The projects in this guide use a student-driven approach to learning. Instead of simply learning about AI through videos or lectures, the students completing these projects are active participants in their AI exploration. In the process, students work directly with innovative AI technologies, participate in “unplugged” activities that further their understanding of how AI technologies work, and create various authentic products—from machine learning models to video games—to demonstrate their learning.

Project 1: Programming with Machine Learning
Project 2: AI-Powered Players in Video Games
Project 3: Using AI for Robotic Motion Planning
Project 4: Machine Learning as a Service

Visit the ISTE website with all the free practical guides for engaging students in AI creation: https://www.iste.org/areas-of-focus/AI-in-education

This guide provides student-driven projects that can directly teach subject area standards in tandem with foundational understandings of what AI is, how it works, and how it impacts society.

Instead of simply learning about AI through videos or lectures, the students completing these projects are active participants in their AI exploration. In the process, students work directly with innovative AI technologies, participate in “unplugged” activities that further their understanding of how AI technologies work, and create various authentic products—from presentations to designing an AI robot—to demonstrate their learning.
• Project 1: What AI Does Well and Does Not Do Well
• Project 2: Training Data and Machine Learning
• Project 3: Senses vs. Sensors
• Project 4: Navigation and AI

Visit the ISTE website with all the free practical guides for engaging students in AI creation: https://www.iste.org/areas-of-focus/AI-in-education

This guide provides student-driven projects that can directly teach subject area standards in tandem with foundational understandings of what AI is, how it works, and how it impacts society. Several key approaches were taken into consideration in the design of these projects. Understanding these approaches will support both your understanding and implementation of the projects in this guide, as well as your own work to design further activities that integrate AI education into your curriculum.

Project 1: AI Chatbots
Project 2: Developing a Critical Eye
Project 3: Using AI to Solve Environmental Problems
Project 4: Laws for AI

Visit the ISTE website with all the free practical guides for engaging students in AI creation: https://www.iste.org/areas-of-focus/AI-in-education

In this guide, students’ exploration of AI is framed within the context of ethical considerations and aligned with standards and concepts, and depths of understanding that would be appropriate across various subject areas and grade levels in K–12. Depending on the level of your students and the amount of time you have available, you might complete an entire project, pick and choose from the listed activities, or you might take students’ learning further by taking advantage of the additional extensions and resources provided for you. For students with no previous experience with AI education, exposure to the guided learning activities alone will create an understanding of their world that they likely did not previously have. And for those with some background in computer science or AI, the complete projects and resources will still challenge their thinking and expose them to new AI technologies and applications across various fields of study.

Project 1: Fair's Fair
Project 2: Who is in Control?
Project 3: The Trade-offs of AI Technology
Project 4: AI and the 21st Century Worker

Visit the ISTE website with all the free practical guides for engaging students in AI creation: https://www.iste.org/areas-of-focus/AI-in-education.

With the release of ChatGPT in November 2022, the field of higher education rapidly became aware that generative AI can complete or assist in many of the kinds of tasks traditionally used for assessment. This has come as a shock, on the heels of the shock of the pandemic. How should assessment practices change? Should we teach about generative AI or use it pedagogically? If so, how? Here, we propose that a set of open educational practices, inspired by both the Open Educational Resources (OER) movement and digital collaboration practices popularized in the pandemic, can help educators cope and perhaps thrive in an era of rapidly evolving AI. These practices include turning toward online communities that cross institutional and disciplinary boundaries. Social media, listservs, groups, and public annotation can be spaces for educators to share early, rough ideas and practices and reflect on these as we explore emergent responses to AI. These communities can facilitate crowdsourced curation of articles and learning materials. Licensing such resources for reuse and adaptation allows us to build on what others have done and update resources. Collaborating with students allows emergent, student-centered, and student-guided approaches as we learn together about AI and contribute to societal discussions about its future. We suggest approaching all these modes of response to AI as provisional and subject to reflection and revision with respect to core values and educational philosophies. In this way, we can be quicker and more agile even as the technology continues to change.

We give examples of these practices from the Spring of 2023 and call for recognition of their value and for material support for them going forward. These open practices can help us collaborate across institutions, countries, and established power dynamics to enable a richer, more justly distributed emerging response to AI.

In this video segment adapted from the Massachusetts Institute of Technology, researchers in the Artificial Intelligence Laboratory working to engineer smarter robots are now building a machine that interacts socially with people.

Students are introduced to the concept of light pollution by investigating the nature, sources and levels of light in their classroom environment. They learn about the adverse effects of artificial light and the resulting consequences on humans, animals and plants: sky glow, direct glare, light trespass, animal disorientation and energy waste. Student teams build light meters using light sensors mounted to LEGO® MINDSTORMS® NXT intelligent bricks and then record and graph the light intensity emitted in various classroom lighting situations. They are introduced to the engineering concepts of sensors, lux or light meter, and lumen and lux (lx) illuminance units. Through this activity, students also learn how to better use light and save energy as well as some of the technologies designed by engineers to reduce light pollution and energy waste.

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 have developed a new method for teaching self-driving cars how to respond to emergencies. Unlike other approaches, which teach cars to respond according to hard and fast rules, this new method trains onboard computers to react like humans do. That unique ability could make self-driving cars vastly quicker at recognizing and avoiding potential accidents. Human drivers react instinctively to road hazards—whether that’s a car that brakes suddenly or a cyclist who rushes into traffic. It’s an ability that comes from years of experience and one that’s often taken for granted. As AI experts have learned, teaching computers to do the same is notoriously difficult. Rule-based methods provide basic functionality. But they tend to be very time-consuming and can’t account for unforeseen emergencies—two tremendous liabilities for self-driving cars..."

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 China have pooled together some of the most powerful techniques in machine learning to create the ultimate control system. Successfully deployed in AI-regulated hybrid electric vehicles, the framework could grant other autonomous systems unprecedented levels of control and foresight. Machine learning is booming. And arguably the most popular technique in this branch of artificial intelligence is deep reinforcement learning. Loosely modeled after our brains’ reward system, deep reinforcement learning has enabled machines to reach or even surpass human-level performance in various tasks. Those tasks range from the trivial, like playing Go or video games, to the possibly life-saving, such as detecting firearms from video. But deep reinforcement learning algorithms have their limitations. For one, they generally lack the ability to take lessons learned in one task and apply them to another..."

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

The People's Guide to Artificial Intelligence is an educational and speculative approach to understanding artificial intelligence (AI) and its growing impact on society. The 78-page booklet explores the forms AI takes today and the role AI-based technologies can play in fostering equitable futures. The project resists narratives of dystopian futures by using popular education, design, and storytelling to lay the groundwork for creative imaginings.

Short Description:
This book is intended to be a pragmatic guide to helping able citizen data scientists to utilize common frameworks and tools to create conversational artificial intelligence experiences for users.

Long Description:
This book is intended to be a pragmatic guide to helping able citizen data scientists to utilize common frameworks and tools to create conversational artificial intelligence experiences for users.

Word Count: 4670

(Note: This resource's metadata has been created automatically by reformatting and/or combining the information that the author initially provided as part of a bulk import process.)

Short Description:
This book provides an overview of the field of natural language processing and recently developed methods, presuming only knowledge of computing with data structures.

Long Description:
This book allows a reader with a background in computing to quickly learn about the principles of human language and computational methods for processing it. The book discusses what natural language processing (NLP) is, where it is useful, and how it can be deployed using modern software tools. It covers the core topics of modern NLP, including an overview of the syntax and semantics of English, benchmark tasks for computational language modelling, and higher level tasks and applications that analyze or generate language. It takes the perspective of a computer scientist. The primary themes are abstraction, data, algorithms, applications and impacts. It also includes history and trends that are important for understanding why things have been done the way that they have.

Word Count: 70048

ISBN: 978-1-7376595-1-8

(Note: This resource's metadata has been created automatically by reformatting and/or combining the information that the author initially provided as part of a bulk import process.)

Los proyectos de esta guía utilizan un enfoque centrado en los alumnos para el aprendizaje. En lugar de solo aprender acerca de la IA con videos o conferencias, los alumnos que realizan estos proyectos son participantes activos en la exploración de ella. En el proceso, los estudiantes trabajarán directamente con tecnologías innovadoras de IA, participarán en actividades no en línea para ampliar su comprensión de cómo funcionan las tecnologías de IA y crearán diversos productos auténticos
desde modelos de aprendizaje automático hasta videojuegos— para demostrar su aprendizaje.

PROYECTO 1: Programación con aprendizaje automático
PROYECTO 2: Jugadores asistidos por IA en videojuegos
PROYECTO 3: Uso de la IA para planificar movimientos robóticos
PROYECTO 4: El aprendizaje automático como un servicio

Esta guía ofrece proyectos centrados en los alumnos que pueden enseñar directamente estándares de áreas de estudio en conjunto con comprensiones fundamentales de los que es la IA, cómo funciona y cómo impacta a la sociedad. Fueron considerados varios enfoques clave para diseñar estos proyectos. Entender estos enfoques sustentará su comprensión y la implementación de los proyectos de esta guía, así como su trabajo para diseñar más actividades que integren la enseñanza sobre la IA en su plan de estudios.

PROYECTO 1: Lo que la IA hace bien y lo que no hace tan bien
PROYECTO 2: Datos de entrenamiento y aprendizaje automático
PROYECTO 3: Los sentidos comparados con los sensores
PROYECTO 4: Navegación e IA

Esta guía ofrece proyectos centrados en los alumnos que pueden enseñar directamente estándares de áreas de estudio en conjunto con comprensiones fundamentales de los que es la IA, cómo funciona y cómo impacta a la sociedad. Fueron considerados varios enfoques clave para diseñar estos proyectos. Entender estos enfoques sustentará su comprensión y la implementación de los proyectos de esta guía, así como su trabajo para diseñar más actividades que integren la enseñanza sobre la IA en su plan de estudios.

PROYECTO 1: Chatbots de IA
PROYECTO 2: Desarrollo de una mirada crítica
PROYECTO 3: Uso de la IA para resolver problemas del medio ambiente
PROYECTO 4: Leyes para la IA

En esta guía, la exploración de la IA por parte de los alumnos se enmarca en el contexto de las consideraciones éticas, y en concordancia con los estándares, conceptos y profundidad adecuados para varias materias de K–12. Dependiendo del nivel de sus alumnos y la cantidad de tiempo que tenga disponible, puede completar todas las actividades de Inicio hasta las actividades de Demostraciones culminantes; puede seleccionar actividades de la lista; o puede llevar el aprendizaje de los alumnos más lejos, aprovechando las extensiones y recursos adicionales proporcionados. Para los alumnos sin experiencia previa de formación en la IA, la exposición misma a las actividades de aprendizaje guiadas creará una comprensión de su mundo que probablemente no tenían antes. Y para aquellos con conocimientos previos en informática o con la IA, los proyectos y recursos completos seguirán desafiando su razonamiento y los expondrán a nuevas tecnologías y aplicaciones de la IA en diversos campos de estudio.

PROYECTO 1: Lo justo es justo
PROYECTO 2: ¿Quién tiene el control?
PROYECTO 3: Las ventajas y desventajas de la tecnología de la IA
PROYECTO 4: La IA y el trabajador del siglo XXI

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 making rapid advances in medicine. Already, there are machine learning algorithms that can outperform doctors in some medical fields. There’s only one fairly big problem: experts aren’t quite sure how these algorithms work. While designers know full well what goes into the A-I systems they build and what comes out, the learning part in between is often too complex to comprehend. To their users, machine learning algorithms are effectively black boxes. Now, researchers from the RIKEN Center for Advanced Intelligence Project in Japan are lifting the lid. They’ve developed a deep-learning system that can outperform human experts in predicting whether prostate cancer will reoccur within one year. More importantly, the deep learning system they developed can acquire human-understandable features from unannotated pathology images to offer up critical clues that could help humans make better diagnoses themselves..."

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

The Shallow and the Deep is a collection of lecture notes that offers an accessible introduction to neural networks and machine learning in general. However, it was clear from the beginning that these notes would not be able to cover this rapidly changing and growing field in its entirety. The focus lies on classical machine learning techniques, with a bias towards classification and regression. Other learning paradigms and many recent developments in, for instance, Deep Learning are not addressed or only briefly touched upon.

Biehl argues that having a solid knowledge of the foundations of the field is essential, especially for anyone who wants to explore the world of machine learning with an ambition that goes beyond the application of some software package to some data set. Therefore, The Shallow and the Deep places emphasis on fundamental concepts and theoretical background. This also involves delving into the history and pre-history of neural networks, where the foundations for most of the recent developments were laid. These notes aim to demystify machine learning and neural networks without losing the appreciation for their impressive power and versatility.

Short Description:
Students in an undergraduate seminar in Cognitive Science share what they've learned, regarding the state of the field in AI research. While AI work is remarkable, even the most astonishing projects are a far cry from replicating human intelligence. Why is that? Well, for one, humans have yet to pin down their own intelligence.

Long Description:
Students in an undergraduate seminar in Cognitive Science share what they’ve learned, regarding the state of the field in AI research. While AI work is remarkable, even the most astonishing projects are a far cry from replicating human intelligence. Why is that? Well, for one, humans have yet to pin down their own intelligence. While work in AI may eventually point humans towards an answers to pressing questions regarding the nature of consciousness and matters of intelligence, at this present moment, the hard problem of consciousness is still that: a problem without a solution.

Word Count: 17607

(Note: This resource's metadata has been created automatically by reformatting and/or combining the information that the author initially provided as part of a bulk import process.)

Short Description:
Social media, digital devices, and networked communication systems have become fully integrated into our everyday living experience. This e-book touches upon the human experience of contemporary trends that affect how we perceive ourselves, others, and society.

Long Description:
Authored as a companion to COMM601 Trends in Digital & Social Media, Granite State College (USNH), Concord, NH.

Word Count: 25859

(Note: This resource's metadata has been created automatically by reformatting and/or combining the information that the author initially provided as part of a bulk import process.)