# data[].attributes.attachment_link.readable_text
These products can be used in many different ways, and are also called "foundation models." This category includes products like generative AI, such as chatbots and products that create images from text inputs, translation tools, or computer vision models that can examine images and detect objects like logos, flowers, dogs, or buildings.

These products are built for a specific purpose, but they aren't specifically designed for kids or education. Examples of this category include automated recommendations in your favorite streaming app, or the way an app sorts the faces in a group of photos so you can find pictures of your niece at a wedding.

This category is a subset of Applied Use products, and it covers products specifically built for use by kids and teens, either at home or in school. This category also includes education products designed for teachers or administrators (such as a virtual assistant for teachers) that are ultimately intended to benefit students in some way.

# data[].attributes.attachment_link.description
Common Sense Media improves the lives of kids and families by providing independent reviews, age ratings, & other information about all types of media.

# data[].attributes.attachment_link.readable_text
While AI policy debates continue across school systems, educators aren't waiting for perfect guidelines; they're finding practical ways to use AI to deepen student learning today.

Powerful Learning fosters student agency, purpose, curiosity, and connection, and two Verizon Innovative Learning Schools coaches-Rachael Eberhardt from Albuquerque School of Excellence in New Mexico and Murissa Mays from Frontier Schools in Kansas City, Missouri-are seeing artificial intelligence create real opportunities for these elements to flourish. While challenges around academic integrity and equitable access remain, there are many bright spots in their schools.

Teaching Students to Use AI as a Thinking Tool

At Frontier Schools in Kansas City, instructional coach Murissa Mays observed a teacher transform how students approach research and writing. Instead of banning AI, she explicitly taught students to use it ethically and strategically. Students used Goblin Tools to break down complex assignments into manageable steps, particularly helping students who struggled with executive functioning and task initiation. When they began their recent essays, they learned to use Perplexity to locate reliable, citable sources to strengthen their arguments, not replace their thinking.

"Students are now able to streamline parts of their workflow while still producing high-quality, authentic work that reflects their own thinking," Coach Mays said. The teacher provided sample prompts, discussed boundaries, and invited students to ask when they were unsure if their AI use was appropriate. This transparency around appropriate use directly addresses integrity concerns while building student agency.

At Albuquerque School of Excellence, instructional coach Rachael Eberhardt identifies similar potential for fostering curiosity through Magic School 's "rooms" feature, where teachers can create AI tutors that play specific roles. Teachers maintain visibility into student work, able to spot when students might be copying rather than developing their own voice.

When Danielle Duryea, a fifth-grade teacher, wanted her students to engage meaningfully with the American Revolution, she created tutor bots representing different historical figures that students could interview while drafting their essays. According to Ms. Duryea, "Students were more engaged and on-task, and their writing was more detailed, as they were able to connect to a time in history that was hard for them to conceptualize initially." This approach gives students more ways to explore ideas while keeping their learning visible and authentic.

Building Teacher Confidence and Capacity

At both schools, the coaches recognized that teacher readiness varied widely. Coach Mays facilitated professional development with Teach Share AI, where teachers learned to generate differentiated learning activities. Starting with prompts like "Generate an intervention activity for intermediate English language learners," teachers watched as the platform refined prompts for precision, then produced quality instructional materials with one click.

Teachers discovered they could edit the AI-generated documents like Canva designs, adjusting activity types (e.g., do now, independent practice, small group work) and creating tiered resources aligned to student data trends. The impact was immediate: Several teachers expressed newfound confidence in using AI for differentiation. One Teacher Leader Corps member applied for and was selected as a Teach Share Ambassador, expanding AI capacity across the campus by gaining access to new releases and other perks.

Coach Mays also uses AI in her own coaching practice to reduce bias in her classroom observation notes. "I like this because it provides a guardrail for me, to make sure I'm giving unbiased feedback," she said. "Observations can feel subjective, but asking AI to make my informal notes from the visit more objective makes it easier for me to process what happened in the classroom. It helps me be productive in my follow-up conversations, and it builds trust and teacher confidence when you are speaking from an unbiased lens."

Meanwhile, at Albuquerque School of Excellence, Coach Eberhardt approached teacher readiness differently. After attending an AI conference, she opened a staff presentation with Google's Quick Draw, having teachers guess what they were drawing while watching AI predict in real-time to demonstrate how predictive AI works. She then introduced her ChatGPT assistant, "Liam," complete with Irish accent, to demonstrate AI's capabilities and limitations in a disarming way.

"Teachers who are, understandably, nervous about AI often don't realize how often they already interact with it regularly and how it can simplify many mundane tasks," she said. One social studies teacher, Jonathan Buys, tried using Brisk AI to design an assessment with explicit parameters: include varying content (written and multimedia), consider standards and teacher objectives, and consider student grade level and general class characteristics. The resulting assessment, with the teacher's modifications, exceeded his expectations.

Mr. Buys explained that he was struggling to develop a cohesive assessment that was standards-aligned, but "the first time I used [Brisk AI], I couldn't believe how quickly I could develop a comprehensive assessment over a large unit; I was able to upload all my instructional materials and explain to the tool exactly what I needed it to develop." Since then, Mr. Buys has continued to employ Brisk AI in his lesson planning on a regular basis.

Both coaches emphasize that successful AI integration requires critical evaluation and maintaining student ownership of learning. Whether it's Coach Mays teaching students to recognize when they need help evaluating appropriate prompts, or Coach Eberhardt's teachers learning to adapt AI suggestions rather than accept them wholesale, the pattern remains consistent: AI used thoughtfully can amplify good teaching. It can enhance how students exercise agency, explore with curiosity, and connect authentically to their learning.

Getting Started

Ready to explore AI in your classroom or coaching practice? Consider these steps:

Focus on student thinking, not output. Use AI to deepen exploration and support different learners, not to shortcut the learning process.

Teach students boundaries first. Model appropriate prompts and invite questions about ethical use.

Begin with one tool and one purpose. Whether it's Perplexity for research or Goblin Tools for task management, master one use case before expanding.

Learn more about Verizon Innovative Learning Schools.

# data[].attributes.attachment_link.title
AI in Action: Instructional Coaches Share What's Working Now - Digital Promise

# data[].attributes.attachment_link.description
Two Verizon Innovative Learning Schools coaches share how they coach teachers to teach with AI.

# data[].attributes.attachment_link.readable_text
Artificial intelligence is growing by leaps and bounds. It's no longer science fiction anymore!

Lots of AI tools can serve as our own personal assistants -- answering questions, helping us write text, create images, speak more effectively, and more.

Time has always been a barrier in the classroom. Most teachers would agree that they would be able to do more -- including the things that are most important -- if they had more time.

Teachers can find AI tools that are ready to help them out right now. Lots of them are free or have "freemium" plans with limited free options.

Below is a description of AI and how you can start using it to support your work in class.

What is artificial intelligence?

Artificial intelligence (AI) refers to the ability of a computer or machine to perform tasks that would normally require human intelligence, such as learning, problem-solving, decision-making, and more. There are several different approaches to building AI systems, including machine learning, where a system is trained on a dataset and can improve its performance over time, and rule-based systems, where the system follows a set of predetermined rules to make decisions.

AI can be applied to a wide range of areas, including natural language processing, image and video recognition, robotics, and more. The goal of AI research is to create systems that can perform tasks at least as well as, or ideally better than, humans. AI has the potential to revolutionize many industries and transform the way we live and work, but it also raises important ethical and social questions.

30 AI tools for the classroom

We have gathered 30 of our favorite AI tools for the classroom below. You can scroll down and check them all out or jump to any category of tools by clicking on the icon below.
Note: Pricing is accurate as of our most recent update in November 2023.

# data[].attributes.attachment_link.description
Artificial intelligence is growing by leaps and bounds. It's no longer science fiction anymore! Lots of AI tools can serve as our own personal

# data[].attributes.headline
"Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task"

# data[].attributes.subject
"Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task"

# data[].attributes.attachment_link.title
Designing Civic Learning for AI Justice — Civics of Technology

# data[].attributes.attachment_link.description
Civics of Technology Announcements Next Tech Talk: The next Tech Talk will be held on September 2 at 8:00 Eastern Time. Register on our events page. Come join our community in an informal discussion about tech, education, the world, whatever is on your mind! Conference Recordings: Missed a s

# data[].attributes.attachment_link.description
Displaying AI Justice Project Toolkit. Page 1 of 31 Page 13 of 31

# data[].attributes.attachment_link.title
Research-Practice Webinar | Bugs, Bots, and Algorithmic Bias: Examining the Power and Peril of AI in Education

# data[].attributes.attachment_link.description
A research-backed and practice-relevant conversation about the power and peril of AI in education.

# data[].attributes.attachment_link.title
Ethical AI: The Skills and Knowledge Equity Leaders Need

# data[].attributes.attachment_link.description
The equity imperative surrounding artificial intelligence (AI) in education is both urgent and complex. This brief provides an overview of the essential considerations for educational leaders approaching AI integration with equity at the forefront.

# data[].attributes.attachment_link.title
AI Learning Priorities for All K-12 Students - Computer Science Teachers Association

# data[].attributes.attachment_link.description
AI Learning Prioritiesfor All K-12 Students AI Learning Prioritiesfor All K-12 Students Vision All students must learn about artificial intelligence…

# data[].attributes.attachment_link.readable_text
Introduction
Resources
Where is AI in the World of Today and Tomorrow?
Where Might AI Fit Within California Schools?
Fundamental Skills for Educators and Students
Utilizing AI in Schools
Developing AI in Schools
References

Introduction

Please note: This page is meant to provide helpful guidance to our partners in education and is, in no way, required to be followed. This page is intended to be informative rather than prescriptive. The information is merely exemplary, and compliance with any information or guidance on this page is not mandatory. (See Education Code § 33308.5.)

Emerging technologies often lead to new and exciting learning opportunities for students, particularly in increasing personalization and accessibility options. While Artificial Intelligence (AI) can be a valuable learning tool for educators and students, it must be evaluated according to usage terms, and clear guidelines for data collection should prioritize student safety.

The California Department of Education (CDE) considers human relationships crucial in education, particularly when incorporating generative AI tools such as ChatGPT into schools. This is particularly vital in light of school closures that occurred in the recent past due to the COVID19 pandemic and other natural disasters, that left many educators and students physically isolated. AI or any other technology cannot replace the value of a student's relationship with a caring educator who can connect on a human level.

In accordance with State Superintendent of Public Instruction Tony Thurmond's professional learning initiative, CDE is committed to supporting AI-focused professional learning for administrators and educators, to educate them about AI's benefits and limitations. The Professional Learning Initiative can keep educators up-to-date on emerging technology tools for the classroom. CDE's Computer Science Coordinator has participated in numerous AI panels across the state and has led CDE's STEAM (Science, Technology, Engineering, Arts, Mathematics) workgroup in introducing AI to CDE. The department hosted an AI panel discussion in May of 2023 entitled "Artificial Intelligence: Demystifying AI for CA Students," and the Computer Science (CS) Coordinator regularly collaborates with members of the Computer Science Teachers Association and CSforCA AI workgroup to remain current on educational trends in the field of AI and other emerging technologies in respect to their impacts on education.
CDE is preparing additional resources to support educators, including the implementation of an AI webinar series "Artificial Intelligence: Learning with AI, Learning about AI," which features educators from K12 and postsecondary spaces, as well as industry professionals, complete with actionable resources for schools with a learner centered focus. CDE is participating along with multiple organizations including Code.org, Educational Testing Services, International Society for Technology in Education, Khan Academy, and World Economic Forum, in the TeachAI project (teachai.org), to engage in conversations with a goal of empowering educators to teach with and about AI.

CDE encourages educators to approach AI with a lens of equity. As educators and students learn about potential inaccuracies, bias, and social impacts of AI, they build capacity to promote ethical use of technology. The department supports educators who adapt their teaching methods to incorporate emerging technologies effectively in line with student needs.

Resources

Information and resources related to AI in education are available on the California Educators Together Statewide AI group (Login Required)

Where is AI in the World of Today and Tomorrow?

AI is an integral part of our daily lives, from virtual assistants to recommendation algorithms that determine the content on social media feeds. At its core, artificial intelligence (AI) refers to computer systems designed to perform tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, making decisions, and learning from data. AI systems start by ingesting vast amounts of data. This data can be in various forms, such as text, images, or numbers. For instance, in a language translation application, the input could be a sentence in one language. Once the data is inputted, AI systems employ algorithms to process and analyze it. Algorithms are akin to sets of instructions that guide the AI in making sense of the data. These instructions can involve complex mathematical operations, data transformations, and statistical modeling. After processing the data, the AI system generates an output, which could be a translation of the input sentence into another language in the case of the language translation example. AI systems often have a learning component, improving their performance over time by learning from new data, a process known as machine learning.

In this age of AI, it is essential that both educators and students demystify this technology and grasp how it produces output. A conceptual knowledge of the benefits and potential risks of computing technologies is increasingly relevant for our students and educators alike. A growing knowledge about AI improves safety and efficiency when utilizing AI systems and supports understanding of potential inaccuracies and biases that may exist in their outputs.

While we cannot predict the future, it appears certain that technology will continue to influence our personal and professional lives. A report by the World Economic Forum in 2023 indicates that 1.1 billion jobs are likely to be radically transformed within the next decade. Skills the workplace of the future will call for include artificial intelligence and machine learning, cloud computing, product management, and social media.
According to the 2023 AI Index report by the Stanford Institute for Human Centered Artificial Intelligence, demand for AI related professionals is increasing in nearly every sector of the economy. As such, AI literacy is foundational for a well-rounded education to prepare students for today and tomorrow. California's fundamental AI skills for educators and students address both learning with and about AI, to promote efficient and ethical use of these emerging technologies in and outside of the classroom.

Where Might AI Fit Within California Schools?

As is the case with all technologies utilized in educational settings, student safety concerns underscore the importance of evaluating terms of use, data collection guidelines, and ethical use policies. Educators are encouraged to engage in professional learning regarding the capabilities and limitations of AI so that they may guide students in navigating a world in which AI permeates personal and professional lives. Professional learning can guide educators to supporting safe environments in which students learn with AI. Learning with AI can enhance academic learning in any content area, when implemented with safety and ethical use in mind. Educators and students learning with AI may have conversations regarding appropriate use of AI in and outside of the classroom, crafting ethical use guidelines together to ensure students build skills in leveraging the benefits of AI while maintaining academic integrity and maximizing learner responsibility.

Educational leaders are encouraged to provide access to computer science education for all K-12 students so that students learn about AI equitably. As educators and students demystify AI systems, as they see past the perceived "magic" of these technologies and deconstruct them to build a conceptual understanding of their inner workings, they are better able to engage as responsible, ethical citizens of emerging technologies.

In accordance with the concepts in the California computer science standards, a comprehensive computer science foundation for students includes conceptual understanding and skills of the five concept areas of the standards which include computing systems, networks and the internet, data and analysis, algorithms and programming, and impacts of computing. AI touches upon each of these concept areas. Computer science is referred to as a core subject in the state board adopted California computer science standards, designed for all K-12 students. In alignment with these K12 standards, as students gain the ability to navigate computing systems, utilize networks and the internet responsibly, analyze and interpret data with an antibias lens, solve problems using algorithms and programming, and carefully consider the ethics of computing technologies, they become informed digital citizens who can leverage AI and other technologies to promote social justice and equity. The importance of computer science is recognized at a national level, with Every Student Succeeds Act including computer science as part of a "well-rounded education" (2015). Computer science concepts permeate nearly every industry in our midst, with the California computer science standards emphasizing its interdisciplinary nature and relevance in our digitally connected society. The appendix of the California computer science standards provides example models for providing access for computer science education in K12 systems, which are to be based on local capacity and context rather than as a one size fits all approach.

Fundamental Skills for Educators and Students

Safe Use

Engage in Safe Use by Reviewing AI Data Collection, User Privacy

Data collection, in the context of AI, refers to the process of gathering and storing information from various sources, such as user interactions, sensor data, or online behavior. This data serves AI systems, enabling them to learn, adapt, and make informed decisions. Data collection for AI systems may encompass a wide range of information, including personal details, preferences, browsing history, and more. Educators and students are encouraged to build awareness of data collection activity of AI systems, in order to safeguard personal information and adhere to data privacy regulations.

User privacy refers to safeguarding individuals' personal information and ensuring that it is handled in a responsible and ethical manner. It entails protecting data from unauthorized access, breaches, and misuse. In the educational context, user privacy is of paramount importance to protect students' sensitive information and maintain trust within the learning environment. For example, while educators can leverage AI to support them in developing lesson plans, individual education plans, and analyzing assessment data, it is vital that they omit identifiable student information when inputting data to AI systems. It is important that educators and students review when and if data they input into an AI system is collected, and if that data will be stored to further its learning or be deleted.

Educational leaders are encouraged to build trust by fostering transparency. Trust is the cornerstone of safe AI use in education. When educators and students know that their data is handled with care and transparency, they are more likely to embrace AI technologies for learning and teaching. Compliance with privacy laws is not just a legal requirement but may be considered a moral obligation for educational institutions. Districts and educators must consider COPPA (Children's Online Privacy Protection Act) and FERPA (Family Educational Rights and Privacy Act) when considering AI use in the educational setting.

The integration of AI into California's education system presents immense opportunities for innovation, increased accessibility, and personalized learning. However, it also underscores the critical need for educators and students to engage in safe AI use by rigorously reviewing data collection and user privacy practices. By adhering to ethical principles and staying vigilant in safeguarding personal information, California's educational community can harness the power of AI while preserving individual privacy and security.

Pros and Cons

Leverage AI Capabilities, Critique Inaccuracies

A commitment to providing equitable teaching and learning environments for all students aligns perfectly with the potential of AI to help bridge the digital divide. By supporting educators and students in leveraging AI effectively and addressing inaccuracies, we can enhance the quality of education and ensure that every student, regardless of their circumstances, has access to a meaningful education that prepares them for the world of today and tomorrow.

The "Closing the Digital Divide Initiative," led by State Superintendent Thurmond and his Closing the Digital Divide Task Force, aims to eliminate disparities in access to computing devices and connectivity among California students. This initiative recognizes the profound impact that the digital divide has on educational equity. Educators and students who have the skills to leverage AI safely and efficiency will benefit from increased productivity. Conversely, communities who lack access to skill building experiences with AI at home may not have the opportunity to gain proficiency with this emerging technology if the skills are not also addressed in the school setting.

Educators and students are encouraged to leverage AI capabilities. AI can facilitate personalized learning experiences, enabling teachers to create custom-tailored lessons, and provide personalized support to students. AI can analyze student data to tailor lessons, adapt to individual learning styles, and provide targeted interventions, thereby addressing educational disparities. AI tools can increase access for marginalized populations. Examples include language translation support for multilingual students, voice typing for students with disabilities, and organizational tools for executive processing support. AI can expedite administrative tasks and automate workflow for both educators and students. This may allow educators to focus more on instruction and student support, freeing up time to build connections with students. AI can support students with after-hours tutoring/resource support to support academic learning. This support may be of value to students who do not have access to academic support outside of the classroom.

It is important that educators and students learn to critique AI inaccuracies, to support media literacy. AI systems may perpetuate biases or generate inaccurate results. AI systems may hallucinate information or provide incorrect citations. Educators can integrate lessons on AI ethics, bias detection, and fact-checking into the curriculum. As students are encouraged to question AI-generated information and engage in constructive discussions about AI's role in education, they build their skills in utilizing AI responsibly. Learning AI skills fosters digital literacy, a fundamental requirement for participating in an increasingly digital world. Educators have a unique opportunity to leverage AI capabilities and address inaccuracies within the "Closing the Digital Divide Initiative." By integrating AI skills into our educational approach, we not only bridge the digital use divide but also prepare our students for a future where AI is an integral part of their lives. By fostering critical thinking, ethical considerations, and technical proficiency, we empower our students to navigate the digital landscape with confidence. By embracing AI as an ally in this endeavor, we embark on a path toward a more inclusive and digitally proficient educational landscape.

Social Impacts

Consider Presence of Bias and Social Impacts of AI

The fundamental skill of recognizing the impacts of algorithmic bias and considering the social consequences of artificial intelligence (AI) fit within State Superintendent of Public Instruction Tony Thurmond's "Antibias Education Initiative." Algorithmic bias refers to the presence of unfair and discriminatory outcomes in machine learning algorithms and AI systems due to the data used to train them or the design choices made during their development. As educators and educational leaders, our commitment to confronting hate, bigotry, racism, and bias aligns seamlessly with the need to raise awareness about algorithmic bias and its far-reaching societal effects. By integrating these concepts, we can empower educators and students to become informed advocates in the fight against bias and discrimination.

Algorithmic bias in AI systems can perpetuate and amplify societal biases, leading to discriminatory outcomes, particularly affecting marginalized communities. Educators and educational leaders play a crucial role in raising awareness about algorithmic bias. By communicating its impacts, we equip students to recognize and address bias in AI systems, fostering critical thinking and empathy. Educators are encouraged to integrate discussions about algorithmic bias into existing curriculum, emphasizing real-world examples of bias in AI applications. Students can be guided to explore the ethical implications and societal consequences of algorithmic bias.

AI technologies have a profound impact on society, including their influence on employment, access to resources, and decision-making processes. Educators can incorporate lessons on the societal implications of AI into the curriculum and encourage students to analyze how AI affects various aspects of society, from healthcare to criminal justice, and explore strategies to mitigate bias and discrimination.

By understanding the social impacts of AI and algorithmic bias, students can become more empathetic and better equipped to advocate for fair and equitable AI systems. Equipped with knowledge about AI bias, students can make informed ethical decisions in a world increasingly shaped by AI technologies.

Incorporating the fundamental skill of communicating algorithmic bias impacts and considering the social consequences of AI in alignment with the "Antibias Education Initiative" presents a unique opportunity to empower educators and students to confront hate, bigotry, racism, and bias. AI education can be utilized as a powerful tool to help address bias and discrimination, creating a future where our students are not only informed but also engaged advocates for a more inclusive and tolerant society.

Demystify

Build Understanding of How AI Systems Work

The continued growth of artificial intelligence (AI) and its impact on various facets of society necessitates equipping our students with the knowledge and skills to engage with AI systems thoughtfully and responsibly. AI technologies are becoming pervasive across industries, making AI understanding a core competency. As AI continues to shape various industries, students who understand AI fundamentals will be better prepared for future careers. AI literacy is becoming a valuable skill across many fields. Building a foundational understanding of how AI systems work and produce output not only prepares students for future careers in computer science and technology-related fields, but it also ensures that all students enter the workforce as knowledgeable, resourceful consumers of AI. A strong conceptual understanding of AI empowers students to adapt and contribute to an evolving workforce.

Today's students are digital natives, but they must also be digitally literate to thrive in the AI era. Knowing how AI processes data and generates outputs enables students to think critically about the results AI systems provide. They can question and evaluate the information they receive and make informed decisions. This is of particular significance as students utilize AI in the classroom, to maintain academic integrity and promote ethical use of AI. Educators and students are urged to leverage AI to foster and not inhibit critical thinking. An understanding of the inner workings of AI supports learners in identifying inaccuracies, questioning AI outputs, and recognizing potential bias. Ethical considerations are paramount in AI development and usage. As educators and students build an understanding of how data collection feeds AI outputs, educators and students become ethical technology users and potential creators who prioritize fairness, accountability, and transparency.

Building AI understanding for all students, regardless of zip code, race, gender, or socioeconomic status, can help to diversify the STEM pipeline by breaking down barriers and biases in tech fields. AI can be a powerful tool for solving complex problems. Students with knowledge of AI can harness this technology to address real- world challenges and innovate. Ensuring that all students have access to AI education promotes inclusivity and reduces the risk of a digital divide.

Educators and students who build skills in understanding how AI systems work is essential for equipping them with the skills and knowledge needed to navigate the digital landscape responsibly, think critically, and thrive in a technology-driven future. It empowers them to become not just consumers but active participants and ethical stewards of AI technology.

Utilizing AI in Schools

What might it look like to utilize AI in California schools? Artificial intelligence technologies, tools, and applications can be utilized in the educational environment to enhance and improve various aspects of teaching and learning. This integration can take several forms and serves multiple purposes. Implementation may be customized to meet local context and needs of each school community.

Why?

Planning and Workflow Support

AI-powered tools can enhance productivity and empower educators to provide more personalized and effective learning experiences. By embracing AI, educators can optimize their workflows, save time, and elevate the quality of education they deliver. As the educational landscape evolves, educators are tasked with managing increasingly complex teaching environments. AI offers to meet this challenge by providing tools that streamline administrative tasks, enhance instructional planning, and support educators in adapting to diverse student needs. AI can automate routine administrative tasks such as data analysis, generating practice sets, unpacking standards, scheduling, content generation, and more. This can allow educators to allocate more time to teaching and supporting students. AI is not a replacement for educators but a powerful ally in the pursuit of educational excellence. By utilizing AI for planning, automation, and workflow support, educators can optimize their time and resources, ultimately improving the quality of education they provide. The integration of AI-driven tools empowers educators to address individual student needs, promote engagement, and make data-informed decisions.

Accessibility & Personalization

In alignment with the neuroscientifically backed principles of Universal Design for Learning, inclusive learning environments empower students to become expert learners, equipped with tools that increase accessibility and personalize the learning experience to meet their unique needs. Additionally, connecting content to students' lives furthers the personalization process and supports culturally responsive pedagogy. AI tools can support educators and students in the endeavor to tailor learning experiences to the students in their midst.

AI can generate personalized learning materials, such as worksheets, quizzes, and reading assignments, aligned with students' learning goals, strengths, and interests. AI analytics can provide educators with insights into student progress and areas that require additional support, allowing for educators to provide targeted interventions during small group instruction. AI-powered tools can enable text-to-speech and speech- to-text capabilities, ensuring that all students, including those with visual or hearing impairments, can access educational content. AI-driven closed captioning and transcription services can enhance accessibility by making multimedia content, such as videos and podcasts, accessible to individuals with hearing impairments. AI-driven language translation tools can break language barriers, making educational content accessible to multilingual learners. AI-powered assistive technologies, such as screen readers and voice recognition software, can empower students with disabilities to engage fully in the learning process.

College/Career Readiness

As students utilize AI responsibly in K12 schools, they gain skills that support thinking processes, organization of workflow, ethical technology use, and abilities to communicate effectively in a digitally connected world.

AI-driven tools encourage students to think critically and approach complex problems analytically, a skill highly valued in both higher education and the workforce. Students exposed to AI tools are adept at automating routine tasks and managing their time efficiently, which is invaluable for college success. As we teach students about AI ethics, we help to ensure they make responsible decisions in both personal and professional lives. Proficiency in AI technologies gives students a competitive edge in the job market and positions them as valuable assets in a technology-driven workforce. AI integration promotes digital literacy, enabling students to effectively navigate the digital landscape, a skill required in both academic and professional settings.

Address the Digital Use Divide

As technology becomes increasingly integral to education, educators and educational leaders must harness AI's power to ensure equitable access, enhance digital literacy, and prepare all students to thrive in an AI-driven world. The digital use divide, characterized by disparities in access to and effective use of technology, is a pressing concern in modern education. AI, when thoughtfully integrated, offers a powerful means to bridge this divide and promote equitable opportunities. Infusing AI into curricula enables students to develop digital literacy skills, empowering them to navigate technology effectively and responsibly. AI-powered language translation and accessibility tools can break down language and physical barriers, supporting more inclusive learning environments.

How?

Evaluate Terms of Use, Enact Data Collection Guidelines

As school districts consider the integration of AI systems into their educational environments, it is essential to thoroughly evaluate the terms of use to ensure the responsible and effective deployment of AI technology. Local education agencies may wish to consider the following steps as they evaluate safety of AI systems:

1. Data Privacy Compliance:

Legal Framework: Ensure that the AI system complies with federal and state data privacy laws, including the Family Educational Rights and Privacy Act (FERPA) and the Children's Online Privacy Protection Act (COPPA).

Data Handling: Review how the AI system collects, stores, and manages student data. Ensure it adheres to privacy protocols and encryption standards.

Data Ownership: Clarify who owns the data generated or processed by the AI system and establish rights and responsibilities regarding data access and sharing.

2. Security Measures:

Data Security: Assess the AI system's security measures, including encryption, authentication, and authorization protocols, to safeguard against data breaches and cyber threats.

Access Control: Verify that the AI system has robust access controls to restrict data access to authorized personnel only.

3. Transparency:
Algorithm Transparency: Seek clarity on how the AI system's algorithms make decisions and whether the vendor can provide insights into the system's decision-making processes.

4. Data Retention:
Data Retention: Determine how long the AI system retains student data and whether it aligns with your district's data retention policies.

5. Accessibility and Inclusivity:
Universal Design: Confirm that the AI system adheres to universal design principles, making it accessible to all students, including those with disabilities.

6. Educational Partner Involvement:
Community Engagement: Involve parents, students, educators, and administrators in the evaluation process to gather their input and address their concerns.

7. Continuous Monitoring:
Ongoing Assessment: Establish procedures for ongoing monitoring and evaluation of the AI system's compliance with the terms of use and data privacy standards.

Ethical Use Policies

In today's technology-driven world, students are exposed to digital tools and platforms from a young age. It is important to provide them with ethical use guidelines that promote responsible, respectful, and safe online behavior. Involving students in the creation of these guidelines not only ensures their relevance but also empowers them to be active participants in shaping their digital learning environment. This is true of all technologies and is of particular need as emerging technologies such as AI become more prevalent in society.

The following steps may be helpful in developing ethical use policies for AI use with students. It may be beneficial to involve students in the process of creating ethical use guidelines from the beginning. Their input is invaluable in shaping rules that resonate with their experiences. Provide students with educational resources and discussions on digital ethics, covering topics like online privacy, cyberbullying, plagiarism, and responsible sharing. Facilitate brainstorming sessions or focus groups with students to collect their thoughts, concerns, and ideas regarding online behavior. Work with students to draft the ethical use guidelines collaboratively. Encourage them to express their views and concerns and guide them in turning those ideas into actionable rules.
Integrate real-world scenarios and case studies into discussions to help students apply ethical principles to practical situations. Encourage students to review and provide feedback on the drafted guidelines. Peer review fosters a sense of ownership and accountability. Launch the guidelines formally, communicate them to all educational partners, and provide training or workshops to help students understand and embrace them. Establish a process for continuous review and updates to keep the guidelines relevant and responsive to evolving digital challenges.

Creating ethical use guidelines for students, with a focus on valuing their voices, is a powerful way to empower them to navigate the digital world responsibly and ethically. These guidelines can provide a framework for fostering a positive digital learning environment, where students can thrive academically and personally while upholding principles of respect, responsibility, and safety. By involving students in this process, we not only teach them valuable life skills but also instill a sense of ownership and shared responsibility for their digital community.

Support Mental Health: Emphasize Human Connection

While AI offers numerous benefits, it is vital to ensure that the technology enhances rather than replaces the human touch in education, promoting student well-being and emotional development. The introduction of AI into K-12 education has the potential to revolutionize learning experiences. However, it is essential to strike a balance between technological advancement and the preservation of essential human elements, including human connection and mental health support.

Encourage educators to maintain strong, personal relationships with students despite the integration of AI. AI should complement, not replace, these connections. AI can be used to facilitate peer collaboration, allowing students to work together, discuss, and learn from one another, fostering social connections.

As educational communities integrate AI, they are encouraged to ensure that it intersects with the spirit of State Superintendent of Public Instruction Tony Thurmond's Mental Health Support Initiative. It is important to educate students about the role of AI data collection in shaping their online experiences. For example, biased data collection can perpetuate stereotypes and discrimination. When data collected reflects biases, it can lead to unfair treatment or exclusion of certain groups. For students, this can result in feelings of marginalization and can negatively affect their mental health. Further mental health challenges may arise outside of the school setting as students indicate preferences on social media platforms and streaming services. The AI algorithms behind these platforms may lead individuals to narrow views. This can hinder constructive dialogue and understanding between students, leading to feelings of isolation and frustration, both of which can negatively impact mental health. Building healthy relationships and connections with peers is fundamental to mental health. If AI algorithms steer students toward divisive content, it can impede their ability to engage in open, empathetic conversations and form diverse social connections. Therefore, conversations regarding the impacts of AI on mental health are paramount for students and educators to navigate the digital world. The integration of AI into K-12 education offers immense potential for enhancing learning experiences. However, it is imperative to prioritize human connection and consider mental health implications as we adopt these technologies. By emphasizing strong teacher-student relationships, promoting peer collaboration, and incorporating social-emotional learning, educators can create a nurturing environment that fosters both academic and emotional growth.

Developing AI in Schools

What does it mean for K-12 students to develop AI in schools? This means students actively engage in the creation, programming, and understanding of artificial intelligence within their educational environment. This concept goes beyond merely using AI tools or applications; it involves students becoming creators and innovators in the field of AI.

Students are encouraged to not only learn with AI but also to learn about AI through hands-on exploration. This opportunity can be provided to students through engagement with computer science concept areas, each of which can align with AI. The national computer science framework, which provided a foundation to guide the development of California's computer science standards, indicates that in early childhood education, "computer science supports play-based pedagogy, extends what educators are already doing in their classrooms, and guides young learners to notice, name, and recognize how computing shapes their world." This concept extends to developing AI in schools as well. Students of all ages may explore data collection via unplugged activities, experience concepts of algorithm design by experimenting with AI prompts and discuss the impacts of AI on society. The student activities align to the California computer science concepts of data and analysis, algorithms and programming, and impacts of computing. Resources for educators to embed AI lessons into their classrooms is available on the California Educators Together statewide AI group (Login Required) . Flexible implementation ideas for providing access to computer science education in K12 systems are included in the California computer science standards appendix and may be customized according to local context and community needs. Developing AI in K-12 schools is about empowering students to become active participants in the technology of today and tomorrow. It equips them with the knowledge, skills, and ethical understanding needed to contribute to the responsible and innovative development of artificial intelligence, both during their educational journey and in their future endeavors.

Why?

Expand Access for Traditionally Underrepresented Groups

Developing artificial intelligence (AI) in K-12 schools, which may occur through computer science pathways, may act as a powerful means to address systemic bias and expand access for traditionally marginalized groups in computer science, technology, and STEM (science, technology, engineering, mathematics) fields. By integrating AI education with a focus on diversity and inclusion, we can pave the way for a more equitable future in these disciplines. A lack of diversity and representation in computer science, technology, and STEM fields has long been a concern. In April of 2021, Pew Research Center indicated that LatinX and Black workers are underrepresented in the STEM workforce, while White and Asian workers are overrepresented. Women were reported to make up a mere quarter or less of all computer and engineering jobs. To address this issue, it is crucial to start early in the educational pipeline, which makes K-12 schools an ideal setting for nurturing diversity, inclusion, and equal access.

Computer Science is akin to a gateway to STEM for the post-secondary level, particularly for underrepresented populations. College Board reports that in 2019, the Advanced Placement (AP) Computer Science Principles course was a first AP course for 68% of Black students, 59% of LatinX students, and 60% of first-generation college students. Furthermore, students who take AP computer science principles are more likely to major in computer science or another STEM major in contrast with similar students who did not take the course. The likelihood is further increased for LatinX and women.

AI education in K-12 schools introduces students from all backgrounds to the world of computer science and AI, providing early exposure and demystifying these fields. The introduction of California's computer science standards highlights the importance of inclusionary computer science opportunities:

"The standards are designed for each and every student, including underserved populations: girls, low-income students, homeless students, rural students, African American and LatinX students, students who are English learners, students with disabilities, and foster youth. Students' access to and achievement in computer science must not be predictable on the basis of race, ethnicity, gender, socioeconomic status, language, religion, sexual orientation, cultural affiliation, or special needs."

As educators model diversity in computer science opportunities and emphasize the importance of recognizing and mitigating bias in AI systems, they promote fairness and teach students about ethical AI development.

Empower Learners to Design Systems to Solve Challenges

By fostering student voice, motivation, and problem-solving skills within AI education, we can equip students to become active contributors to technologies of today and tomorrow. As we incorporate AI education in K-12 schools in a way that provides opportunity for students to not only understand AI but to actively engage with it, we demystify AI, promote critical thinking, and instill motivation to design AI systems that tackle meaningful problems. As K12 systems empower students to see AI as a tool they can wield to solve problems, this challenges the perception that AI is the realm of experts. As student voice is welcomed in AI education, this fosters a sense of ownership over learning experiences. This allows students to infuse creativity into AI projects, exploring innovative solutions to real-world issues. As students voice their concerns and interests, education in developing AI can help them to propose technological solutions to relevant challenges in their lives. Engaging with AI in meaningful ways taps into students' intrinsic motivation, as they see the direct impact of their work on solving authentic problems. By embracing student voice, promoting problem-solving, and providing opportunities for hands-on learning, we can equip the next generation with the skills and confidence needed to shape the future of AI for the better. This approach not only enriches their educational experience but also prepares them to tackle complex challenges in a rapidly evolving technological landscape.

How?

Integrate AI Skills & California Computer Science Standards into Curriculum

Incorporating AI skills and computer science standards into K-12 education can equip students with the knowledge and abilities necessary to navigate and contribute to an increasingly AI-powered world. This integration fosters computational thinking, problem- solving, and innovation, paving the way for students to become AI developers and creators. Infusing AI skills into the curriculum ensures that students are prepared to engage with and contribute to emerging technologies that are reshaping industries and society. California's computer science standards provide a comprehensive foundation that includes essential concepts such as algorithms, data analysis, and programming- key components of AI development. The computer science standards emphasize interdisciplinary learning, connecting AI with various subjects and applications, from healthcare to engineering to agriculture to art and more.

Infusing AI skills and computer science standards into K-12 education aligns curriculum with the needs of the modern world, fosters critical thinking and creativity, and prepares students to become active contributors to the AI revolution. As we provide educators with the professional learning and resources needed to integrate AI, we provide all students with the opportunity to gain the skills and knowledge required to navigate and shape the AI-driven future. This approach not only enriches educational experiences but also empowers students to innovate and create in an increasingly AI-powered world.

Integrate 5 Big Ideas of AI

The field of artificial intelligence (AI) is rapidly evolving, and its impact on society is profound. To ensure that K-12 students are well-prepared to navigate this AI-powered world, the 5 Big Ideas of AI can be integrated into the curriculum to meet students' developmental levels. These ideas serve as a foundational framework that aligns with educational goals and enhances students' cognitive, computational, and ethical skills.
The 5 Big Ideas of AI:

Perception: Understanding how AI systems perceive the world is fundamental. This includes image and speech recognition, natural language understanding, and sensory data processing. Integrating this idea into education enables students to comprehend how AI systems interact with the environment.

Representation: AI relies on data and information representation. Teaching students how data is structured and organized empowers them to work with AI models and make informed decisions about data usage and manipulation.

Reasoning: AI systems use reasoning to make decisions and solve problems. Integrating this idea helps students develop critical thinking skills, algorithmic reasoning, and the ability to assess the logic behind AI decisions.

Learning: Machine learning is at the core of AI. Teaching students about machine learning algorithms, training models, and the concept of learning from data prepares them to understand the AI systems that surround them.

Societal Impact: Recognizing the societal impact of AI, including ethical considerations and bias, is essential. This idea encourages students to engage in discussions about AI's role in society and its ethical implications.

Integrating the 5 Big Ideas of AI into K-12 education is a forward-looking strategy that supports educational goals by fostering critical thinking, creativity, problem-solving, ethical awareness, and career readiness. This approach not only equips students with AI knowledge but also prepares them to be informed and responsible citizens in an increasingly AI-driven world. The 5 Big Ideas of AI provide a solid foundation for AI education that aligns with the broader educational mission of preparing students for success in college/career for today and into the future.

References

AI for K12 Initiative is a joint project of the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA), funded by National Science Foundation Award DRL-1846073

California Department of Education. (2018). Computer Science Standards for California Public School Kindergarten through Grade Twelve (PDF) .

CAST (2018). Universal Design for Learning Guidelines version 2.2 .

Every Student Succeeds Act of 2015, Pub. L. No. 114-95. 20 U.S.C.A. 6301 (2016). K-12 Computer Science Framework . (2016).

Jeff Wyatt, Jing Feng, and Maureen Ewing, "AP Computer Science Principles and the STEM and Computer Science Pipelines", College Board, 2020.

Nestor Maslej, Loredana Fattorini, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Helen Ngo, Juan Carlos Niebles, Vanessa Parli, Yoav Shoham, Russell Wald, Jack Clark, and Raymond Perrault, "The AI Index 2023 Annual Report," AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2023.

Pew Research Center, April, 2021, "STEM Jobs See Uneven Progress in Increasing Gender, Racial and Ethnic Diversity.

World Economic Forum. (2023). Future of Jobs 2023 Insight Report May 2023. Geneva.

Return to Top
Questions: Teacher and Leader Policy Office | CSed@cde.ca.gov | 916-445-7331

Last Reviewed: Wednesday, September 20, 2023

# data[].attributes.attachment_link.description
Information regarding the role of artificial intelligence (AI) in California K12 education.

# data[].attributes.attachment_link.title
California Department of Education’s (CDE) AI Webinar Series

# data[].attributes.attachment_link.description
California Department of Education’s AI Webinar Series: Learning With AI, Learning About AI. Webinar series is designed to support educators in implementing…

# data[].attributes.attachment_link.title
Episode 33: Critical AI Literacy and the Future of Education

# data[].attributes.attachment_link.title
The hidden cost of AI: Unpacking its energy and water footprint

# data[].attributes.attachment_link.description
The OECD and IEEE co-organised an event on the margin of the French AI Action Summit to discuss AI’s growing environmental challenges.

# data[].attributes.attachment_link.title
From Clouds to Ground: Rethinking Data for AI Through Planetary Justice — AI + Planetary Justice Alliance

# data[].attributes.attachment_link.description
In this reflection from the Data for Public Goals event at Politecnico di Milano, we call for a radical reframing of how we understand and govern data for AI. Moving beyond technical metrics and ethical tweaks, the piece introduces a planetary justice lens that traces AI’s impacts across its full

# data[].attributes.attachment_link.description
It’s been over 12 months since the first blog post about the AI Assessment Scale, and a lot has changed, both with the technology and with our understandings of how it impacts assessments in …

# data[].attributes.attachment_link.authors[].name
Cade Metz, Karen Weise, Marco Hernandez, Mike Isaac and Anjali Singhvi

# data[].attributes.attachment_link.title
How A.I. Is Changing the Way the World Builds Computers

# data[].attributes.attachment_link.description
Tech companies are revamping computing — from how tiny chips are built to the way they are arranged, cooled and powered — in the race to build artificial intelligence that recreates the human brain.

# data[].attributes.attachment_link.title
Critical AI Literacy: Empowering Youth for Education Justice (21CSLA at CISC 2025)

# data[].attributes.body
What are the tools, platforms, or promises that you have been thinking about, informed or influenced by?

# data[].attributes.attachment_link.title
Future schools and the energy implications of AI in education: A review of scenarios and method for engaging young people in futures thinking - Fareed Kaviani, Neil Selwyn, Yolande Strengers, Kari Dahlgren, Bronwyn Cumbo, Markus Wagner, 2025

# data[].attributes.attachment_link.description
‘School of the future’ scenarios remain a popular means of animating policy, industry and public debates around issues relating to technological, economic, soci…

# data[].attributes.attachment_link.title
Navigating AI in K-12 Education with a Critical Lens — Civics of Technology

# data[].attributes.attachment_link.description
Civics of Tech Announcements Monthly Tech Talk on Tuesday, 12/03/24 . Join our monthly tech talks to discuss current events, articles, books, podcast, or whatever we choose related to technology and education. There is no agenda or schedule. Our next Tech Talk will be on Tuesday, December 3rd,

# data[].attributes.attachment_link.readable_text
School districts need a clear strategy for AI. We're here to help.

Rapid advancements in AI bring both opportunities and challenges for school districts. Without a clear, structured plan, AI integration can become fragmented, inequitable, and even misaligned with your district's priorities.

This step-by-step toolkit can help you ensure that AI use is aligned with your district's unique mission, vision, and goals. Designed to be flexible and adaptable for districts of different sizes, and with different needs and priorities, our toolkit emphasizes direct support for implementation-not just planning-at each stage.

Just getting started? Before diving into the full toolkit, use our Getting Started guide first to assess your district's AI readiness and identify practical next steps based on what stage you're in. To find the full suite of resources, go directly to the toolkit itself:

# data[].attributes.attachment_link.title
AI Toolkit for School Districts | Common Sense Education

# data[].attributes.attachment_link.description
Common Sense Education provides educators and students with the resources they need to harness the power of technology for learning and life. Find a free K-12 Digital Citizenship curriculum, reviews of popular EdTech apps, and resources for protecting student privacy.

# data[].attributes.attachment_link.title
About a quarter of U.S. teens have used ChatGPT for schoolwork – double the share in 2023

# data[].attributes.attachment_link.description
Teens are far more likely to say it’s acceptable to use ChatGPT for research (54%) than for math problems (29%) and essays (18%).

# data[].attributes.attachment_link.title
Student, teacher AI use continued to climb in 2023-24 school year

# data[].attributes.attachment_link.description
Some 39% of teachers reported regularly using detection tech to spot AI-related plagiarism, according to the Center for Democracy & Technology.

# data[].attributes.attachment_link.title
Empowering Education Leaders: A Toolkit for Safe, Ethical, and Equitable AI Integration - Office of Educational Technology

# data[].attributes.attachment_link.description
This toolkit helps educational leaders ensure safe, ethical, and equitable use of AI in schools, districts, and education communities.

# data[].attributes.attachment_link.title
Research-Practice Webinar, December 12 | Possibilities and Problems of Generative AI as a Resource for Learning

# data[].attributes.attachment_link.description
Join us on Thursday, December 12 from 3:30–4:30 pm PT for a research-backed and practice-relevant conversation about what educators need to know about AI and leading for equity.

# data[].attributes.attachment_link.readable_text
Generative artificial intelligence (AI) has quickly become an integral part of the digital landscape, surfacing new ways for people to learn, create, and innovate. At the same time, it brings both proven and unknown risks to everything from privacy to equity and accuracy.

Young people are extremely important in considering the future of generative AI-they're not only early adopters and influencers, but will also be among the first to grapple with its consequences. Understanding the perspectives of young people when it comes to generative AI is paramount, especially considering the impact of digital technologies on youth well-being.

This study, conducted in partnership with and the Center for Digital Thriving at Harvard Graduate School of Education, examines how young people perceive and interact with generative AI technologies, with special attention to race and ethnicity, age, gender, and LGBTQ+ identity.

These nuanced views of teens and young adults from diverse demographic groups offer valuable insights into the potential benefits of generative AI, such as broader access to information, streamlining of tasks, and enhanced creativity. However, young people also expressed concerns about potential negative impacts, including job loss, privacy issues, intellectual property theft, misinformation and disinformation, and even AI taking over the world.

It's essential to understand young people's perspectives about generative AI, especially when considering programs, policies, and design features that impact the mental health of marginalized and minority populations like LGBTQ+, Black, and Latinx youth. The data in this report can ensure that the well-being of the earliest adopters is prioritized.

# data[].attributes.attachment_link.title
Teen and Young Adult Perspectives on Generative AI: Patterns of Use, Excitements, and Concerns

# data[].attributes.attachment_link.description
Young people regularly use generative AI, and believe in its potential to change the world. But they have concerns as well, and their perspectives are important in shaping the future of this transformative technology.

# data[].attributes.attachment_link.title
[Episode 6] The Potential of AI in Education with Chris Mah and Hillary Walker | Berkeley School of Education

# data[].attributes.attachment_link.description
Equity Leadership Now! hosts conversations with equity-conscious leaders from pre-K through university settings who transform structures and strategies for educating students, particularly for those from historically marginalized communities.

# data[].attributes.body
Since it's changing so fast, how do we learn about it critically use it and/or refuse it ethically and responsibly? Where are the guard rails?

# data[].attributes.attachment_link.title
Youth Perspectives on AI in the Classroom

# data[].attributes.attachment_link.description
NAMLE’s 2024 Youth Advisory Council created a resource for educators that shares their perspectives on AI in the classroom.

# data[].attributes.headline
Teaching Critical GenAI Literacy: Empowering Students for a Digital Democracy

# data[].attributes.subject
Teaching Critical GenAI Literacy: Empowering Students for a Digital Democracy

# data[].attributes.attachment_link.description
Welcome to interactive presentation, created with Publuu. Enjoy the reading!

# data[].attributes.attachment_link.title
Ethical AI for TK-12 Education Leaders Multimedia Text Set

# data[].attributes.attachment_link.title
Teaching Critical GenAI Literacy Empowering Students for a Digital Democracy article Elemen.pdf

# data[].attributes.attachment_link.title
Cultivate Critical Generative Artificial Intelligence (GenAI) Literacy + UDL.pdf

# data[].attributes.attachment_link.title
Teaching Critical GenAI Literacy_ Empowering Students for a Digital Democracy.wav

# data[].attributes.headline
Critical AI Literacy, Ethics and Justice

# data[].attributes.subject
Critical AI Literacy, Ethics and Justice

# data[].attributes.body
These lessons focus on critical artificial intelligence (AI) literacy, ethics and justice with environmental literacy, critical media literacy and digital citizenship.

# data[].attributes.attachment_link.description
The California Educators Together platform is brought to you by a collaboration between the California Department of Education and the CDE Foundation.

# data[].attributes.body
Generative AI is a subset of AI that focuses on creating something new. Instead of simply analyzing and learning from input data, generative AI can generate novel content, often from user-generated prompts and input requests. Outputs from these models can range from text to images and audio, including music and even realistic human speech. Generative AI operates on the foundations of machine learning and neural networks, two core concepts in the AI world.

# data[].attributes.attachment_link.readable_text
1. Why students should learn about Generative AI

2. Key Concepts of Generative AI

3. Teaching AI through ChatGPT-centered activities

4. Effective Use of Generative AI Tools

5. Integrating Digital Literacy Skills

As the school year gave way to summer, I found myself in a lively discussion with fellow educators about ChatGPT in our daily lives. The consensus? The robots are indeed here, but instead of a bleak, science fiction-inspired future, we envisioned our interactions with generative AI resembling one of Michael Scott's quirky, albeit misguided, ventures into the realm of computers on "The Office." After some robust laughter, our conversation took a serious turn-how do we bring this transformative technology to our students in the classroom?

How do we teach students to use ChatGPT and other generative AI tools effectively and responsibly?

One technology in the ever-evolving digital landscape has consistently made headlines and transformed industries: Artificial Intelligence (AI). A particular branch of AI, called generative AI, is revolutionizing how we interact with technology and has made us rethink the future of education. Experts are already mapping it out.

One remarkable example of generative AI is ChatGPT, a state-of-the-art generative language model developed by OpenAI. ChatGPT's public release after Thanksgiving of 2022 shook the world with a broad spectrum of curiosity and concern. As educators, we must equip our students with an understanding of this groundbreaking technology and not just about ChatGPT.

Preparing students for their futures, which AI and advanced technologies will undoubtedly shape, requires us to collectively embrace these evolving technologies to teach students how to use these tools ethically and effectively.

Understanding Generative AI and ChatGPT

Generative AI is a subset of AI that focuses on creating something new. Instead of simply analyzing and learning from input data, generative AI can generate novel content, often from user-generated prompts and input requests. Outputs from these models can range from text to images and audio, including music and even realistic human speech. Generative AI operates on the foundations of machine learning and neural networks, two core concepts in the AI world.

ChatGPT is a large language model that utilizes a sophisticated form of machine learning called deep learning. Trained on vast amounts of internet text, ChatGPT can produce human-like responses based on the inputs it receives. "GPT" in ChatGPT stands for "Generative Pre-training Transformer":

Generative refers to the model's ability to create or generate contextually relevant data.

Pre-training means the model's trained on a large dataset (mostly text) to understand human language.

Transformer references the type of computer neural network used to generate the human-like speech output.

Students need to understand this rudimentary element of what GPT stands for to use and effectively appreciate ChatGPT's capabilities and limitations.

The goal of ChatGPT is not simply to mimic human conversation but to provide a useful and meaningful interactive tool that can assist in a wide range of tasks. Many educators and students are already finding uses -from lesson plans and generating questions to writing, editing, and basic coding.

The Importance of Learning about Generative AI

The significance of generative AI, especially in the rise of large language models (LLM) like ChatGPT, cannot be understated. Generative AI tools are growing more ubiquitous and powerful, finding use in many sectors, including entertainment, healthcare, education, and business. Understanding generative AI is no longer just beneficial for computer scientists or tech professionals; it's becoming a vital skill for everyone as our future workforce will evolve with this technology.

For high school students, learning about generative AI and tools like ChatGPT can offer invaluable insights into the technology shaping their world. Moreover, as these students prepare to enter the workforce, a foundational understanding of AI can open doors to many career opportunities in the tech industry and those outside who will use AI to make their work more efficient.

By incorporating generative AI concepts into our curriculum, we are not just teaching students about technology-we are preparing them for a future where AI plays an increasingly integral part in everyday life. AI already does so much for us today with autocorrect, facial recognition, and producing social media feeds. This is why learning about generative AI, specifically models like ChatGPT, is critical for today's students.

Generative AI Lesson Plans: Core Concepts

Much like teaching students how to use a graphing calculator, a 3D printer, or even a graphic organizer, students need to understand how to operate these tools if they are to use them effectively. However, for AI, teachers should highlight the key foundational concepts of generative AI. A deeper understanding of these principles will allow students to appreciate the potential and inner workings of models like ChatGPT.

Below are several concepts and examples to guide student understanding:

Machine Learning (ML):

Machine Learning, a subset of AI, centers on teaching computers to learn from data without explicit programming. Students need to grasp a basic understanding of ML because it forms the backbone of generative AI.

One common example of ML is an email account spam filter or junk folder. The program learns from data-features of emails marked as "spam" or "not spam"-to predict and filter out future spam emails. Another example involves teams at institutions like MIT developing AI programs to learn video games like Super Smash Bros. to take on human challengers.

Neural Networks

Inspired by how the human brain functions, neural networks are a series of algorithms trained to process data similar to how the human brain interprets sensory inputs. Neural networks, particularly "deep" neural networks with many layers, are essential to generative AI models like ChatGPT.

One basic example is image recognition, like your phone's ability to recognize your face and that of others for tagging or security purposes. This ability comes from a neural network trained to identify faces and distinguish them by distinct traits accumulated from more and more photos. Another example includes photographing hand-written notes to convert into digital text formats. In this process, a neural network learns how to identify the distinct attributes of diverse handwriting.

Natural Language Processing (NLP)

NLP is the technology used to give computers the ability to understand, interpret, generate, and respond to data in a way that mirrors human language. Essentially, it's training artificial intelligence in the field of linguistics. It's central to language models like ChatGPT and is vital for students to understand how AI interacts in human-like ways.

By understanding these core concepts of AI, students will gain a more holistic view of generative AI and how they can utilize it. Moreover, these principles are crucial not only for potential careers in AI and tech but also for other fields that increasingly intersect with AI, such as healthcare, business, and even the arts. As educators, our role is to make these complex concepts accessible and engaging to empower the next generation with AI literacy.

Teaching Generative AI Through ChatGPT-centered Activities

To successfully engage students with generative AI and tools like ChatGPT, we must understand their functionality's key elements. One tactic involves a "hands-on" approach by blending the what and how-to of these tools-leading to practical and creative classroom activities, fostering hands-on learning experiences, and critical metacognitive activities.

Introducing ChatGPT to students offers a revolutionary way to engage them in learning. Unlike traditional search engines, which return a list of websites based on keywords, LLMs like ChatGPT are equipped to process and generate human-like text, making learning more interactive and individualized to students based on their input prompts.

Search engines excel in locating specific information across the internet, and so programs like Google and Bing are still essential for students' day-to-day learning. LLMs, however, stand out in their ability to maintain engaging, multi-turn dialogues. LLMs reference previously entered context, enabling real-time, meaningful conversations like speaking to a knowledgeable person. This level of interactivity can stimulate critical thinking in students as they probe complex topics and can allow students a more individualized learning experience.

Input-Prompt-Based Activity

ChatGPT generates responses based on an input prompt. Well-crafted prompts will help students become more proficient and efficient with the tool. At the moment, most experts agree on five key components of a well-structured prompt:

1. Defining the role or area of work the chat will focus on

2. Provide context or relevant background information

3. Clearly state the ask or task at hand

4. Establish limitations by describing what can and cannot be considered

5. Offer additional guidance or references

Consider creating an activity where students can experiment with different prompts using the structure above. Have students observe and evaluate the variety of responses generated by ChatGPT, especially as they play with other aspects of the prompt structure. This engineering activity can be ludic and collaborative in its design by asking students to be creative and critical of their development.

Pattern Recognition, not Conscious Understanding

Students should understand early in their exploration of generative AI tools that they operate based on recognizing and predicting patterns from data they've already been trained on. These tools do not possess understanding or consciousness.

Despite the human-like text it generates, generative AI is a tool and not a sentient being. It's improbable that programs like ChatGPT will be as creatively unique as human students.

One way to teach students about this concept is to organize a prediction-style activity. Students will quickly realize that ChatGPT operates based on recognizing and predicting patterns rather than a conscious understanding. Consider using the prompts they've already developed and have students adjust one parameter. Encourage students to make predictions based on their adjustments and then reflect on them.

No Memory or Continuity Activity

Unlike humans when engaged in conversations, ChatGPT doesn't hold a working memory. Instead, a previous context within a chat thread is prepended (attach data to the beginning of another) with each new prompt as you hit submit. This means the chat is limited to only so much context before entirely forgetting earlier content. Therefore, providing too much information will limit the response of ChatGPT, making it shorter and hyper-focused on the more recent prompt.
I have explored this activity numerous times to understand more about how to shape prompts and expectations. First, have students establish a goal:
"I will have ChatGPT accomplish this for me..."
Next, create a series of quick, easy-to-develop prompts to converse with ChatGPT. From there, continuously change aspects by adding and adjusting the context.

Prompt: You're a student learning about the history of artificial intelligence. Develop an expository essay describing the history of generative AI. Be sure the essay is under 500 words and is written in a language a middle school student can understand. Adjustment: You're a teacher researching the history of artificial intelligence. Develop a conference paper describing the history of generative AI and neural network construction. Be sure the essay is under 800 words and is written in a language other educators can understand.

As students introduce new prompts, have them identify when information is no longer included or relevant to their initial goals or when the response is not of the same quality as previous outputs. Think about the game Jenga-as you remove and stack a single block, the tower can grow incredibly high but will become more unstable.

Teaching Students About Effective Generative AI Use

The Power of Tuning

A further step towards helping students understand the working memory of programs like ChatGPT, as well as the accuracy and relevance of its generative outputs, depends on how students fine-tune their input prompts and interactions. Like editing and proofing writing, students should understand how tuning and revising can create better outputs or influence hallucinations or unexpected responses.

Hallucinations and Inaccurate Information

Arguably the most important aspect to teach students, ChatGPT is a tool for people to use and is, therefore, just as subject to human error. Many have learned about "hallucinations" and wild responses from the model, so teachers need to help students understand how generated responses could include plausible but factually inaccurate and nonsensical information.

ChatGPT cannot access real-time information unless you're using plug-ins or third-party apps. Instead, it generates text based on patterns learned from the broad dataset it was trained on. For ChatGPT3.5, the current version available for free, that dataset cuts off in June of 2021. If asked about a specific event, even one that occurred before the training data was collected, or asked to provide personal experiences, it could "hallucinate" a response that seems plausible but is entirely untrue or inaccurate.

Integrating the Four Cs of Digital Literacy with Generative AI

In the digital age, the Four C's of Digital Literacy-Critical Thinking, Creativity, Communication, and Collaboration-are essential literacy skills for students to succeed in the future.

We've identified these skills as valuable competencies in two of our XQ Learner Outcomes- Original Thinkers of an Uncertain Future and Generous Collaborators for Tough Problems. Our XQ Learner Outcomes are a series of research-backed skills and outcomes that outline what students should know to be successful in college, career, and whatever the future may hold.

Here's how these skills connect to AI literacy, particularly when using generative AI in the classroom:

Critical Thinking-Teaching students to analyze and evaluate AI-generated information enhances critical thinking skills. Have students discern between reliable and unreliable AI outputs, an increasingly important skill set as AI becomes more pervasive in society.

Creativity-With AI like ChatGPT, students can leverage their creativity in new ways. Whether generating novel content, designing unique AI interactions, or problem-solving with AI, integrating AI in classrooms inspires a new dimension of creativity.

Communication-Effectively expressing one's thoughts is crucial in the digital age. Students can focus on their communication skills when interacting with AI as they practice drafting and editing. Combined with metacognitive activities, students can reflect on how the chatbot, and other models, read and respond to their writing.

Using AI in the classroom helps boost digital literacy and prepares students for a future where integration will increase in daily life. This digital empowerment fosters key learning outcomes to help students become active contributors in the evolving digital landscape.

Teaching Students About the Ethics & Safety of AI

AI businesses and industries have outlined and implemented various safety measures like content filters to limit inappropriate or harmful output. But the system isn't perfect, and students should be aware.

Concerns with ChatGPT and Student Plagiarism

Plagiarism is often a paramount concern for educators, and this concern persists regardless of the use of generative AI. It's crucial to remind students that plagiarism is unacceptable, including content produced by AI. Typically, plagiarism can indicate deeper issues students grapple with, such as self-doubt or challenges in executive functioning.

Rather than viewing AI as a source of content, we should position it as a facilitator for learning activities. Generative AI can serve as an excellent tool for brainstorming and revising work, aiding in conceptualizing and refining ideas. Additionally, it's important to review and emphasize the correct method of citing materials, including those generated by AI programs. This ensures academic integrity and helps students responsibly maximize AI's educational potential.

While teaching students about LLMs like ChatGPT, it's equally important to guide them through AI tools' ethical and safety aspects. Data, privacy, and ethics are larger conversations beyond just one subject. But below are several approaches specific to generative AI:

Data Privacy-As LLMs generate text based on input, students need to understand why it's important not to provide any sensitive personal information while interacting with these models. Understanding the broader principle of data privacy is vital for all students and educators in our digital age.

AI and Bias-Teach students about the potential for AI systems to reflect and even amplify societal biases present in the training data, especially given that many of these models are trained on current information published online. Encourage students to critically evaluate AI-generated content for any potential bias.

Responsible AI Use-Students should be encouraged to use AI responsibly, understanding that AI tools can be misused to generate inappropriate, harmful, or misleading content. Guide students towards ethical practices while using AI; refrain from using AI to deceive others through plagiarism, spreading false information, or generating content to use against or harm another individual.

Content Filtering and Safety Measures-Lastly, students should know about the safety measures implemented by organizations such as OpenAI, including content filtering, to limit the generation of inappropriate content. However, consider how you can include students in identifying how to create a safe and inclusive learning environment when using generative AI.

Making ethics and safety a central part of AI education prepares students to use AI tools effectively and navigate the evolving digital landscape responsibly and conscientiously.

By integrating the understanding of these elements into hands-on activities, we can help students comprehend how to use ChatGPT effectively and grasp the broader principles of AI. These valuable skills will prepare students for the future, fostering an in-depth understanding of technology, promoting creativity, and encouraging critical thinking about the benefits and challenges of AI.

AI businesses and industries have outlined and implemented various safety measures like content filters to limit inappropriate or harmful output. But the system isn't perfect, and students should be aware.

Conclusion: Where Educators Can Go with Generative AI

As we forge ahead in this AI revolution, how we integrate artificial intelligence and generative AI in classrooms is not just a possibility-it's an inevitability. Using generative AI in the classroom, such as tools like ChatGPT, will empower students with a powerful and interactive learning experience. Students can become proficient and responsible AI users by understanding core concepts of generative AI and recognizing large language models' capabilities and limitations through hands-on activities.

Ensuring students grasp critical concepts such as AI hallucinations, data privacy, and AI's potential for bias is essential to fostering AI literacy. This ties directly to the Four C's of Digital Literacy-Critical Thinking, Creativity, Communication, and Collaboration-equipping students with the skills necessary to navigate and contribute to an AI-driven world.

As educators, we need to prepare our students for this future. We must equip them with the knowledge to use generative AI tools like ChatGPT effectively and understand the ethical and safety considerations surrounding them. The goal is to create a learning environment where students participate actively in the digital landscape and are not just passive consumers.
Photo at top by: Chris Chandler
Stay Updated: If you are interested in learning more about how to integrate generative AI into your classroom further, join us in our mission to rethink high school education. Subscribe to our XQ Xtra Newsletter and mailing list, where you'll get the latest news, ideas, and stories straight from the forefront of educational innovation.

# data[].attributes.attachment_link.title
Incorporating Generative AI in High School Classrooms: A Deep Dive into Using ChatGPT and Similar Tools - XQ

# data[].attributes.attachment_link.description
Explore how to integrate generative AI tools like ChatGPT in high school classrooms to foster digital literacy and prepare students for the future.

# data[].attributes.attachment_link.title
Critical AI Literacy, Ethics and Justice — National AI Literacy Day

# data[].attributes.attachment_link.description
These lessons focus on critical artificial intelligence (AI) literacy, ethics and justice with environmental literacy, critical media literacy and digital citizenship. Free with registration, requires account to view lesson.

# data[].attributes.headline
The Race, Abolition, and AI Program: Empowering Young People to Navigate the Intersection of Race and Technology

# data[].attributes.subject
The Race, Abolition, and AI Program: Empowering Young People to Navigate the Intersection of Race and Technology

# data[].attributes.body
by Colin Angevine

# data[].attributes.attachment_link.provider_name
The Connected Wellbeing Initiative - Youth powered wellbeing in a connected world

# data[].attributes.attachment_link.title
The Race Abolition and AI Program - The Connected Wellbeing Initiative

# data[].attributes.attachment_link.description
The Race, Abolition, and AI Program: Empowering Young People to Navigate the Intersection of Race and Technology by Colin Angevine A Case Study from the Connected Wellbeing Impact Studio “It’s just nice to feel heard and seen for once — being treated like your knowledge matters.” - Race, Abolition, and AI Program youth participant On the

# data[].attributes.attachment_link.readable_text
Shreya Sampath and Marisa Syed are with Encode Justice, a student group partnering with ACLU-NJ to explore the impact of school surveillance on New Jersey students.
Once while taking an exam, one of us asked our teacher how she could see everything we did on our computer screens. She said that the school's IT department had access to everything we did on our computer - not just during the test, but even when we're not in school. That did not sit well with us. The ominous feeling that we were being watched has lingered, a feeling that resonates with students around New Jersey and across the country. It made us realize that as public school students, we are subject to mass surveillance enabled by unvetted advanced technologies every day.

As two of the student leaders of the New Jersey chapter of Encode Justice, a global youth-led coalition advocating for human-centered artificial intelligence, our mission is to explore the vacuum of knowledge surrounding just how far this kind of surveillance goes. In partnership with the ACLU of New Jersey, we have been researching the alarming amount of surveillance that students are subjected to by schools and police. Our research has revealed that schools in New Jersey:

Spend tens of thousands of dollars on tools that scan everything a student writes, communicates, or searches to flag "concerning" content using artificial intelligence. In some school districts, that information could be shared directly with the police.

Spend millions of dollars on surveillance cameras with facial recognition and other AI-enabled capabilities, with no proof that these expensive tools make us safer.

​​​Spend thousands of dollars on tools to monitor everything students do on social media and might punish students for what they post, even when those posts have nothing to do with school.

It is clear from our research that school monitoring software does not serve the interests of all students. Instead, the practice of student digital monitoring desensitizes youth to the surveillance state, disproportionately impacting the lives of lower-income, minority, and LGBTQ+ students.

As members of Gen Z ​​​​in the age of social media companies, facial recognition cameras, and monitoring tools, make us - and many others our age - desensitized to the idea of mass surveillance. Though school surveillance has existed in some form for decades, the explosion of AI-powered surveillance followed an increase in the distribution of school-sponsored laptops and the move to remote learning. Administrators began monitoring the actions and speech of students using algorithms and technology. The "alleged" purpose of this technology is to protect students from unsuitable content, assure that students do not misuse resources, and flag activity that denotes issues related to mental health, substance use, and harassment that need immediate attention.

But studies have shown that six in ten students report they do not feel comfortable expressing their true thoughts and feelings online when their activity is being monitored - a clear display of the harmful chilling effect of school surveillance that deters students from exercising their rights and freedoms. In a school context, this means students are scared to research contentious issues for assignments, express their opinions, and communicate their feelings using school-sponsored computers.

And for students who are unaware of the extent of the surveillance they are being subjected to, they might share sensitive information on their computers without knowing who may see it. Students commonly explore sensitive topics like mental health or sexuality online, but these surveillance technologies put that kind of personal information at risk without their consent. In a country turning more hostile to the LGBTQ+ community every day, "outing" students and exposing them to discrimination.

Low-income and minority students are particularly vulnerable to this heightened surveillance. At one of our high schools, low-income students make up 60% of the population. For many, a school-sponsored computer is their only avenue to participate in the many parts of our lives that happen online. In certain cases, when an algorithm flags that online activity, it might contact the police directly before notifying parents or school administrators, subjecting those students and their families to unnecessary policing. These systems subject low-income students to over-policing or push them away from online pursuits for fear of being criminalized.

The companies that make surveillance tools often market their technologies as a response to the student mental health crisis, encouraging schools to use them in substitution of mental health infrastructure. But by relying on algorithms and content moderators detached from student experiences, these technologies dehumanize students. We found that the North Brunswick school district spent over $58,000 for just one year of a software tool that monitors student communications with AI. That money could instead go to real mental health resources.

Ultimately, students care about privacy, too. We care that our data is collected and commodified by anonymous third parties. We care that many of our peers, censor themselves in learning environments whose purpose is to encourage exploration and free thought. We care that the implications of surveillance are wide-reaching and more severe for lower-income minority communities. As AI-powered surveillance continues infiltrating new environments, we must regulate its uses. Unchecked AI leads to perpetuating - and worsening -already existing discrimination. Together, we must encourage school districts to make evidence-based decisions that center the needs of students. Heightened surveillance is not the solution.

# data[].attributes.attachment_link.title
As Students, We Face Invasive AI-powered School Surveillance. Now We're Calling on Lawmakers to Regulate it.

# data[].attributes.attachment_link.description
Students care about their privacy but are facing invasive levels of AI-powered surveillance in schools, perpetuating existing discrimination. NJ students are calling on lawmakers to implement regulations on AI-powered surveillance and protect student's right to privacy.

# data[].attributes.attachment_link.description
ISTE offers free resources and guides for educators who are interested in learning about how artificial intelligence can empower learners at every level.

# data[].attributes.body
Machine learning is a subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed. Machine learning is behind chatbots and predictive text, language translation apps, the shows Netflix suggests to you, and how your social media feeds are presented. It powers autonomous vehicles and machines that can diagnose medical conditions based on images.

# data[].attributes.attachment_link.readable_text
Machine learning is behind chatbots and predictive text, language translation apps, the shows Netflix suggests to you, and how your social media feeds are presented. It powers autonomous vehicles and machines that can diagnose medical conditions based on images.

When companies today deploy artificial intelligence programs, they are most likely using machine learning - so much so that the terms are often used interchangeably, and sometimes ambiguously. Machine learning is a subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed.

"In just the last five or 10 years, machine learning has become a critical way, arguably the most important way, most parts of AI are done," said MIT Sloan professor Thomas W. Malone, the founding director of the MIT Center for Collective Intelligence. "So that's why some people use the terms AI and machine learning almost as synonymous ... most of the current advances in AI have involved machine learning."

With the growing ubiquity of machine learning, everyone in business is likely to encounter it and will need some working knowledge about this field. A 2020 Deloitte survey found that 67% of companies are using machine learning, and 97% are using or planning to use it in the next year.

From manufacturing to retail and banking to bakeries, even legacy companies are using machine learning to unlock new value or boost efficiency. "Machine learning is changing, or will change, every industry, and leaders need to understand the basic principles, the potential, and the limitations," said MIT computer science professor Aleksander Madry, director of the MIT Center for Deployable Machine Learning.

While not everyone needs to know the technical details, they should understand what the technology does and what it can and cannot do, Madry added. "I don't think anyone can afford not to be aware of what's happening."

That includes being aware of the social, societal, and ethical implications of machine learning. "It's important to engage and begin to understand these tools, and then think about how you're going to use them well. We have to use these [tools] for the good of everybody," said Dr. Joan LaRovere, MBA '16, a pediatric cardiac intensive care physician and co-founder of the nonprofit The Virtue Foundation. "AI has so much potential to do good, and we need to really keep that in our lenses as we're thinking about this. How do we use this to do good and better the world?"

What is machine learning?

Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.

The goal of AI is to create computer models that exhibit "intelligent behaviors" like humans, according to Boris Katz, a principal research scientist and head of the InfoLab Group at CSAIL. This means machines that can recognize a visual scene, understand a text written in natural language, or perform an action in the physical world.

Machine learning is one way to use AI. It was defined in the 1950s by AI pioneer Arthur Samuel as "the field of study that gives computers the ability to learn without explicitly being programmed."

The definition holds true, according to Mikey Shulman, a lecturer at MIT Sloan and head of machine learning at Kensho, which specializes in artificial intelligence for the finance and U.S. intelligence communities. He compared the traditional way of programming computers, or "software 1.0," to baking, where a recipe calls for precise amounts of ingredients and tells the baker to mix for an exact amount of time. Traditional programming similarly requires creating detailed instructions for the computer to follow.

But in some cases, writing a program for the machine to follow is time-consuming or impossible, such as training a computer to recognize pictures of different people. While humans can do this task easily, it's difficult to tell a computer how to do it. Machine learning takes the approach of letting computers learn to program themselves through experience.

Machine learning starts with data - numbers, photos, or text, like bank transactions, pictures of people or even bakery items, repair records, time series data from sensors, or sales reports. The data is gathered and prepared to be used as training data, or the information the machine learning model will be trained on. The more data, the better the program.

From there, programmers choose a machine learning model to use, supply the data, and let the computer model train itself to find patterns or make predictions. Over time the human programmer can also tweak the model, including changing its parameters, to help push it toward more accurate results. (Research scientist Janelle Shane's website AI Weirdness is an entertaining look at how machine learning algorithms learn and how they can get things wrong - as happened when an algorithm tried to generate recipes and created Chocolate Chicken Chicken Cake.)

Some data is held out from the training data to be used as evaluation data, which tests how accurate the machine learning model is when it is shown new data. The result is a model that can be used in the future with different sets of data.

Successful machine learning algorithms can do different things, Malone wrote in a recent research brief about AI and the future of work that was co-authored by MIT professor and CSAIL director Daniela Rus and Robert Laubacher, the associate director of the MIT Center for Collective Intelligence.

"The function of a machine learning system can be descriptive, meaning that the system uses the data to explain what happened; predictive, meaning the system uses the data to predict what will happen; or prescriptive, meaning the system will use the data to make suggestions about what action to take," the researchers wrote.

There are three subcategories of machine learning:

Supervised machine learning models are trained with labeled data sets, which allow the models to learn and grow more accurate over time. For example, an algorithm would be trained with pictures of dogs and other things, all labeled by humans, and the machine would learn ways to identify pictures of dogs on its own. Supervised machine learning is the most common type used today.

In unsupervised machine learning, a program looks for patterns in unlabeled data. Unsupervised machine learning can find patterns or trends that people aren't explicitly looking for. For example, an unsupervised machine learning program could look through online sales data and identify different types of clients making purchases.

Reinforcement machine learning trains machines through trial and error to take the best action by establishing a reward system. Reinforcement learning can train models to play games or train autonomous vehicles to drive by telling the machine when it made the right decisions, which helps it learn over time what actions it should take.

In the Work of the Future brief, Malone noted that machine learning is best suited for situations with lots of data - thousands or millions of examples, like recordings from previous conversations with customers, sensor logs from machines, or ATM transactions. For example, Google Translate was possible because it "trained" on the vast amount of information on the web, in different languages.

In some cases, machine learning can gain insight or automate decision-making in cases where humans would not be able to, Madry said. "It may not only be more efficient and less costly to have an algorithm do this, but sometimes humans just literally are not able to do it," he said.

Google search is an example of something that humans can do, but never at the scale and speed at which the Google models are able to show potential answers every time a person types in a query, Malone said. "That's not an example of computers putting people out of work. It's an example of computers doing things that would not have been remotely economically feasible if they had to be done by humans."

Machine learning is also associated with several other artificial intelligence subfields:
Natural language processing
Natural language processing is a field of machine learning in which machines learn to understand natural language as spoken and written by humans, instead of the data and numbers normally used to program computers. This allows machines to recognize language, understand it, and respond to it, as well as create new text and translate between languages. Natural language processing enables familiar technology like chatbots and digital assistants like Siri or Alexa.
Neural networks
Neural networks are a commonly used, specific class of machine learning algorithms. Artificial neural networks are modeled on the human brain, in which thousands or millions of processing nodes are interconnected and organized into layers.

In an artificial neural network, cells, or nodes, are connected, with each cell processing inputs and producing an output that is sent to other neurons. Labeled data moves through the nodes, or cells, with each cell performing a different function. In a neural network trained to identify whether a picture contains a cat or not, the different nodes would assess the information and arrive at an output that indicates whether a picture features a cat.
Deep learning
Deep learning networks are neural networks with many layers. The layered network can process extensive amounts of data and determine the "weight" of each link in the network - for example, in an image recognition system, some layers of the neural network might detect individual features of a face, like eyes, nose, or mouth, while another layer would be able to tell whether those features appear in a way that indicates a face.

Like neural networks, deep learning is modeled on the way the human brain works and powers many machine learning uses, like autonomous vehicles, chatbots, and medical diagnostics.

"The more layers you have, the more potential you have for doing complex things well," Malone said.

Deep learning requires a great deal of computing power, which raises concerns about its economic and environmental sustainability.

How businesses are using machine learning

Machine learning is the core of some companies' business models, like in the case of Netflix's suggestions algorithm or Google's search engine. Other companies are engaging deeply with machine learning, though it's not their main business proposition.

Others are still trying to determine how to use machine learning in a beneficial way. "In my opinion, one of the hardest problems in machine learning is figuring out what problems I can solve with machine learning," Shulman said. "There's still a gap in the understanding."

In a 2018 paper, researchers from the MIT Initiative on the Digital Economy outlined a 21-question rubric to determine whether a task is suitable for machine learning. The researchers found that no occupation will be untouched by machine learning, but no occupation is likely to be completely taken over by it. The way to unleash machine learning success, the researchers found, was to reorganize jobs into discrete tasks, some which can be done by machine learning, and others that require a human.

Companies are already using machine learning in several ways, including:

Recommendation algorithms. The recommendation engines behind Netflix and YouTube suggestions, what information appears on your Facebook feed, and product recommendations are fueled by machine learning. "[The algorithms] are trying to learn our preferences," Madry said. "They want to learn, like on Twitter, what tweets we want them to show us, on Facebook, what ads to display, what posts or liked content to share with us."

Image analysis and object detection. Machine learning can analyze images for different information, like learning to identify people and tell them apart - though facial recognition algorithms are controversial. Business uses for this vary. Shulman noted that hedge funds famously use machine learning to analyze the number of cars in parking lots, which helps them learn how companies are performing and make good bets.

Fraud detection. Machines can analyze patterns, like how someone normally spends or where they normally shop, to identify potentially fraudulent credit card transactions, log-in attempts, or spam emails.

Automatic helplines or chatbots. Many companies are deploying online chatbots, in which customers or clients don't speak to humans, but instead interact with a machine. These algorithms use machine learning and natural language processing, with the bots learning from records of past conversations to come up with appropriate responses.

Self-driving cars. Much of the technology behind self-driving cars is based on machine learning, deep learning in particular.

Medical imaging and diagnostics. Machine learning programs can be trained to examine medical images or other information and look for certain markers of illness, like a tool that can predict cancer risk based on a mammogram.
Read report: Artificial Intelligence and the Future of Work
How machine learning works: promises and challenges

While machine learning is fueling technology that can help workers or open new possibilities for businesses, there are several things business leaders should know about machine learning and its limits.
Explainability
One area of concern is what some experts call explainability, or the ability to be clear about what the machine learning models are doing and how they make decisions. "Understanding why a model does what it does is actually a very difficult question, and you always have to ask yourself that," Madry said. "You should never treat this as a black box, that just comes as an oracle ... yes, you should use it, but then try to get a feeling of what are the rules of thumb that it came up with? And then validate them."

This is especially important because systems can be fooled and undermined, or just fail on certain tasks, even those humans can perform easily. For example, adjusting the metadata in images can confuse computers - with a few adjustments, a machine identifies a picture of a dog as an ostrich.

Madry pointed out another example in which a machine learning algorithm examining X-rays seemed to outperform physicians. But it turned out the algorithm was correlating results with the machines that took the image, not necessarily the image itself. Tuberculosis is more common in developing countries, which tend to have older machines. The machine learning program learned that if the X-ray was taken on an older machine, the patient was more likely to have tuberculosis. It completed the task, but not in the way the programmers intended or would find useful.

The importance of explaining how a model is working - and its accuracy - can vary depending on how it's being used, Shulman said. While most well-posed problems can be solved through machine learning, he said, people should assume right now that the models only perform to about 95% of human accuracy. It might be okay with the programmer and the viewer if an algorithm recommending movies is 95% accurate, but that level of accuracy wouldn't be enough for a self-driving vehicle or a program designed to find serious flaws in machinery.
Bias and unintended outcomes
Machines are trained by humans, and human biases can be incorporated into algorithms - if biased information, or data that reflects existing inequities, is fed to a machine learning program, the program will learn to replicate it and perpetuate forms of discrimination. Chatbots trained on how people converse on Twitter can pick up on offensive and racist language, for example.

In some cases, machine learning models create or exacerbate social problems. For example, Facebook has used machine learning as a tool to show users ads and content that will interest and engage them - which has led to models showing people extreme content that leads to polarization and the spread of conspiracy theories when people are shown incendiary, partisan, or inaccurate content.

Ways to fight against bias in machine learning including carefully vetting training data and putting organizational support behind ethical artificial intelligence efforts, like making sure your organization embraces human-centered AI, the practice of seeking input from people of different backgrounds, experiences, and lifestyles when designing AI systems. Initiatives working on this issue include the Algorithmic Justice League and The Moral Machine project.

Putting machine learning to work

Shulman said executives tend to struggle with understanding where machine learning can actually add value to their company. What's gimmicky for one company is core to another, and businesses should avoid trends and find business use cases that work for them.

Machine learning is changing, or will change, every industry, and leaders need to understand the basic principles, the potential, and the limitations.
Aleksander Madry MIT Computer Science Professor
The way machine learning works for Amazon is probably not going to translate at a car company, Shulman said - while Amazon has found success with voice assistants and voice-operated speakers, that doesn't mean car companies should prioritize adding speakers to cars. More likely, he said, the car company might find a way to use machine learning on the factory line that saves or makes a great deal of money.

"The field is moving so quickly, and that's awesome, but it makes it hard for executives to make decisions about it and to decide how much resourcing to pour into it," Shulman said.

It's also best to avoid looking at machine learning as a solution in search of a problem, Shulman said. Some companies might end up trying to backport machine learning into a business use. Instead of starting with a focus on technology, businesses should start with a focus on a business problem or customer need that could be met with machine learning.

A basic understanding of machine learning is important, LaRovere said, but finding the right machine learning use ultimately rests on people with different expertise working together. "I'm not a data scientist. I'm not doing the actual data engineering work - all the data acquisition, processing, and wrangling to enable machine learning applications - but I understand it well enough to be able to work with those teams to get the answers we need and have the impact we need," she said. "You really have to work in a team."

Sign-up for a Machine Learning in Business Course.

Watch an Introduction to Machine Learning through MIT OpenCourseWare.

Read about how an AI pioneer thinks companies can use machine learning to transform.

Watch a discussion with two AI experts about machine learning strides and limitations.

Take a look at the seven steps of machine learning.
Read next: 7 lessons for successful machine learning projects

# data[].attributes.attachment_link.description
Machine learning is a powerful form of artificial intelligence that is affecting every industry. Here's what you need to know about its potential and limitations and how it's being used.

# data[].attributes.attachment_link.title
A Call to Action for Closing the Digital Access, Design, and Use Divides.pdf

# data[].attributes.attachment_link.readable_text
Lately I've been digging in to the metaphor of Generative AI content as "digital plastic". I first wrote about it back in 2023 in this article, where I defined the media as a "mass produced, synthetic form of data that like its physical counterpart doesn't degrade much over time" and likened it to the way physical plastic has left its mark on the geological record, suggesting that AI-generated content could leave a similar "layer" in the digital ecosystem.

Earlier this year, I followed up that original post with a second which extended the environmental analogy and critiqued the business model of AI developers responsible for these "slop producing" machines that are currently flooding the internet.

But as I mentioned in the original post, like real-world plastics Generative AI content will have positives as well as negatives. Much of the nuance can be found in the production and distribution of these media, terms which I'm borrowing and updating from social semioticians Gunther Kress and Theo Van Leeuwen, in their text Multimodal Discourse Analysis ( a text I've written about elsewhere for its usefulness in framing multimodal GenAI). The "plastic" analogy for media also features in that text, though it takes a retrospective look at the impact of real plastics rather than digital media.

In this post, I'm continuing to bounce these ideas around, including going back through criticisms and praise for plastics which extend back for decades, and which might help to illuminate some of the nuances of digital plastic.

Production

Kress and Van Leeuwen's text breaks "meaning making" down into four strata: discourse, design, production, and distribution. I looked closely at the first two in the earlier post, exploring how both discourse and design play a part in my own use of multimodal GenAI for this blog and my other writings. But production does not only realise design work, it contributes to meaning-making. In fact, in Kress and Van Leeuwen's words, "semiosis begins at the level of production." But what does this actually mean for my metaphor of digital plastic?

Form my perspective, it suggests the meaning-making process starts the moment content is created, not just when it's consumed. For AI-generated content, this has important implications:

1. Algorithm as author: The choices made in designing and training AI models are inherently "meaningful". What data is used for training? What is excluded? What biases are encoded in the algorithms? These decisions shape the content before it's even generated.

2. Multimodality: When we use AI to generate text, images, audio, or video, the choice of mode itself is part of the meaning-making process. A GPT-generated story carries different meanings than a DALL-E generated image on the same topic, and different encoded values will be represented - I'll demonstrate that in a moment.

3. Technological context: The capabilities and limitations of AI systems influence what can be produced and how. This technological context is as much a part of the meaning as the content itself. Different users (authors) have access to different tools and resources.

4. Power dynamics: Who has access to these powerful AI tools? How are they being deployed? Who owns the data? The distribution of power in AI content creation is shaping our digital landscape.

5. Constraints and affordances: The specific features and constraints of AI models (like token limits, speed, or image resolution) affect the produced content in ways that become part of its meaning.

Understanding that semiosis begins at production challenges us to look beyond just the flood of AI-generated content. We need to critically examine the systems and processes that produce this digital plastic. It's not enough to moderate the output; we must also scrutinise the production pipeline itself.

In the context of digital plastic, this perspective brings to mind questions like:

How does the distribution of access to AI tools shape our digital discourse?

How do the training datasets for large language models influence the content they produce?

What meanings are encoded in the choice to use AI for content creation in the first place?

But this talk of multimodal discourse can be quite abstract. We need some more concrete examples.

PowerPoints, Parrots, and Plastic Trees

Think about the ways in which Word Processors (like MS Word) and PowerPoints influence how we produce text. You might design a presentation or a report with a certain audience in mind, but the platform you chose to produce the text exerts its own influence.

Microsoft Word ships with default line spacing, preferred fonts which change over time (Times New Roman, then Calibri, now Aptos), and affordances such as set titles, headings, and other styles and formatting quirks. Most users are content to stick within the boundaries of default settings, whereas some will actively rail against them.

Some people love PowerPoint for its simplicity and easy to use templates. Some organisation, like big consulting firms and government departments, use PowerPoints so much that it has become a cultural meme. Others, like The Guardian's Andrew Smith, believe that the dot-point slides are " killing critical thought". Again, whatever your stance on them, PowerPoints exert an influence on meaning in the production of texts.

Now turn to Generative AI. Emily Bender, Timnit Gebru, and others famously referred to Large Language Models like GPT as "stochastic parrots", in that they randomly repeat back to us the churned up contents of their massive training datasets. But viewed from the perspective of 'production', these stochastic parrots are more than just dataset echo chambers. They're complex systems that shape the very nature - and meaning - of the content they produce. Just as Word and PowerPoint influence our writing and presentations, AI models exert their own unique influence on the digital plastic they generate.

Consider the default "settings" of an AI model. These aren't as obvious as font choices or slide layouts, but they're just as impactful. The model's training data, its architecture, safety features, even the prompts used to generate responses - all of these act as invisible defaults that shape the production. And just like how most people stick with Times New Roman or Calibri, many users of AI tools accept these defaults without question.

The cultural influence of AI-generated content is also becoming increasingly apparent. Just as PowerPoint has become synonymous with certain types of corporate communication, AI-generated text is developing its own recognisable style. We're starting to see certain "tells" that indicate AI authorship - perhaps a bit too fluent, a bit too generic, or filled with "delving" and "navigating".

Returning to my main analogy, Kress and Van Leeuwen also provide a brief history of critical attitudes towards real-world plastics extending back to the 1930s. As a medium of production, plastic has been both lauded and criticised - much like we're seeing right now with Generative AI text, images, videos, and music.

1930s artist and designer Paul Frankl wrote that, through plastics, "base materials are transmuted into marvels of new beauty" and philosopher and semiotician Roland Barthes praised them as "miraculous" and stated in his text Mythologies that, "more than a substance, plastic is the very idea of its infinite transformation; as its everyday name indicates, it is ubiquity made visible."

In fact, "ubiquity made visible" sounds like a perfect term for the AI-generated content currently flooding our search results and social media feeds, and Barthes wasn't only praising plastic for its ubiquity. Later in the same text, he writes that the substance is "graceless" and "destroys all the pleasure...the humanity of touch". This is all sounding pretty familiar...

Even French socialist and philosopher Jean Baudrillard (whose work was also the inspiration for The Matrix, whether he wanted it to be or not) had a crack at synthetic materials, writing:

The manufacture of synthetics signifies for materials a stepping back from their natural symbolism towards a polymorphism, towards a superior level of abstraction which enables a game of the universal associations to take place. (from The System of Objects, 1996, quotes in Kress and Van Leeuwen, 2001 and in this more article on 'Dematerialization' by Van Leeuwen)

If I'm reading this right, Baudrillard is suggesting synthetics are freed from the inherent symbolic meanings associated with natural materials. This allows them to take on multiple forms and meanings, enabling a more abstract and universal system of associations for designers.

With the metaphor of "digital plastic", I think we can view AI-generated content in a similar fashion. Like synthetic materials, AI-generated text, images, or videos aren't bound by the 'natural symbolism' of other human-created content, even those texts produced via other digital platforms like word processors and slideshows.

This flexibility allows the 'game of universal associations', where we can draw on both the vast training data and the multimodality of GenAI to produce novel combinations. But this abstraction also means that AI-generated content lacks the grounding in lived experience that gives human-created content its depth and resonance. There's a balance to be struck between the flexibility and ubiquity of AI-generated content and the authenticity and intentionality of human creation.

A New Language for Digital Plastics

Kress and Van Leeuwen close their discussion of production with a quote from Sylvia Katz, plastics expert at the Victoria and Albert Museum, who predicted in 1990 that "before the century runs out a new lexicon will have to be created for plastic, a terminology that can be understood in a direct, non-technical way." In many ways, Katz's prediction has come true.

While we haven't developed an entirely new lexicon for plastics, our language around them has indeed evolved. Terms like "microplastics," "biodegradable," "compostable," and "ocean plastic" have entered common usage. We now have a more nuanced vocabulary to discuss the environmental impact of plastics, with phrases like "plastic pollution," "plastic-free," and "zero waste" becoming part of everyday discourse.

But this evolution in language has been driven less by the need to describe plastics themselves and more by the urgent need to address their environmental impact. The new terminology is less about the technical or production properties of plastics and more about their lifecycle and effects on the world around us.

Thinking about the implications of AI-generated content, we're in a similar position to where we were with plastics in the 1990s. We're developing new terms and concepts to describe this digital phenomenon - "digital plastic" being one such attempt. There's an element of both the technical and the societal; the synthetic production of media, and its impacts and implications for our digital ecosystem.

This parallel underscores something else I've written about a lot recently: the importance of developing clear, accessible language to discuss new technologies and their effects. As we work to understand the challenges and opportunities of AI-generated content, perhaps we too will need to create "a new lexicon... that can be understood in a direct, non-technical way" to fully grasp and communicate the impact of digital plastic on our digital world.
I regularly work with schools, universities, and faculty teams on developing guidelines and approaches for Generative AI. If you're interested in talking about consulting and PD, get in touch via the form below:

# data[].attributes.attachment_link.title
The Semiotics of Synthetic Media: Production and Meaning in Digital Plastic

# data[].attributes.attachment_link.description
Lately I've been digging in to the metaphor of Generative AI content as "digital plastic". I first wrote about it back in 2023 in this article, where I defined the media as a "mass produced, synthetic form of data that like its physical counterpart doesn't degrade much over time" and likened it to the way...

# data[].attributes.attachment_link.readable_text
Update: since I wrote this original post covering the nine areas, I've expanded each one into a complete article. Have a read through this post, and then when you're ready to dive deeper into AI ethics, check out the full series here. If you linked to this post as part of a course or university resource, I suggest updating your links with the complete series.

As we head into the start of Term 1 it's already looking like Artificial Intelligence is going to be one of the most talked about issues in the classroom. Much of the narrative around models like OpenAI's ChatGPT has centred on students using it to cheat on assignments. But I've already been working with schools this year who are much more interested in the potential of these technologies to help, rather than hinder in education.

Earlier this week I wrote a post with some Practical Strategies for ChatGPT in Education. It's proven to be one of the most popular posts I've written on this blog, showing that there's an appetite from teachers wanting to learn how to work with the AI. I also wrote a " Back to Basics " post for those who had never heard of ChatGPT, or wanted some grounding in AI and Large Language Models before diving in.

And yet, as much as I enjoy working with the technology, it has many flaws. I think it's our responsibility to discuss the ethical considerations of AI with our students. AI ethics goes beyond the well-documented "algorithmic bias" that results in language models like ChatGPT producing racist and sexist output. In this article, I will explore nine ethical considerations ranging from "beginner" to "intermediate" and "advanced" levels.
Practical Strategies for ChatGPT in Education ran as a live webinar in February. To access a recording of the webinar, click here:
I've levelled the concerns for two reasons. Firstly, the levels reflect how easy it is to access information and resources on the particular ethical concern, and how likely the concepts are to already fit within your curriculum. For example, "environmental impact" is a beginner-level concern as it has been explored thoroughly in the media, and the climate crisis is already part of many curricula. Secondly, as you move through the levels you and your students will be required to understand and apply increasingly complex concepts and terminology. "Affect recognition" in the advanced level, for instance, requires some knowledge of facial recognition and the psychology of human emotions.

For each level I've also included a few examples of how and where you could teach students about these issues. Right now, there is no "AI curriculum" for schools. There is no single subject area devoted to Artificial Intelligence. In fact, AI already influences most aspects of our lives, so it is fitting that we should teach AI ethics across all of our subject areas. The links embedded throughout this post are there to use as resources, and for going further down the rabbit hole.
Grab a free PDF infographic of this post: Table of Contents:
Bias

Artificial Intelligence comes in many forms, but all require data. ChatGPT, for example, is a large language model that is trained on a huge dataset which includes the " common crawl"; a text-based database of over 12 years' worth of web pages. These datasets give the models tremendous capabilities, but they are also inherently biased. Indiscriminately "scraping" the internet lets in the bad along with the good, meaning that the dataset can contain racist, sexist, ableist, and otherwise discriminatory language.

Unfortunately, AI large language models hold up a mirror to internet society, and the reflection isn't pretty. Like other societies, the online community underrepresents marginalised groups, and overrepresents others. The prevalence of racism and bigotry on sources like Reddit and Twitter can bleed through the datasets and be reproduced in the output.

Bias can also come from the methods of training and reinforcement used when developing the AI systems. For example, police in the US have used systems for "predictive policing" which use algorithms to predict people likely to commit crimes. These algorithms disproportionately target poor, Black, and Latinx communities, reinforcing existing systemic biases.
Teaching points Subject examples

Environmental impact

The technology industry as a whole has an enormous impact on the environment. Most devices from smartphones to laptops incorporate metals like lithium and rare earth minerals which are in short supply and costly to extract. The mining and refining of these products adds to the environmental impact of developing the technologies that AI is built from. These costs include soil erosion, water pollution, and greenhouse gas emissions.

AI computing is increasingly carried out in "the cloud". Cloud services sound like an ethereal and temporary arrangement, but the name actually hides the physical reality of the technology. Cloud computing relies on huge data centres and infrastructure, all of which consumes energy and produces waste.

Although many of the major companies such as Google, Microsoft, and Meta have pledged to make their data centres carbon neutral, in reality this often means engaging in carbon-trading or offsetting schemes rather than actually reducing the amount of waste or environmental damage. This "greenwashing" is heavily criticised by those who would rather see an actual reduction in emissions.
Teaching points Subject examples

Academic integrity and "truth"

Academic integrity - or using AI language models to cheat - has been by far the biggest potential issue of AI covered recently in the media. There have been widespread fears that students will use language models like ChatGPT to write essays, answer questions, and cheat on assignments. These fears seem particularly strong in secondary and tertiary education, where many assignments are provided in written form.

It is also still unclear to what extent using an AI constitutes "cheating". It is not, strictly speaking, plagiarism as the output of the model is not copied from another source. Rather, the output is an original creation which is generated " probabilistically ". Knowing where to draw the line raises ethical questions about academic integrity and honesty. This has already led some universities to permit the technologies as long as they are credited. In fact, this article was written with the assistance of ChatGPT. All of the words you're reading are mine (I happen to enjoy writing), but I used the AI to help organise the structure and to fill some of my knowledge gaps in different subject areas. I'll explain the process in full in my next post.

As well as cheating, there are concerns that AI will be used to produce massive amounts of "fake news" or deliberately harmful media. This may be unintentional - one of the biggest current flaws of most language models is that they can generate very convincing lies. Or, people may use these technologies maliciously to spread political misinformation or otherwise cause harm.
Teaching points Subject examples
Copyright and intellectual property

Closely related to truth and integrity are ideas of intellectual property and the legal concerns of copyright. Copyright issues have been particularly prevalent in AI image generation. AI image generators like Stable Diffusion, DALL-E 2, and Midjourney are trained on images "scraped" from the internet. These images are then broken down and run through the AI algorithms, so that they can later be reconstructed.

This has resulted in artists' "styles" being used in AI image generation without their permission. Many artists believe that this infringes their intellectual property rights, and is an ethical issue. An often-used counterargument is that all art is based on other artists' work, and therefore the machine is simply replicating those processes. Class action lawsuits have already been filed against some AI image generators on behalf of artists.

Large language models like ChatGPT also incorporate huge amounts of other writers' work. Where writing is publicly available - such as out of copyright books or CC journalism and articles - it can be incorporated into the dataset. Even when writing is protected by copyright it can become part of the datasets. Prompting a language model to write something in the style of another author could be viewed in the same way as an image generator adopting another artist's style.
There are also question marks over who owns the copyright to materials produced by AIs such as image generators and language models. Teaching points Subject examples

Privacy and security

Privacy is a major concern in the development and use of AI systems. As these technologies become more sophisticated and integrated into our lives, there are increasing concerns about the collection and use of personal data, data breaches, and the lack of transparency in AI decision-making.

One of the most prominent examples of these issues can be found in the use of facial recognition technology. This technology, which is used in a variety of applications such as security, surveillance, and marketing, has been criticised for its potential to violate individuals' privacy and civil rights. For example, facial recognition systems have been known to have higher error rates for people with darker skin tones, and have been used to target and monitor marginalised communities as discussed earlier in "bias".

Another example of privacy concerns with AI systems is targeted advertising. AI-powered algorithms are used to analyse data on individuals' online activities in order to deliver targeted ads. Whilst this may seem harmless, it raises concerns about data privacy, data breaches, and the use of personal data for commercial gain.
Teaching points: Subject examples:

Data collection and "datafication"

The phrase " data is the new oil " crops up everywhere when you start researching AI. As I wrote about earlier in "bias", Artificial Intelligence is powered by huge amounts of data. The oil analogy suggests both data as fuel, but also the costly, dangerous, and extractive process of data-mining. In the constant quest for more and more data, the companies that develop AI systems sometimes revert to unethical practices.

" Datafication " is a term used for turning all parts of our life into a data point to be fed into an AI algorithm. As per the privacy discussion above, this should raise some serious concerns. From location data to health data, shopping habits, likes, clicks, and views, almost every interaction we have with technology is fed into an algorithm somewhere.

As we become commodities, we open ourselves up to exploitation. One major ethical concern with "datafication" is that fact that the users become the products, and that the free-labour of the users is used to generate capital for the platform owners.

"Big Data" also contributes to many of the issues we have described so far, including bias and discrimination. Any data collected by the devices we wear and use or the platforms we subscribe to ultimately becomes part of the algorithm's "world view". Unfortunately, because not everyone in the world has access to these technologies, that worldview is by definition missing some very important data.
Teaching points

Data is the new oil, both in the sense of being fuel, and in that it is costly and damaging to extract.

"Datafication" is the process of turning every aspect of our lives into data.

Users become products, and user data becomes capital.

Big Data doesn't include marginalised groups, and therefore doesn't truly represent society.

Subject examples
Success! You're on the list.

Affect recognition

Affect recognition means interpreting a person's emotions through their facial expressions, body language, speech patterns and actions. It's a controversial practice that has been widely criticised for poor research methodologies and inconsistent results.

Despite these controversies, affect recognition is an industry worth billions of dollars. It is also an industry that has already made its way into education. A system named 4 Little Trees, developed in Hong Kong, claimed to be able to monitor children's facial expressions and to assign labels for emotions such as 'happy', 'sad', and 'angry'. The system also claimed to be able to identify motivation and to predict grades.

Affect recognition is problematic on a number of levels. As well as the aforementioned question mark over its accuracy, many people question whether emotions should be "datafied" at all. There are privacy concerns with affect recognition being built into surveillance technology, including in schools. And, similar to the issues with bias discussed earlier, affect recognition technology can perpetuate discrimination. In one example, an algorithm trained to identify possible "terrorist behaviour" resulted in racial profiling.
Teaching points
Human labour

The ethical concern of AI and human labour is a two sided coin. On the one hand there are always fears that machine automation will replace jobs, even in white collar industries like law and finance. On the other is the fact that current AI systems actually rely on a tremendous amount of dangerous, low-paid human labour.

The "robots taking our jobs" argument goes back a long way. In the 16th Century, Queen Elizabeth I rejected an application for a patent on a stocking making machine, for fears it would put too many stocking-makers out of work. In more recent years, old fears of AI replacing human "knowledge work" have been reignited by ever-more powerful models like GPT-3. And though most commentators are quick to claim that AI will never replace teachers, some have made predictions that some or all parts of the job could be automated by as early as 2027.

Hidden beneath the rhetoric of the jobs AI will destroy, however, is an unseen narrative of the jobs it currently requires to function. It is useful for the companies behind AI technology that the public views it as something mysterious and almost magical. Current advances like ChatGPT and Midjourney seem to be able to produce countless outputs in text and image with little input. But there is human labour powering the magic.

A recent article by Time magazine explored the harsh conditions of the Kenyan workers employed by OpenAI to label inappropriate data for its language model. Working for less than $2 an hour, these labourers were partly responsible for training an AI algorithm to identify harsh language, graphic, sexual, and violence phrases, and other "toxic" text. Workers were required to read and label huge amounts of this data, with some reporting the experience as deeply traumatic.
Teaching points Subject examples

Power and hegemony

This final ethical concern brings us full circle back to "bias", but with a more nuanced perspective. Because the data AI models are built on is "frozen in time", it represents a static world view which encodes existing power and hierarchies in society. The reinforcement of the hegemony can further oppress and marginalise already disadvantaged people.

Think of AI as a self-perpetuating cycle. The datasets encode a certain power structure into the model - often the dominance of a heterosexual, white, Western, male perspective due to the volume of content on the internet from that lens. This is then reflected in the output, which may be used to train future models by generating "synthetic data". Although e fforts are underway to make "fair" synthetic data, it has still be found to reproduce biases.

AI also reinforces global hegemonies both in political and corporate terms. Countries and organisations need access to wealth, energy, and resources to successfully train and scale up AI models. This means that powerful AI is increasingly concentrated in the hands of those who already have the most. Actions like those outlined above in "human labour" further entrench the divide between the wealthy countries who produce AI and the poorer countries who bear the brunt of the human and environmental costs.
Teaching points Subject examples
In discussing these ethical issues with students, it can be helpful to bear in mind some existing ethical perspectives, frameworks, and approaches. In this section I'll explore some general ethics as well as some frameworks for ethical and "Responsible" AI produced by companies like Google and Microsoft.

Apply these frameworks and perspectives to your discussions with students, for example by providing them with the examples and asking them to produce their own ethical frameworks.

Ethical perspectives

There are many ethical perspectives which could be applied to AI. In this paper, for example, the author discusses Terry Bynum's principles of virtue ethics and human flourishing, translated from Aristotle into modern times. I've used ChatGPT to "translate" one step further by asking for a simplification that may be more suitable for using with students. Bynum's original is on the left.

1. Human flourishing is central to ethics.
2. Humans as social animals can only flourish in society.
3. Flourishing requires humans to do what we are especially equipped to do.
4. We need to acquire genuine knowledge via theoretical reasoning and then act autonomously and justly via practical reasoning in order to flourish.
5. The key to excellent practical reasoning and hence to being ethical is the ability to deliberate about one's goals and choose a wise course of action.

1. Being happy and healthy is important for doing the right thing
2. People need other people to be happy and healthy
3. To be happy and healthy, people need to do what they are good at
4. To know what is right, people need to learn and think for themselves
5. Making good choices is key to doing what is right.

Discussions of "human flourishing" and basic virtues then feed into more specific ethical guidelines developed by organisations and countries to help govern the creation of AI.

Ethical guidelines

Here are several examples of guiding principles used in AI ethics or "responsible AI". There are many more available, some of which I will link in the resources section of this post.

EU High-Level Expert Group on Artificial Intelligence

1. Human agency and oversight

2. Technical robustness and safety

3. Privacy and data governance

4. Transparency

5. Diversity, non-discrimination and fairness

6. Social and environmental wellbeing

7. Accountability

Google

1. Be socially beneficial

2. Avoid creating or reinforcing unfair bias

3. Be built and tested for safety

4. Be accountable to people

5. Incorporate privacy design principles

6. Uphold high standards of scientific excellence

7. Be made available for uses that accord with these principles.

Industry.gov.au Australian Ethics Principles

1. Human-centred values

2. Fairness

3. Privacy protection and security

4. Reliability and safety

5. Transparency and explainability

6. Contestability

7. Accountability

There are some key overlapping areas been these guidelines, and some which are unique. In teaching AI ethics, explore many different examples and discuss with students which are more appropriate, and why.

"Ethics washing"

One final point to note is that just because organisations develop AI Ethics or Responsible AI principles, doesn't mean they follow them. Just like the "greenwashing" of environmental concerns discussed earlier, many AI companies have been accused of paying lip-service to ethical concerns.

AI Ethics principles are often non-binding and cannot be enforced by law. To do that, we need laws and regulations imposed by states and countries.

We are at a watershed moment in terms of Artificial Intelligence like ChatGPT. Although the technology has existed for years, the release of OpenAI's chatbot has thrust it into the limelight, raising awareness for both teachers and students.

Beyond the narrative of cheating and dramatic claims of the end of industrial-era education, AI offers real opportunities to personalise learning and help with differentiation, diversity, and inclusion. But there is a shadow side to AI which cannot be ignored.

The ethical concerns of AI are complex - I haven't even scratched the surface in this article, but hopefully I have provided enough for teachers to start exploring down their own rabbit holes. I'll end with a few final suggestions for teaching AI ethics, and resources and further reading. If you've enjoyed this article, please share it.

Teaching AI Ethics: Suggested Activity

This activity comes via ChatGPT with some back-and-forth to refine the idea. I'll explore the method behind creating resources like this in more detail in a future post. For now, here are the prompts and the activity:

Prompt: Suggest a teaching idea for these ethical issues. Do not align to a particular subject area. Build out robust and interesting lesson activity ideas which can be applied to any of the 9 areas. Do not align activities with the 9 areas. Use contemporary teaching practices, and a mix of discussion, research, student centred, and explicit instruction. Produce a title for the activity (use markdown to format headings), one or two learning intentions, and the description of the activity. Limit activity to a maximum of 50 minutes.
Activity Title: "Ethical Decision Making in AI"

Learning Intention 1: Students will understand the importance of ethical decision making in the development and implementation of AI technology.

Learning Intention 2: Students will be able to identify and analyse potential ethical issues in a given AI scenario.

Activity Description:

1. Begin the lesson by introducing the concept of ethical decision making in AI and its importance.

2. Divide the class into small groups and provide each group with a different AI scenario (e.g. facial recognition technology in law enforcement, self-driving cars, AI-powered virtual assistants).

3. Have each group brainstorm potential ethical issues that may arise from the implementation of the AI technology in their scenario.

4. Each group will then present their findings to the class and engage in a class discussion on the potential ethical issues identified by each group.

5. As a class, generate a list of best practices for ethical decision making in AI development and implementation.

6. As an extension, students can research real-life cases where ethical issues have arisen in AI and present it to the class.

Prompt: Focus on the activity "Ethical Decision Making in AI". Create a handout resource for the different scenarios for step 2. Produce 6 scenarios each with a subheading (e.g., facial recognition in law enforcement, self-driving cars, AI-powered assistants, AI powered tutors). The scenario must include a brief description of the scenario and the pros and cons and ethical concerns.
Handout:
If you have questions, comments, or would like to chat about teaching with AI, use the form below to get in touch:

Books

Kate Crawford - Atlas of AI

Virginia Eubanks - Automating Inequality

Shoshana Zuboff - The Age of Surveillance Capitalism

James Barrat - Our Final Invention

Nick Srnicek - Platform Capitalism

Mark Andrejevic - Automated Media

Resources
Ethical guidelines on the use of AI in education - European Council Teaching kids the ethics of AI - MIT media lab AI in education - ISTE
AI ethics policies and guidelines

Success! You're on the list.

# data[].attributes.attachment_link.description
Update: since I wrote this original post covering the nine areas, I've expanded each one into a complete article. Have a read through this post, and then when you're ready to dive deeper into AI ethics, check out the full series here. If you linked to this post as part of a course or university...

# data[].attributes.attachment_link.title
The Manifesto for Teaching and Learning in a Time of Generative AI: A Critical Collective Stance to Better Navigate the Future | Open Praxis

# data[].attributes.attachment_link.description
Open Praxis is a peer-reviewed open access scholarly journal focusing on research and innovation in open, distance, and flexible education. It is published by International Council for Open and Distance Education - ICDE Web of Science (ESCI) Impact Factor for 2023 is 0,9; ranked 457 out of 759 in Education and Educational Sciences in Q3. Scopus Cite Score is 3.5; ranked 453 out of 1543 in Education category in Q2 and 72 out of 2801 in Library and Information Sciences category in Q2. Facts and Figures: 2024 article acceptance rate is 33%, rejection rate is 67% (13% desk rejections - 54% rejections after review). While the number of days for acceptance is 44, the number of days for rejection is 16.

# data[].attributes.attachment_link.title
Resources on Refusing, Rejecting, and Rethinking Generative AI in Writing Studies and Higher Education

# data[].attributes.attachment_link.title
 Resources to Learn about AI Hype, AI Harms, Blockchain Harms, and Data Justi

# data[].attributes.attachment_link.title
The Environmental Impacts of AI -- Primer

# data[].attributes.attachment_link.description
A Blog post by Sasha Luccioni on Hugging Face

# data[].attributes.attachment_link.title
Against AI and Its Environmental Harms-keep | Riseup Pad

# data[].attributes.attachment_link.title
AI & Ethics Presentation by Dr. Torrey Trust

# data[].attributes.attachment_link.readable_text
Author links open overlay panel Davy Tsz Kit Ng a , Jac Ka Lok Leung b , Samuel Kai Wah Chu a , Maggie Shen Qiao a

Show more

Highlights

*
Research on AI literacy has not been widely published in educational journals.

*
An exploratory review was conducted to conceptualize the newly emerging concept "AI literacy" to define, teach and evaluate AI literacy.

*
This review proposed four aspects (i.e., know and understand, use, evaluate and ethical issues) for fostering AI literacy based on the adaptation of classic literacies.

Abstract

Artificial Intelligence (AI) has spread across industries (e.g., business, science, art, education) to enhance user experience, improve work efficiency, and create many future job opportunities. However, public understanding of AI technologies and how to define AI literacy is under-explored. This vision poses upcoming challenges for our next generation to learn about AI. On this note, an exploratory review was conducted to conceptualize the newly emerging concept "AI literacy", in search for a sound theoretical foundation to define, teach and evaluate AI literacy. Grounded in literature on 30 existing peer-reviewed articles, this review proposed four aspects (i.e., know and understand, use and apply, evaluate and create, and ethical issues) for fostering AI literacy based on the adaptation of classic literacies. This study sheds light on the consolidated definition, teaching, and ethical concerns on AI literacy, establishing the groundwork for future research such as competency development and assessment criteria on AI literacy.

Keywords

AI literacy questionnaire

# data[].attributes.attachment_link.title
Conceptualizing AI literacy: An exploratory review

# data[].attributes.attachment_link.description
Author links open overlay panel Davy Tsz Kit Ng a , Jac Ka Lok Leung b , Samuel Kai Wah Chu a , Maggie Shen Qiao a Show more * Research on AI literacy has not been widely published in educational journals.

# data[].attributes.attachment_link.title
Navigating AI Safety: A Socio-Technical and Risk-based Approach to Policy Design | TechPolicy.Press

# data[].attributes.attachment_link.description
By combining a socio-technical framework and a risk-based approach, we can create innovative, adaptive AI policy that balances safety with tech progress.

# data[].attributes.attachment_link.readable_text
DOI:
https://doi.org/10.5210/fm.v29i4.13636

Abstract

The stated goal of many organizations in the field of artificial intelligence (AI) is to develop artificial general intelligence (AGI), an imagined system with more intelligence than anything we have ever seen. Without seriously questioning whether such a system can and should be built, researchers are working to create "safe AGI" that is "beneficial for all of humanity." We argue that, unlike systems with specific applications which can be evaluated following standard engineering principles, undefined systems like "AGI" cannot be appropriately tested for safety. Why, then, is building AGI often framed as an unquestioned goal in the field of AI? In this paper, we argue that the normative framework that motivates much of this goal is rooted in the Anglo-American eugenics tradition of the twentieth century. As a result, many of the very same discriminatory attitudes that animated eugenicists in the past ( e.g., racism, xenophobia, classism, ableism, and sexism) remain widespread within the movement to build AGI, resulting in systems that harm marginalized groups and centralize power, while using the language of "safety" and "benefiting humanity" to evade accountability. We conclude by urging researchers to work on defined tasks for which we can develop safety protocols, rather than attempting to build a presumably all-knowing system such as AGI.

# data[].attributes.attachment_link.title
The TESCREAL bundle: Eugenics and the promise of utopia through artificial general intelligence

# data[].attributes.attachment_link.description
https://doi.org/10.5210/fm.v29i4.13636 The stated goal of many organizations in the field of artificial intelligence (AI) is to develop artificial general intelligence (AGI), an imagined system with more intelligence than anything we have ever seen. Without seriously questioning whether such a system can and should be built, researchers are working to create "safe AGI" that is "beneficial for all of humanity."

# data[].attributes.attachment_link.title
AI Text and Student Words:  How Can We Best Distinguish? Anna Mills for Auburn University.pptx

# data[].attributes.attachment_link.title
Lost in Translation: Reimagining the Machine Learning Life Cycle in Education

# data[].attributes.attachment_link.description
Machine learning (ML) techniques are increasingly prevalent in education, from their use in predicting student dropout, to assisting in university admissions, and facilitating the rise of MOOCs. Given the rapid growth of these novel uses, there is a pressing need to investigate how ML techniques support long-standing education principles and goals.

# data[].attributes.attachment_link.readable_text
The UDL Guidelines are a tool used in the implementation of Universal Design for Learning, a framework to improve and optimize teaching and learning for all people based on scientific insights into how humans learn. Learn more about the Universal Design for Learning framework from CAST. The UDL Guidelines can be used by educators, curriculum developers, researchers, parents, and anyone else who wants to implement the UDL framework in a learning environment. These guidelines offer a set of concrete suggestions that can be applied to any discipline or domain to ensure that all learners can access and participate in meaningful, challenging learning opportunities.

Reenvisioning the UDL Guidelines

Because the UDL Guidelines are meant to be informed by feedback from the field as well as new research, they have been updated several times in the past. We are in the process of updating the Guidelines once again in our UDL Rising to Equity initiative.

This update will focus specifically on addressing systemic barriers that result in inequitable learning opportunities and outcomes. CAST aims to develop a transparent, inclusive, and community-driven process. If you are interested in collaborating and staying updated on our progress, we invite you to complete a brief survey.

# data[].attributes.attachment_link.description
The UDL Guidelines are a tool used in the implementation of Universal Design for Learning, a framework to improve and optimize teaching and learning for all people based on scientific insights into how humans learn. Learn more about the Universal Design for Learning framework from CAST. The UDL Guidelines can be used by educators, curriculum developers, researchers, parents, and anyone else who wants to implement the UDL framework in a learning environment. These guidelines offer a set of concrete suggestions that can be applied to any discipline or domain to ensure that all learners can access and participate in meaningful, challenging learning opportunities.

# data[].attributes.attachment_link.readable_text
This Guide is a 2024 version of the 2023 Crafting Your GenAI & AI Policy : A Guide for Instructors , which was designed to help university/college instructors think through their courses and their assessments to decide if they need to be redesigned for the age of AI before crafting their GenAI and AI Policy.

That 2023 Guide is based on the premise that many courses and assessments likely need to be rethought and redesigned in the age of AI, and that instructors should consider if there is any redesigning they want to do before finalizing their GenAI & AI Policy.

This 2024 Guide better enables course instructors to navigate through the material, but also provides some more fundamentals - the "first day of class is around the corner, what do I need to do now" strategies. Fundamentals like understanding how GenAI works, choosing my GenAI policy, securing assessments, talking about GenAI with my students, identifying misuse and addressing suspected misuse. For those instructors already comfortable with the fundamentals, they can find material in this Guide to go further by assessing the impact of GenAI on my course assessments and "delving" ( see what I did there? ) into assessment redesign.

(last updated September 10, 2024; site is still in the process of being updated/added to. If you want anything added or see corrections to be made, please email tbg@ucsd.edu)

# data[].attributes.attachment_link.description
Everything you need to know to Craft a GenAI Policy for Your Courses

# data[].attributes.attachment_link.title
EngageAI - An AI Bill of Rights for Educators.pdf

# data[].attributes.attachment_link.readable_text
Quick Links

Download AI Resources Guide Download Technical Checklist for AI AI Best Practices Technical Checklist for AI Sample Policies & Resources
Technical Checklist for AI

Special thanks to the San Bernardino Superintendent of Schools for their work on this checklist.

Has the application been vetted by LEA leadership for alignment with instructional and organizations goals?

Has the LEA reviewed the vendor's Terms of Service (TOS) to ensure compliance (i.e., if intended users are students, does the vendor prohibit children un the age of X from using the applications)?

Does the LEA have board policy, administrative regulations, and general guidelines regarding the use of AI.

Does the staff AUP include guidance about appropriate use of AI, including language about secure and safe data sharing practices?

Is the product hosted or on-premises?

If hosted, has the vendor signed LEA's data privacy agreement? If not and unwilling to do so, consider parent waiver.

Has the vendor detailed how LEA provided data (user prompts, supplied data, generated output etc.) will be used for training and tuning?

Has the vendor confirmed that any user-provided data (user prompts, supplied data, generated output, etc.) will remain the property of the LEA and that no LEA data will be retained past the timeline specified in the privacy agreement and/or terms of service (TOS)? Has the vendor confirmed that any user-provided data (user prompts, supplied data, generated output, etc.) will remain the property of the LEA and that no LEA data will be retained past the timeline specified in the privacy agreement and/or terms of service (TOS)?

Does the vendors privacy policy address FERPA, SOPIPA, COPPA, CCPA, and any AI specific laws?

If hosted, what firewall and filtering changes will be necessary?

If hosted, do you need to make changes to your PAT or NAT pools to ensure on your firewall to ensure uninterrupted access?

If hosted, does the application support SSO or federated loggings for easy account management? If not, how will the LEA manage accounts?

Are existing user devices compatible with and capable of supporting the application?

If hosted, does the application require a cell phone to register an account (ChatGPT)? If so, how will the LEA manage registration? Consider involving HR early to address any bargaining unit issues that may come up from the use of personal devices.

If hosted, how is billing handled? PO? Credit card, on an account-by-account basis? If a credit card is the only option, consider involving Purchasing and Business Services to discuss challenges with billing management.

If hosted, will users be expected or allowed to provide LEA data when using the application? If yes, how will users access the data?

If users are expected or allowed to provide student or staff data when using AI agent, have clear guidelines been established detailing what data is allowed to be shared?

SIS/Assessment reports available from user accessible reporting options?

Automated regular extracts uploaded at district-level?
If yes, did the vendor provide a data map detailing the fields and formats for the data?

API-based integrations with specific applications?

If yes, are targeted applications compatible? Has the AI vendor detailed what fields will be called and the frequency of the API requests?

How will performance of applications be affected by API calls?

If on-premises, what are the storage and processing requirements?

If on-premises, are there any 3rd party integrations with hosted applications?

If so, how will data be exchanged with 3rd party applications?

Are 3rd party applications accessing LEA's student or staff data? If so, have they signed LEA's privacy agreement?

If not, consider parent waiver.

How will users be trained on appropriate and effective use of application?

If an existing approved and used application adds AI capability, have you checked with the vendor to confirm that current data privacy agreement covers AI features?

What data elements of existing approved applications are used by new AI features?

Has the vendor confirmed that any user-provided data (user prompts, supplied data, generated output, etc.) will remain the property of the LEA and that no LEA data will be retained past the timeline specified in the privacy agreement and/or terms of service (TOS)?

Has the vendor confirmed that any user-provided data (user prompts, supplied data, generated output, etc.) will remain the property of the LEA and that no LEA data will be retained past the timeline specified in the privacy agreement and/or terms of service (TOS)?

If students are expected to use the application, how will they access it?

How will it be rostered?

Google Workspace or M365 integration?

Classlink, Clever or other SSO?

Nightly extracts/uploads and individualized accounts?

Is the use of AI tools covered by student RUP/AUP?

How will the system be monitored for appropriate use and/or abuse?

Will parents/guardians have access to the student tool?

# data[].attributes.attachment_link.title
AI Checklist - California IT in Education

# data[].attributes.attachment_link.description
Quick Links Download AI Resources Guide Download Technical Checklist for AI AI Best Practices Technical Checklist for AI Sample Policies & Resources Technical Checklist for AI Special thanks to the San Bernardino Superintendent of Schools for their work on this checklist. Has the application been vetted by LEA leadership for alignment with instructional and organizations goals?

# data[].attributes.body
Educators can critically inquire into technologies (in this case AI) by asking the these five technoskeptical questions:

1. What does society give up for the benefits of the technology (AI)?

2. Who is harmed and who benefits from the technology?

3. What does the technology need?

4. What are the unintended or unexpected changes caused by the technology?

5. Why is it difficult to imagine our world without the technology?

(Civics of Technology).

Furthermore:

Rather than accepting the determinism of the techno-capitalist agenda, how are we empowering students with agency to assert counternarratives for just futures?

# data[].attributes.attachment_link.readable_text
Technologies are not neutral and neither are the societies into which they are introduced. As technology continues encroaching in our lives, how can we advance technology education for just futures?

The Civics of Technology Project shares research and curriculum and offers professional development that encourages teachers and students to critically inquire into the effects of technology on our individual and collective lives.

# data[].attributes.attachment_link.description
The Civics of Technology Project shares research and curriculum and offers professional development that encourages teachers and students to critically inquire into the effects of technology on our individual and collective lives.

# data[].attributes.attachment_link.readable_text
Before using AI in K-12 classrooms, teachers, administrators, and district leaders should understand what AI is and is not. Implementing AI apps without a solid foundational understanding does not serve students. But who to turn to for critical analysis of AI instead of hype?

Following these experts has helped me understand AI. Their work has influenced my posts about using a chatbot as a guest speaker and the 100% ethical AI app. With one exception, these are experts on AI, not K-12 education. Get clarity on AI, then think about using it in the classroom.

This list has none of the three B's. That means:

These experts often speak about the ethical concerns of AI. Neither "an AI apocalypse" nor "student cheating" come up when they address AI ethical concerns.

With that, let's meet the experts. Please follow them.

Timnit Gebru

Scientist and AI ethicist Dr. Timnit Gebru was the co-head of Google's AI Ethics team until she co-authored a paper Google objected to. The paper, On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?🦜 (opens PDF), argued that Large Language Models such as ChatGPT and Gemini amplify bias and environmental racism. Two of Gebru's co-authors are on this list. Anyone interested in AI should read their work.

She is now the Founder and Executive Director of the Distributed AI Research Institute (DAIR) and the founder of Black in AI, a community of Black researchers working in AI.

Here is Gebru's TEDx talk about AI and marginalized communities.

Margaret Mitchell

Mitchell makes sense of Gemini's recent problems in her Time Magazine piece, Ethical AI Isn't to Blame for Google's Gemini Debacle.

Here is a TED talk Mitchell gave about AI while still at Google.

Emily Bender

Dr. Emily Bender is a computational linguist and Linguistics Professor at the University of Washington.

Bender co-authored a 2022 research paper, Situating Search (opens PDF), which argued that Large Language Models (LLMs) are not a search tool.

Bender is a co-author of the Stochastic Parrots paper. According to a profile of Bender in New York Magazine, "A stochastic parrot (coinage Bender's) is an entity "for haphazardly stitching together sequences of linguistic forms ... according to probabilistic information about how they combine, but without any reference to meaning." "Stochastic Parrots" is an easy way to remember what LLMs do. Anyone interested in AI should read the profile.

At 11:45 of this episode of the Tomayto Tomahto podcast, she said, "The issues and dangers that we identified [in the Stochastic Parrots paper] have not been addressed."

Here, Bender spoke about interacting with chatbots.

In this video, Bender and Mitchell talk about data sets and bias.

This is OpenAI CEO Sam Altman's response to the term "Stochastic Parrots."

As someone who wrote on March 9, 2020, "The fears over Coronavirus may be more hype than substance," I should probably not throw stones at someone for saying something so incorrect. At least mine didn't denigrate all of humanity.

Alex Hanna

Hanna talks about algorithmic bias and AI in this video.

Joy Buolamwini

In an interview, Buolamwini said, "We can address algorithms of exploitation by establishing creative rights that uphold the four C's: consent, compensation, control, and credit. Artists should be paid fairly for their valuable content and control whether or how their work is used from the beginning, not as an afterthought."

Buolamwini shared an approach all schools should take when evaluating AI apps: "We continue to ask questions that go beyond hype or doom and ask if the AI systems being adopted in our schools, workplaces, and hospitals actually deliver what they promise."

Watch Buolamwini's TED talk to learn about algorithmic bias.

"We continue to ask questions that go beyond hype or doom and ask if the AI systems being adopted in our schools, workplaces, and hospitals actually deliver what they promise."Dr. Joy Buolamwini, 'Unmasking AI' and the Fight for Algorithmic Justice
A conversation with Dr. Joy Buolamwini, By Nabiha Syed, The Markup, November 18, 2023

Here Buolamwini delivers her poem, "AI, Ain't I A Woman?"

Sasha Luccioni

Dr. Sasha Luccioni is a scientist and AI researcher. She and Dr. Mitchell are two of the writers of an interesting post about AI watermarking - the practice of labeling synthetic content.

In Luccioni's TED talk, she addressed the actual harms of AI. Neither "students cheating" nor "AI taking over the world" is among them.

Meredith Broussard

In this video, Broussard spoke about what AI is and how that is related to intelligence.

Luiza Jarovsky

Luiza Jarovsky is a lawyer and CEO of Implement Privacy who focuses on AI and privacy.

Jarovsky said something about writing and generative AI that resonates: "If you have something to say, say it...If you don't have anything to say, don't stuff it with AI-generated text." She was referring to LinkedIn comments, but to me, that quote applies to all writing.

Gary Marcus

In this TED Talk, Marcus spoke about how he loves AI, but is worried about its direction and risks.

Paris Marx

Dan Meyer

Dan Meyer is the only expert on this list with a K-12 background. His Math-focused Substack critically examines educational technology. His thoughts on AI in the classroom are sober and informed. As the excitement about AI in education grew, Meyer retained his critical lens. For samples of the wisdom Meyer brings to the conversation about AI in education, please read his posts, Happy 60th Birthday, ChatGPT!, Khanmigo Doesn't Love Kids, and Generative AI Asks Teachers to Eat Scraps From the Table.

Essential Resources

Here are resources you can use to learn about AI from the experts:

Continuing The Conversation

What do you think? Whose insight do you turn to understand AI? Comment below or Tweet me at @TomEMullaney.

Does your school or conference need a tech-forward educator who critically evaluates AI? Reach out on Twitter or email mistermullaney@gmail.com.

Blog Post Image: The blog post image is a mashup of three images. The background is Education and reading concept by Sensay on Adobe Stock. The books and lightblub icon is 3d minimal self-development concept by peerapong on Adobe Stock. The robot is White male cyborg thinking and touching his head 3D rendering by sdecoret on Adobe Stock.

AI Disclosure:

I wrote this blog post without the use of any generative AI. That means:

I developed the idea for the post without using generative AI.

I wrote an outline for this post without the assistance of generative AI.

I wrote the post from the outline without the use of generative AI.

I edited this post without the assistance of any generative AI. I used Grammarly to assist in editing the post. I have Grammarly GO turned off.

I did not use any WordPress AI features to write this post.

# data[].attributes.attachment_link.title
Teachers: Follow These Experts To Learn AI

# data[].attributes.attachment_link.description
Before using AI in K-12 classrooms, teachers, administrators, and district leaders should understand what AI is and is not. Implementing AI apps without a solid foundational understanding does not serve students. But who to turn to for critical analysis of AI instead of hype? Following these experts has helped me understand AI.

# data[].attributes.attachment_link.description
Data & Society advances public understanding of the social implications of data-centric technologies and automation.

# data[].attributes.attachment_link.authors[].name
LIS - The London Interdisciplinary School

# data[].attributes.attachment_link.description
Not only can ChatGPT's AI save you time and improve your teacher workflow, but it can also help you create innovative learning experiences and build easy assessment opportunities.

# data[].attributes.attachment_link.readable_text
Much has changed in the AI/ML world but the concept of 'garbage in; garbage out' remains stoic. Any algorithm is only as good as its training data. And, no training data is without bias, not even the ones generated through automation.

In the past, many machine learning algorithms have been unfair to certain religions, races, genders, ethnicities, and economical statuses, among others. The Watson supercomputer from IBM that gave suggestions to doctors using a dataset of medical research papers was found to favor reputable studies only. Amazon's recruiting algorithm was found to favor men over women. COMPAS, a risk assessment algorithm used by many state judges in the USA to assist sentence determination, was disputed for assigning a higher reoffense risk factor in violent crimes to Black people. In 2016, Microsoft's one-day experiment with a Twitter chatbot (that learned from other tweets) ended badly when the bot released 95000 tweets in 16 hours, most filled with misogyny, racism, and anti-semitic ideas.

But, here is the thing - Algorithms aren't unethical, racist, or morally flawed. However, the data they are trained on is another story.

A bias can corrupt data at multiple junctions. It can seep into an otherwise harmless dataset during collection, data aggregation, model selection, or even end-user interpretation. In fact, it is almost alright to assume that no data exists without one or other kind of bias. It may be caused by external prejudice from the human trainer. Or, it may appear as a result of oversimplification or unsatisfactory representation of any group in the dataset.

The root cause isn't always obvious. But, the impact is!

A biased dataset is unbalanced. It fails in representing the original motive of the machine learning model. Its results are inaccurate, its decisions are prejudiced, and its precision level can vary under different contexts, thus defeating the purpose of the model.

COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) is a very popular example of the ramifications of a self-sufficient algorithm trained on biased data. It uses a questionnaire to evaluate offenders on various parameters and predicts the likelihood of them committing a crime again. This prediction is used by judges across many US states and jurisdictions to determine sentences.

After a few questionable decisions, various questions about its integrity, and several lawsuits, ProPublica examined the algorithm's risk assessment factors and observed two major absurdities.

Prediction accuracy below 20%

2X instances of wrong labeling in the case of Black offenders

COMPAS is just one example to demonstrate the very real consequences that a human may have to suffer due to a prejudiced ML model. The repercussions of any AI/Ml model's implementational scope are directly impacted by the nature of training an algorithm gets, and hence, on the data annotation service that creates said dataset.

Sample (or Selection) bias is introduced in a system when a subgroup of the data is excluded during its study, accidentally or knowingly. Such a sample will not genuinely reflect the intended observable environment and lead to inaccurate results.

For example, consider a dataset that studies data on the population of a locality for 15 years to help predict new tenant tenure. A bias may be introduced if we falsely interpret the 15-year term as an absolute and exclude any tenant that lived in the locality for less than that duration from our study. Eliminating such data renders the model ineffective because it will not be trained on every possible case.

Algorithmic bias is a systematic error that leads to poor computation. It could be a pre-existing issue or emerge because of program limitations or design constraints. Algorithmic bias can also show up if a particular algorithm is used in an environment it isn't trained for.

For example, consider any automated plagiarism checker that compares over three-word-long strings to a set of content and returns a similarity rate. If the target content is spun and words are changed with synonyms, the algorithm's rate of accuracy will drop.

Prejudice has a massive range. It goes from region-based-terminology differences to deeper discrimination around race, religion, gender, sexuality, etc. Prejudicial bias in a dataset arises because of the perspective of data trainers.

For example, a data labeling team in the UK goes through pictures of women's wallets and labels them as a purse. For the data's receptors in the USA, that model will create ineffective results because a 'purse' in the States refers to a woman's handbag, not a wallet.

Measurement bias occurs due to an issue in data measurement or collection. Such deformation can be a result of misclassification, different tools used for data capture, or basic annotation mistakes.

For example, consider an image annotation service provider working on a project to label vehicles for an automated driving project. If the dataset only has images of vehicles on a road but no pedestrians or stray animals, the trainer will have nothing else to label. The final labeled data and the training it imparts to an ML Model will be very ineffective.

During cleaning, several features of the dataset are weighed and the unimportant ones are often removed. However, if we assign low importance to any feature that is otherwise significant, we may end up creating a dataset with a few underrepresented sections, leading to a prejudiced model.

For example, consider a collection of pre-menopausal symptoms from women- 90% White and 10% Hispanic. It is likely that the 10% are ignored because their symptoms aren't recognized as mainstream. The resultant algorithm will be less likely to diagnose Hispanic women in need of assistance.

Recall bias is also included in data during collection. If the data contributor offers an estimated value instead of an exact one, it will shake the overall accuracy of the set. So, we can consider it a type of measurement bias.

But, an example of direct recall bias in data annotation can be observed in cases where the trainer's label data based on an approximation and the inconsistency is repeated enough times to render the data unreliable for a certain feature.

It is possible for a trainer to project their views about a particular data feature into the dataset during labeling, leading to observer bias.

Two people labeling the same set of images can come up with different results. For example, despite the instructions to label the image as boats or not boats, a trainer may label one boat as a yacht and another as a ship, throwing off the consistency in the resultant dataset.

Observer bias is also a very common occurrence with text annotation services. The documents we present to a vendor may fall into the task list of different people across regions. They may perceive handwriting differently and thus create a striking inconsistency across the dataset.

If we take two similar situations and predict the outcome of one based on that of another owing to nothing but the similarity, that would lead to a representation bias. Representation bias is personified in the popular phrase-" correlation does not imply causation."

For example, if an algorithm assesses students' grades and predicts that those with similar grades will get into a particular college because a few of them did, that prediction will have a low accuracy rate.

At the same time, if we solely train an algorithm on a dataset that does not have equal representation for all the groups in the algorithm's intended environment, it will also cause a bias. For example, if we train an algorithm on employees' faces with a dataset of white males only, it will have trouble detecting darker skin color or women with shorter hair, etc.

These examples prove that biases are diverse in nature and can enter a sample set at any point in its lifetime. Many biases are not evident. Many are so intermingled with other processes that identifying them seems an unmanageable challenge.

There are several proposed techniques to build fairness-aware machine learning models. Many approaches, each following different contexts, have been developed and researched to reduce bias in training data for machine learning. Most of them boil down to one thing- being careful and curious at every step is critical.

A machine learning model learns from historical decisions and their intent, where the intent is known. Therefore, if a decision-maker in the past showed any prejudice in their recorded decisions, the ML model will be at risk of reflecting them.

If during data annotation, a trainer labels pictures of Siberian Huskies and Swedish Vallhund as wolves, the model will learn to do the same. If the last 20 recruitments in a company were for 19 men and 1 woman, the ML model learning from this decision-data is likely to discard more resumes from women than men.

Therefore, at every stage of training data preparation, it is important to question where the data is coming from, whose perceptions affected earlier decisions, and what changes need to be made in the data accordingly to clean it for training purposes.

This idea is challenging; its implementation is more so.

Weeding out biases requires a nearly exhaustive understanding of preconceptions that may have polluted the data. But, depending on the objective of a machine learning model, rooting out potential bias-causing perceptions can range from alright to very tricky.

You can note issues in organizational processes and fix them as well. But, doing so when training a model on multiple dynamic parameters will pose many new problems. If, for example, your ML model uses social media posts from a hundred people to determine their employability score, it might be unfair to prospects from countries whose native language isn't English, or who are from underdeveloped countries.

Therefore, despite our best attempts at identifying potential bias and rooting it out, it is preferable to test the conclusion and verify its effectiveness.

An algorithm that works for one set of data will not likely work with an extended version of the same data. It might if we keep testing the system with challenger models and verify its predictive accuracy, transparency, and improvement rate.

Despite all that, assuming self-sufficiency for any machine learning model is a mistake. As the data, its intended environment, and target objective change, so will its accuracy rate.

Despite several reasonable fairness approaches, machine learning models will remain unsatisfactory in one area or another. It is impossible to expect one model to satisfy multiple dynamic constraints and maintain its prediction accuracy simultaneously. We will need to make a choice based on the context.
Also Read:
However, at present, creating decently fair machine learning training data mostly hinges on data annotation and processing. Hopefully, we will have more concrete ways to create fair datasets in near future.

Why is it Better to Outsource Data Annotation? Why Outsourcing Video Annotation Services Is Profitable For Businesses?

# data[].attributes.attachment_link.title
How to Remove Bias in Machine Learning Training Data

# data[].attributes.attachment_link.description
Much has changed in the AI/ML world but the concept of 'garbage in; garbage out' remains stoic. Any algorithm is only as good as its training data. And, no training data is without bias, not even the...

# data[].attributes.attachment_link.readable_text
Today Stanford University's Center for Research on Foundation Models (CRFM) took a big swing on evaluating the transparency of a variety of AI large language models (that they call foundation models). It released a new Foundation Model Transparency Index to address the fact that while AI's societal impact is rising, the public transparency of LLMs is falling - which is necessary for public accountability, scientific innovation and effective governance.

The Index results were sobering: No major foundation model developer was close to providing adequate transparency, according to the researchers - the highest overall score was 54% - revealing a fundamental lack of transparency in the AI industry. Open models led the way, with Meta's Llama 2 and Hugging Face's BloomZ getting the highest scores. But a proprietary model, OpenAI's GPT-4, came in third - ahead of Stability's Stable Diffusion.

CRFM Society Lead Rishi Bommasani and his team, including CRFM Director Percy Liang, evaluated 10 major foundation model developers, including OpenAI, Anthropic, Google, Meta, Amazon, Inflection, Meta, AI21 Labs, Cohere, Hugging Face, and Stability. The team designated a single flagship model for each developer and rated each based on how transparent they are about their models, how they're built, and how they're used. The team broke the scores down into 15 categories including data, labor, compute, and downstream impact. In a recent related effort, the team evaluated model compliance with the EU AI Act.

An 'expansive notion' of transparency

Liang pointed out that the Index focused on a "much more expansive notion" of transparency than simply whether a model is proprietary or open.

"It's not that the open source models are gaining 100% and everyone else is getting zero, there is quite a bit of nuance here," he explained. "That's because we consider the whole ecosystem - the upstream dependencies, what data, what labor, what compute went into a building the model, but also the downstream impact on these models."

LLM companies are not homogenous

While Amazon's Titan model received the lowest scores, Bommasani explained that this doesn't mean there is anything wrong with the model. "There is really no reason those scores couldn't be higher, I think it's just the matter of Amazon coming into this later than, say, OpenAI." Up until now, there may not have been norms around some of the transparency categories, he added. "Hopefully once this is out, some people inside these companies will go hey, we really should be doing this because all of our competitors are - I hope this will become a basic thing that people come to expect."

Overall, "the basic point is that transparency matters," he continued, adding that transparency is not a monolithic concept. "The companies are not homogenous about what they're doing," he said. "It's not like all of them are good at data and bad at disclosing some compute." For example, he explained that Bloom, Hugging Face's model, does risk evaluation. "But when they built BloomZ from it they didn't carry over this kind of analysis of risk and mitigation," he said.

A transparency 'pop quiz'

Liang added that the Index is also a framework for thinking about transparency - and the results are simply a snapshot in time.

"This is 2023, where companies didn't see this coming," he explained. "This is actually kind of a pop quiz in some sense. I'm sure that over the next few months things will improve, there will be more pressure to be more transparent and naturally, companies will want to do more of the right thing."

In addition, he pointed out that some changes would be easy to make. "Others are harder, but I think there's just a low or medium-hanging fruit that companies really ought to be doing," he said. "I'm optimistic that we're going to see some change in the coming months."

# data[].attributes.attachment_link.title
How transparent are AI models? Stanford researchers found out.

# data[].attributes.attachment_link.description
Today Stanford University took a big swing on evaluating the transparency of a variety of AI large language models.

# data[].attributes.headline
How to create good prompts to get output we want?  How to detect and remove bias?  How to cite it?  When is it the person's own work and when is it plagiarism?

# data[].attributes.subject
How to create good prompts to get output we want?  How to detect and remove bias?  How to cite it?  When is it the person's own work and when is it plagiarism?

# data[].attributes.body
Here are some experiments I did with ChatGPT. Note, I did most of these last year and probably mostly with GPT 3.5 though some I retried with GPT 4.

# data[].attributes.attachment_link.description
If you predict the next most likely word, trained on the internet, it is most likely wrong.

# data[].attributes.attachment_link.title
AI usage policies_ A practical guide for schools resources.pdf

# data[].attributes.attachment_link.title
Emerging Technologies Board Policy 6163.4.1 (1).pdf

# data[].attributes.body
"The coded gaze"

# data[].attributes.attachment_link.title
How I'm fighting bias in algorithms | Joy Buolamwini

# data[].attributes.body
A large language model (LLM) is a type of machine learning model that can perform a variety of natural language processing (NLP) tasks such as generating and classifying text, answering questions in a conversational manner, and translating text from one language to another. The label “large” refers to the number of values (parameters) the language model can change autonomously as it learns. Some of the most successful LLMs have hundreds of billions of parameters.

# data[].attributes.attachment_link.readable_text
What Does Large Language Model (LLM) Mean?

A large language model (LLM) is a type of machine learning model that can perform a variety of natural language processing ( NLP) tasks such as generating and classifying text, answering questions in a conversational manner, and translating text from one language to another. The label "large" refers to the number of values (parameters) the language model can change autonomously as it learns. Some of the most successful LLMs have hundreds of billions of parameters.

LLMs are trained with immense amounts of data and use self-supervised learning to predict the next token in a sentence, given the surrounding context. The process is repeated over and over until the model reaches an acceptable level of accuracy.

Once an LLM has been trained, it can be fine-tuned for a wide range of NLP tasks, including:

Building conversational chatbots like ChatGPT.

Generating text for product descriptions, blog posts and articles.

Answering frequently asked questions (FAQs) and routing customer inquiries to the most appropriate human.

Analyzing customer feedback from email, social media posts and product reviews.

Translating business content into different languages.

Classifying and categorizing large amounts of text data for more efficient processing and analysis.

Techopedia Explains

A language model is a type of artificial intelligence model that is trained to understand and generate human language. It learns the patterns, structures, and relationships within a given language and has traditionally been used for narrow AI tasks such as text translation. The quality of a language model depends on its size, the amount and diversity of data it was trained on, and the complexity of the learning algorithms used during training.

A large language model refers to a specific class of language model that has significantly more parameters than traditional language models. Parameters are the internal variables of the model that are learned during the training process and represent the knowledge the model has acquired.

In recent years, the field of natural language processing has seen a trend towards building larger and more powerful language models because of advancements in hardware capabilities, the availability of extremely large datasets, and advancements in training techniques. Large language models, which have billions of parameters, require significantly more computational resources and training data than language models of the past, which makes them more challenging and more expensive to develop and deploy.

How Are Large Language Models Trained?

Most LLMs are pre-trained on a large, general-purpose dataset. The purpose of pre-training is for the model to learn high-level features that can be transferred to the fine-tuning stage for specific tasks.

The training process of a large language model involves:

Pre-processing the text data to convert it into a numerical representation that can be fed into the model.

Randomly assigning the model's parameters.

Feeding the numerical representation of the text data into the model.

Using a loss function to measure the difference between the model's outputs and the actual next word in a sentence.

Optimizing the model's parameters to minimize loss.

Repeating the process until the model's outputs reach an acceptable level of accuracy.

How Do Large Language Models Work?

A large language model uses deep neural networks to generate outputs based on patterns learned from training data.

Typically, a large language model is an implementation of a transformer-based architecture.

Unlike recurrent neural networks ( RNNs) which use recurrence as the main mechanism for capturing relationships between tokens in a sequence, transformer neural networks use self-attention as their main mechanism for capturing relationships.

They calculate a weighted sum for an input sequence and dynamically determine which tokens in the sequence are most relevant to each other.

The relationships between tokens in a sequence are calculated using attention scores that represent how import a token is in regards to the other tokens in the text sequence.

Examples of Large Language Models

Some of the most popular large language models are:

GPT-3 (Generative Pretrained Transformer 3) - developed by OpenAI.

BERT (Bidirectional Encoder Representations from Transformers) - developed by Google.

RoBERTa (Robustly Optimized BERT Approach) - developed by Facebook AI.

T5 (Text-to-Text Transfer Transformer) - developed by Google.

CTRL (Conditional Transformer Language Model) - developed by Salesforce Research.

Megatron-Turing - developed by NVIDIA

# data[].attributes.attachment_link.description
A large language model is a specific class of language model that has significantly more parameters than a traditional language model.

# data[].attributes.body
ChatGPT is a large language model that utilizes a sophisticated form of machine learning called deep learning. Trained on vast amounts of internet text, ChatGPT can produce human-like responses based on the inputs it receives. “GPT” in ChatGPT stands for “Generative Pre-training Transformer”:

Generative refers to the model’s ability to create or generate contextually relevant data.

Pre-training means the model’s trained on a large dataset (mostly text) to understand human language.

Transformer references the type of computer neural network used to generate the human-like speech output.

Students need to understand this rudimentary element of what GPT stands for to use and effectively appreciate ChatGPT’s capabilities and limitations. (https://xqsuperschool.org/uncategorized/using-generative-ai-in-high-school-classrooms/)

# data[].attributes.attachment_link.description
ChatGPT is your AI chatbot for everyday use. Chat with the most advanced AI to explore ideas, solve problems, and learn faster.

# data[].attributes.attachment_link.readable_text
1. Recommendations for Responsible AI Procurement in Government - These recommendations are based on ongoing research and were submitted with my team in response to the US Office of Management and Budget (OMB).

2. AI governance case study: Critique of OpenAI's new AI safety statement - Seven open questions that should have been answered, demonstrating a lack of transparency and accountability

1. Bianchi et al (2023) - This study compared images of professionals generated by Stable Diffusion to information from the US census. It found that GenAI exacerbates gender and race misrepresentation. My summary is here.

2. Zhou et al. (2024) - This study compares bias in generated images across three platforms: Midjourney, Stable Diffusion, and DALL-E2. It finds that all of them are biased, but to varying degrees. My summary is here.

3. Ghosh and Caliskan (2023) - This study shows what "person" means to generative AI. Images of "person" tend to be male and white. Images of women, especially women of color, tend to be sexualized. My summary is here.

"Blue fruit in a glass bowl"

# data[].attributes.attachment_link.title
Ravit's AI ethics resources: Procurement, fines, bias complication, and more

# data[].attributes.attachment_link.description
Some old, some new, some borrowed, some blue

# data[].attributes.attachment_link.readable_text
Deepfakes are videos, photos, or audio recordings that use artificial intelligence to make it look or sound like someone is saying or doing something that is not real.
Deepfake Techniques

Face Swapping: Replacing a person's face with another in a video or photo using AI.

Voice Cloning: Using AI to mimic someone's voice.

Lip Syncing: Making a person's lips appear to match different audio using AI.

The Dangers of Deepfakes
While deepfakes can be used for harmless or even educational purposes, they can also be used unethically in the following ways:

Identity theft: Deepfakes can impersonate individuals in videos or audio recordings, potentially allowing criminals to commit fraud under someone else's name.

Threats/blackmail: Deepfakes can create convincing footage or audio that puts the subject in a bad light, which can then be used to threaten or blackmail them.

Cyberbullying: Deepfakes can be used to create embarrassing or hurtful videos or audio recordings of someone.

Propaganda/fake news: Deepfakes can be used to create fake videos or audio recordings that trick people and sway their opinions on important topics.

Election manipulation: Deepfakes can be used to make candidates look bad or confuse voters by creating fake speeches or interviews.

How to Identify Deepfakes

Detecting deepfakes involves looking for inconsistencies and anomalies in videos. photos, or audio recordings that may not be immediately apparent. It's important to note that deepfake technology is quickly improving, so it may soon be impossible to tell a fake from something real.

Look closely at people's faces in the video. If their skin looks too smooth, their eyes don't blink normally, or their facial expressions seem off, it might be a deepfake.

If the voice doesn't sound quite right, like it's too robotic or the tone doesn't match how the person usually speaks, it could be a sign of a deepfake.

Pay attention to how the lips move. If they're not syncing up with the words perfectly, or the movement looks strange, the video might be manipulated.

Consider the video's overall appearance. Sometimes, the area around the person or behind them might look blurry or glitchy. This can be a sign that the video has been altered.

Or if the shadows on the person's face or in the background don't match up with the lighting in the rest of the video, it could be a clue that it's a deepfake.

A variety of Large tech companies such as Google, Meta, and OpenAI are joining together to form the Coalition for Content Provenance and Authenticity (C2PA) in order to develop methods of labeling AI-generated content to clearly identify when and how it was generated or altered. 1. In January 2024, thousands of AI-generated robocalls impersonating President Joe Biden went out to Democratic voters discouraging them from voting in the New Hampshire primary. How might this type of deepfake use impact the integrity of elections? How can we combat this? This question encourages a discussion on the potential manipulation of voter perceptions and behavior through false representations of political candidates. 2. One 2023 study found that 98% of the deepfake videos online consisted of nonconsensual pornography. The targets of 99% of this content were women, including pop star Taylor Swift. Why do you think this type of content is so widespread, and what are the social and ethical implications? This question prompts a critical discussion about the pervasive use of deepfakes to create nonconsensual pornography, focusing on the disproportionate impact on women. 3. In summer of 2023, a group of New Jersey teenagers used AI to create deepfake pornographic images of an underaged female classmate, which were circulated over group text. What psychological harms could this kind of incident cause for the victim in terms of trust, safety, and mental health? Should legal action be taken against the students involved? This question raises serious issues about the exploitation of minors using deepfake technology, as well as implications for online privacy and consent. It further prompts students to consider appropriate legal and disciplinary responses. 4. In April 2024, a high school principal in Maryland fell victim to a deepfake attack when a disgruntled athletic director faked a recording of the principal making racist remarks. What are the consequences of using deepfakes to impersonate authority figures? Consider implications for the victim, the attacker, and the system as a whole. This question encourages exploration of the ethical, social, and legal repercussions of such digital impersonations at the system level, and how they can undermine trust within communities. 5. What do you think has been the impact of deepfakes online? How has it changed how you interact with the internet? This question invites students to analyze and discuss the various effects that deepfakes have had on the online environment. Considerations include influencing public perception, personal privacy, and trust in digital media. 6. Do you think deepfakes should be banned? Why or why not? This question prompts students to consider the pros and cons of the availability of deepfake technology, weighing the implications for freedom of expression, technological innovation, and personal security. deepfake detectors are available online, but as these operate based on a model that calculates probabilities, which 8. With advancements in AI technology, deepfakes are likely to become even more sophisticated. How should society prepare for these changes? Consider the roles of individuals, educators, policymakers, and technology companies in shaping a response. This question facilitates a discussion on the necessary preparations for future advancements in AI and deepfake technology. will never be 100% reliable.
What Can You Do?

Question everything: AI-generated content has already flooded the internet, so be cautious. Always investigate if a suspicious image, video, or audio clip is real.

Discussion Questions

Use the following scenario-based discussion questions to build background knowledge on the impact of deepfakes on society to inform students' responses to the more general questions on the following page. Links to the news sources for each scenario can be found in the Sources and Further Reading section of this document.

7. Can you think of any positive use cases for deepfake content? Under what circumstances can deepfakes be created and shared ethically?

T his question encourages students to identify and discuss potential positive applications of deepfake technology. Discuss scenarios such as education, entertainment, and philanthropy, and consider the benefits, risks, and circumstances that would justify deepfake use.

Examples to Use in the Classroom

The following links are just a few of the thousands of examples of deepfakes that exist.

# data[].attributes.attachment_link.title
Uncovering Deepfakes: Classroom Guide - AI for Education

# data[].attributes.attachment_link.description
Since 2019, the presence of Deepfakes - hyperrealistic AI-generated video, photo, and audio forgeries - has surged by 550%. This blurring of reality requires a new approach to vetting if online content is real or fake.

# data[].attributes.body
Video describing how Large Language Models, like ChatGPT scan internet texts (by "high-resource" languages) to later summarize and generate new texts.

# data[].attributes.attachment_link.title
What is AI thinking? Anthropic researchers are starting to figure it out

# data[].attributes.attachment_link.description
New research sheds light on how LLMs reason with probabilities, creatively plan ahead, and think in universal concepts—revealing their potential and their risks.

# data[].attributes.attachment_link.title
Laissez-Faire Harms: Algorithmic Biases in Generative Language Models

# data[].attributes.attachment_link.description
The rapid deployment of generative language models (LMs) has raised concerns about social biases affecting the well-being of diverse consumers. The extant literature on generative LMs has primarily examined bias via explicit identity prompting. However, prior research on bias in earlier language-based technology platforms, including search engines, has shown that discrimination can occur even when identity terms are not specified explicitly.

# data[].attributes.attachment_link.readable_text
Our Program

We support teachers and educators to run Day of AI activities in their classrooms through curriculum packages and teacher trainings, all of which are available at no cost to participants.

Developed by leading faculty and educators from MIT RAISE (Responsible AI for Social Empowerment and Education), each curriculum features a series of 30-60 minute lessons that engage kids in creative discovery, discussion, and critical thinking as they learn the fundamentals of AI, investigate its societal impacts, and bring grade-relevant applications of artificial intelligence to life through hands-on activities that are accessible to all, even for those with no computer science or technical background.

Our recommended sequence for all students is to begin with the What is AI? lessons to gain a basic understanding of this new technology. After this introduction, you are free to select the next set of lessons based on your interests or the interests of your students. The ages provided for each level should be used as a guideline only, and educators should feel free to adjust the lesson plans based on their students' abilities.

# data[].attributes.attachment_link.description
We support teachers and educators to run Day of AI activities in their classrooms through curriculum packages and teacher trainings, all of which are available at no cost to participants.

# data[].attributes.attachment_link.title
AI Empire: Unraveling the interlocking systems of oppression in generative AI’s global order - Jasmina Tacheva, Srividya Ramasubramanian, 2023

# data[].attributes.attachment_link.description
As artificial intelligence (AI) continues to captivate the collective imagination through the latest generation of generative AI models such as DALL-E and ChatG…

# data[].attributes.attachment_link.title
AI Text Generators: Sources to Stimulate Discussion among Teachers

# data[].attributes.body
What ethical considerations are not addressed in this TED video? Who do tools like Khanmigo serve and not serve?

# data[].attributes.attachment_link.title
How AI Could Save (Not Destroy) Education | Sal Khan | TED

# data[].attributes.attachment_link.title
Finn Brunton and Helen Nissenbaum: Obfuscation: a user’s guide for privacy and protest - Ethics and Information Technology

# data[].attributes.attachment_caption
Code for What? by Clifford Lee and Elisabeth Soep https://mitpress.mit.edu/9780262047456/code-for-what/