A brief five-module course designed as a non-credit-bearing introduction to AI tools for high school and college students. Adapted from a similar course by Rush University and shared under the CC BY NC SA 4.0 International License.

The Foundations of Applied AI course is an introductory exploration into the world of artificial intelligence (AI), designed for undergraduates with no prior experience in AI. This online, 3-credit course offers a deep dive into AI's core concepts, applications, and the ethical implications of deploying AI technologies across various industries. Through multimedia lectures, case studies, and hands-on projects, students will gain a comprehensive understanding of how AI can transform business, healthcare, and more, while also navigating the ethical considerations vital for responsible AI use.By the end of the course, students will be equipped to identify AI opportunities, understand AI's potential impacts, and discuss the importance of ethical frameworks in AI development. This course lays the groundwork for a future in AI, preparing students for further specialization or to apply AI insights in their fields.

Typical machine learning classes teach techniques to produce effective models for a given dataset. In real-world applications, data is messy and improving models is not the only way to get better performance. You can also improve the dataset itself rather than treating it as fixed. Data-Centric AI (DCAI) is an emerging science that studies techniques to improve datasets, which is often the best way to improve performance in practical ML applications. While good data scientists have long practiced this manually via ad hoc trial/error and intuition, DCAI considers the improvement of data as a systematic engineering discipline.

This is the first-ever course on DCAI. This class covers algorithms to find and fix common issues in ML data and to construct better datasets, concentrating on data used in supervised learning tasks like classification. All material taught in this course is highly practical, focused on impactful aspects of real-world ML applications, rather than mathematical details of how particular models work. You can take this course to learn practical techniques not covered in most ML classes, which will help mitigate the “garbage in, garbage out” problem that plagues many real-world ML applications.

This lesson centers around the How AI Works: What is Machine Learning? video from the How AI Works video series. Watch this video first before exploring the lesson plan.

In this lesson students are introduced to a form of artificial intelligence called machine learning and how they can use the Problem Solving Process to help train a robot to solve problems. They participate in three machine learning activities where a robot - AI Bot - is learning how to detect patterns in fish.

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

Jill Reilly (National Archives and Records Administration), Abigail Potter (Library of Congress) and Mike Trizna (Smithsonian Data Science Lab) present 'Leadership and Collaboration to Advance the Responsible Adoption of Artificial Intelligence in Libraries, Archives and Museums' during the AI Use C... This item belongs to: movies/fantastic-futures-annual-international-conference-2023-ai-for-libraries-archives-and-museums-02.

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Andrew Marcinek is an innovative K-12 leader, author, and national speaker. His work is focused on the intersection of instructional design and Technology.  This article, published in Medium in March 2024, discusses best practices for K-12 Technology Directors when leading an Artificial Intelligence implementation for their district.

Open Nordic library-produced LEARNING RESOURCES that develop students' academic digital competence.
Learning Ressources in LearningLib.org cover subjects like:
Data Management
AI
Information seeking
Source Critisism
Reference Management
Literature seeking

Linda Marie Golian-Lui and Mahesh Neelakanta (Florida Atlantic University) present 'Lessons Learned: Creating a Sustainable AI Lab with College Partners' during the Short Talk and Demo session at the Fantastic Futures ai4LAM 2023 annual conference. This item belongs to: movies/fantastic-futures-annual-international-conference-2023-ai-for-libraries-archives-and-museums-02.

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Powered by the School Library Systems Association of New York and made possible in part through the Allison-Rosen Foundation, LibraryReady.AI is an initiative that aims to integrate artificial intelligence, media literacy, and information fluency into New York State's school libraries.  The goal is to prepare students for an increasingly digital and AI-driven world.

This resource includes a set of activities, teacher guides, assessments, materials, and more to assist educators in teaching about the ethics of artificial intelligence. These activities were developed at the MIT Media Lab to meet a growing need for children to understand artificial intelligence, its impact on society, and how they might shape the future of AI.

In this lab, students will train three simple neural networks using the AP Gridworld software and a perceptron neural network. The lab culminates when students have trained an autonomous car to drive around simple cars without crashing.

On the eve of the CC Global Summit, members of the CC global community and Creative Commons held a one-day workshop to discuss issues related to AI, creators, and the commons. The community attending the Summit has a long history of hosting these intimate discussions before the Summit begins on critical and timely issues.

Emerging from that deep discussion and in subsequent conversation during the three days of the Summit, this group identified a set of common issues and values, which are captured in the statement below. These ideas are shared here for further community discussion and to help CC and the global community navigate uncharted waters in the face of generative AI and its impact on the commons.

This lecture video describes problems associated with HRP, guidelines for effective HRP and the trends impacting manpower planning.
Problems Associated with Manpower Planning
HR practitioners are perceived as experts in handling personnel matter but are not experts in managing business. The personnel plan conceived and formulated by the HR practitioners when enmeshed with organizational plan might make the overall strategic plan itself defective.
HR information often is incompatible with the information used in strategy formulation. Strategic planning efforts have long been oriented towards financial forecasting often to the exclusion of other types of information.
Conflict may exist between short-term and long-term HR needs. For example, there arises a conflict between the pressure to get work done on time and long-term needs, such as preparing people for assuming greater responsibilities. Many managers are of the belief that HR needs can be met immediately because skills are available on the market as long as wages and salaries are competitive. These managers fail to recognize that by resorting to hiring or promoting depending on short term needs alone, long-term issues are neglected.
There is conflict between quantitative and qualitative approach to HRP. Some people view HRP as a number game designed to track the flow of people across the departments. These people rely on a strictly quantitative approach to planning. Others take a qualitative approach and focus on individual employee concerns such as promotion and career development. Best results would accrue if there is a balance between the quantitative and qualitative approaches.
Non-involvement of operating managers renders HRP ineffective.
Recent Trends in HRP
Automation via Artificial Intelligence (AI) and Robotics Process Automation (RPA)
Virtual and Augmented Reality
Employee experience
HR Guidance
Gig Economy
Real Time Performance Feedback
Generational Shifts
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World Education's AI for Learning and Work initiative is dedicated to exploring the intersection of artificial intelligence and education, and how it can shape the future of the way we live and work.  In this webinar,  Jeff Goumas and Rachel Riggs share their insights.

In collaboration with the private and public sectors, the National Institute for Standards and Technology (NIST) has developed a framework to better manage risks to individuals, organizations, and society associated with artificial intelligence (AI). The NIST AI Risk Management Framework (AI RMF) is intended for voluntary use and to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems.

The Artificial Intelligence (AI) for K-12 initiative (AI4K12) is jointly sponsored by AAAI and CSTA.The initiative is developing (1) national guidelines for AI education for K-12, (2) an online, curated resource Directory to facilitate AI instruction, and (3) a community of practitioners, researchers, resource and tool developers focused on the AI for K-12 audience. 

This lesson centers around the How AI Works: Neural Networks video from the How AI Works video series. Watch this video first before exploring the lesson plan.

Students learn how neural networks work. They first discuss an example of an experience that recommends things to you. They then use a widget that recommends videos based on one person. Students watch a video explaining neural networks. They use an updated widget to adjust the weights of each person. Finally, students discuss the need for diverse perspectives when creating recommendation systems.

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

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:

"New sound-recording technology could help researchers keep closer tabs on biodiversity—a rapidly dwindling measure of global health. Biodiversity is critical to maintaining the global ecosystems that provide our basic needs – air, water and soil able to grow food. Human activities such as overexploitation of resources, pollution, habitat alteration and climate change are causing biodiversity loss so extreme that many caution we are entering the sixth mass extinction. But it’s not too late. World leaders have united to promise to conserve biodiversity. Meaningful efforts, however, require cost-effective strategies. One of the most promising solutions to emerge in recent years is passive acoustic monitoring. Natural soundscapes provide rapid insight into the diversity of animals in a certain location, based on metrics known as acoustic biodiversity indices. These audio features are proven to predict the number of species in a given area..."

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

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:

"Researchers from Canada have developed a new control scheme that could improve robots’ ability to home in on and interact with target objects. Their approach could help rev up robots designed to perform various industrial tasks, from polishing and welding to painting and inspection—and perhaps beyond. Robots these days are capable of some pretty amazing things. But one area where robots could use more help is in navigating toward and handling objects. It’s a seemingly simple task that we often take for granted. Consider the scenario of returning to our vehicle after a trip to the supermarket. As we step outside, we must first spot our car among those parked next to it, identifying it by shape (or model) and color. We then have to make our way toward it, unlock and open the door when we’re within the right range, unload, and enter the driver’s seat—all while other shoppers and cars interfere with our path..."

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