

Curriculum aligns to:
- NGSS Engineering standards
- ISTE standards
- Common Core ELA/Literacy standards
- Also maps to CSTA standards

Description: An interactive deep dive into the sort of ethical concerns that companies creating AI based systems should consider. Students explore these ideas through role-playing running their own companies and making ethical decisions for those companies
Skills/knowledge you'll gain: Human-centered design, ethics
Length: 10 hours

Curriculum aligns to:
- NGSS Engineering standards
- ISTE standards
- Common Core ELA/Literacy standards
- Also maps to CSTA standards

Description: A short deep dive into how facial recognition technology is used, some biases it has, and some ways companies, individuals, and the law are fighting against its use in surveillance
Skills/knowledge you'll gain: Ethics
Length: 1 hour

Curriculum aligns to:
- NGSS Engineering standards
- ISTE standards
- Common Core ELA/Literacy standards
- Also maps to CSTA standards

The Student Guide to Artificial Intelligence is an initiative of Elon University, in partnership with the American Association of Colleges and Universities (AAC&U). It is a continuation of a 2023 global collaboration that established a statement of principles to guide development of AI policies and practices in higher education. The statement was released at the United Nations Internet Governance Forum in Kyoto, Japan, and was supported by more than 140 higher education organizations, administrators, researchers and faculty members from 48 countries.

NOTE -- Generative AI was used in the creation of this resources. You can find more information, including the prompts used, here: https://studentguidetoai.org/about/

This resource includes traditional writing prompts as well as prompts and assignments that utilize AI-generated text. It covers the fields of Creative Writing, Composition, and Business and Technical Writing. 

Learn about the implementation and practical aspects of Artificial Intelligence and how to write a plan for applying AI in your own organization in a step-by-step manner.

This course is not about difficult algorithms and complex programming; it is a course for anyone interested in learning how to integrate AI into their own organization.

To understand how current Artificial Intelligence applications can be successfully integrated in organizations, we look at different examples. For instance, how ING uses reinforcement learning for personalized dialog management with its customers or how Radboud UMC uses diagnostic image analysis to discover early stages of infectious diseases.

As part of our two-course program ‘AI in Practice’, this course will guide you in the practical aspects of applying AI in your own organization. You will examine typical applications of AI in use already and learn from their experience. These include challenges of implementation, lifecycle aspects, as well as the maintenance and management of AI applications.

The course presents a variety of case studies from actual situations in public organizations and private enterprises in the healthcare, financial, retail and telecommunications sectors. These include Radboud UMC, the Municipality of Amsterdam, ING, Ahold Delhaize and KPN.

‘AI in Practice – Applying AI’ gives you the ammunition to understand the practical aspects required for the implementation of a variety of AI applications in your organization.

This course, AI in Practice: Preparing for AI, is the 1st course of the online education program AI in Practice. The course gives you a kaleidoscope of examples of applications of AI in various organizations, outlines the state of the art in modern AI research, and provides practical tools for integrating AI into your own organization. The program AI in Practice is built from two initial courses, AI in Practice: Preparing for AI and AI in Practice: Applying AI.

The AI in Practice: Preparing for AI course is designed for people who want to apply AI in their own practical situation.

For the experienced manager who wants to know what AI can do for her own organization.
For the data analyst or business consultant who wants to understand how AI can be applied in the business processes of the company for which they work.
For the student who wants to understand how the results of AI research can be translated into practical applications.

A one-stop shop to get started on the key considerations about data for AI! Learn how crowdsourcing offers a viable means to leverage human intelligence at scale for data creation, enrichment and interpretation, demonstrating a great potential to improve both the performance of AI systems and their trustworthiness and increase the adoption of AI in general.

Description: Learn how conservationists use AI image recognition to save time identifying how and where to protect endangered species. Build your own species-identifying AI system
Skills/knowledge you'll gain: Conservation
Length: 1-2 hours

Curriculum aligns to:
- NGSS Engineering standards
- ISTE standards
- Common Core ELA/Literacy standards
- Also maps to CSTA standards

This lesson centers around the How AI Works: Equal Access and Algorithmic Bias video from the How AI Works video series. Watch this video first before exploring the lesson plan.

In this lesson, students will practice cropping images to uncover the bias underlying the Twitter cropping algorithm. Then, students will read and watch a video about the discovery of this biased algorithm. Finally, students will discuss ways to recognize and reduce bias along with analyzing Twitter's response to the allegations of bias in their cropping algorithm.

This lesson can be taught on its own, or as part of a 7-lesson sequence on How AI Works. Duration: 45 minutes

Grâce à une analyse multidisciplinaire et décomplexée de notre relation à l’intelligence artificielle (I.A.), nous questionnerons nos usages quotidiens de l’I.A. Nous remonterons à l’origine historique de la notion d’« intelligence artificielle » afin d’en tirer des questions pratiques et des réponses concrètes, ainsi que des pistes de réflexion. Ensuite, nous aborderons les principes de base du fonctionnement de l’I.A. moderne pour y puiser une série de clés de compréhension. Enfin, nous nous arrêterons sur les craintes et idées reçues qui cernent le débat actuel autour de l’I.A. pour alors débattre collectivement de questions résolument actuelles.

This course material is created as flipped classroom sessions for Artificial Intelligence Course

An introduction to the main techniques of Artifical Intelligence: state-space search methods, semantic networks, theorem-proving and production rule systems. Important applications of these techniques are presented. Students are expected to write programs exemplifying some of techniques taught, using the LISP lanuage.

Courses on Artificial Intelligence (AI) and Librarianship in ALA-accredited Masters of Library and Information (MLIS) degrees are rare. We have all been surprised by ChatGPT and similar Large Language Models. Generative AI is an important new area for librarianship. It is also developing so rapidly that no one can really keep up. Those trying to produce AI courses for the MLIS degree need all the help they can get. This book is a gesture of support. It consists of about 95,000 words on the topic, with a 3-400 item bibliography.

9. Brave New World - AI/ML

The trifecta of globalization, urbanization and digitization have created new opportunities and challenges across our nation, cities, boroughs and urban centers. Cities are in a unique position at the center of commerce and technology becoming hubs for innovation and practical application of emerging technology. In this rapidly changing 24/7 digitized world, city governments worldwide are leveraging innovation and technology to become more effective, efficient, transparent and to be able to better plan for and anticipate the needs of its citizens, businesses and community organizations. This class will provide the framework for how cities and communities can become smarter and more accessible with technology and more connected.

Artificial Intelligence (AI) promises to offer tremendous opportunities for service excellence, creation of newer domain of employment avenues, and in the process augment industrial and economic growth for countries. The question, however, is India ready to embrace AI and pave for a new technology revolution? India’s history does indeed support this possibility. In the past, starting in the late 1980s, India successfully clasped the computer revolution bubble and took almost a decade to become an information technology (IT) leader in the world. Today, the Indian IT sector contributes significantly to the country’s GDP and is among the highest employment opportunity provider in the country. The journey of India as an IT major, however, has not been without its ups and downs.  

What is the current state of artificial intelligence (AI) in the world of scholarly communication? What impact does AI have on the practices and strategies of publishers, libraries, information technology companies, and researchers? What exactly is AI and what are those in the realm of scholarly communication actually thinking about it and doing with it?

This Charleston Briefing seeks to provide some answers to these very important questions, offering both general essays on AI and more specific essays on AI in scholarly publishing, academic libraries, and AI in information discovery and knowledge building. The essays will help publishers, librarians, and researchers better understand the actual impact of AI on libraries and publishing so that they can respond to the potentially transformative impact of AI in a measured and knowledgeable manner.

"Charleston Briefings: Trending Topics for Information Professionals" is a thought-provoking series of brief books concerning innovation in the sphere of libraries, publishing, and technology in scholarly communication. The briefings, growing out of the vital conversations characteristic of the Charleston Conference and Against the Grain, will offer valuable insights into the trends shaping our professional lives and the institutions in which we work.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Artificial intelligence is transforming our way of life. Able to spot patterns invisible to the human eye, algorithms are learning how to make our lives easier, safer, and more fun. That power is not lost on materials researchers. During the next decade, artificial intelligence or AI-driven research could fundamentally transform how new and better materials are developed. What’s more, it might even revamp how materials research itself is carried out, enabling promising new materials and processes to be developed more quickly. Machine learning methods come in a variety of flavors, with some requiring more guidance, or “supervision,” from researchers. But, generally, a machine-learning algorithm designed to discover and understand the behavior of materials looks for patterns connecting the composition, structure, and properties of known materials..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.