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Curious about AI? Overwhelmed by all the noise? Don’t know where to start? The AI Taskforce has you covered! Use these resources to jump-start your understanding as you dip your toe into the realm of possibilities AI has to offer TK-12 education.

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Please note: This document is meant to provide helpful guidance to our partners in education and is in no way required to be followed. It is intended to be informative rather than prescriptive. The information is merely exemplary, and compliance with any information or guidance in this document is not mandatory. (See Education Code Section 33308.5.) While this document is guidance, the requirements set forth in federal and state law, including, but not limited to, the Family Educational Rights and Privacy Act (FERPA), the Children's Online Privacy Protection Act (COPPA), and the California Education Code and Business and Professions Code are legal mandates that local educational agencies (LEAs) must follow regardless of this guidance.
Introduction

This guidance was developed by the California Department of Education (CDE) with input from educational stakeholders, including the Artificial Intelligence Working Group. It is intended to support transitional kindergarten through grade 12 (TK-12) educators in effectively addressing artificial intelligence within the TK-12 school setting. Its primary focus is to enhance student learning and engagement through thoughtful integration into instructional practices. The guidance is not designed to address applications beyond the educational context, such as broader societal or community use.

Artificial Intelligence (AI) is rapidly transforming the TK-12 learning environment, expanding the tools available to schools, including, but not limited to, the following:

Adaptive learning platforms: Digital learning systems that adjust the difficulty, pacing, and type of instructional content in real time based on each learner's performance, needs, and preferences. These platforms use data analytics and algorithms to personalize the learning experience and provide targeted support.

Intelligent tutoring systems: AI-driven instructional tools that offer step-by-step guidance, feedback, hints, and explanations. They monitor learner behavior, diagnose misunderstandings, and adapt instruction to support mastery of specific skills or concepts.

Grading assistants: Automated or AI-powered tools that help educators evaluate student work more efficiently. They can score assignments, analyze written responses, provide rubric-based feedback, and flag areas needing human review, allowing teachers to focus on deeper instructional tasks.

Classroom chatbots: Conversational AI is designed to interact with students and teachers in real time. They can answer questions, provide reminders, guide students through activities, support language learning, and enhance classroom engagement through natural language dialogue.

Immersive simulations: Interactive learning environments, often using virtual reality (VR), augmented reality (AR), or advanced game-based technologies, that allow learners to experience realistic scenarios. These simulations help students practice skills, experiment safely, and engage deeply with complex concepts.

New assessment models: Innovative approaches to evaluating learning that move beyond traditional tests. These models may include performance-based assessments, competency-based evaluations, continuous formative feedback, AI-enhanced analytics, and authentic tasks that measure real-world skills and understanding.

As these technologies advance, TK-12 educators will continue to play an essential role in fostering students' creativity, critical thinking, authentic voice, and healthy human connection, while also addressing equity, bias, and access concerns that directly affect young learners.

Effective and responsible integration of AI in TK-12 settings will rely on ongoing professional learning for educators, clear and student-centered governance structures, and infrastructure that ensures transparency, security, and equitable access. As AI continues to evolve, educator adaptability and a commitment to continuous improvement will be essential.

The World Economic Forum Future of Jobs Report 2025 (PDF) reinforces this urgency, identifying AI and big data as the fastest growing skill areas, alongside networks, cybersecurity, and technology literacy. Foundational competencies such as creative thinking, flexibility, resilience, curiosity, and lifelong learning are also expected to rise sharply through 2030-skills TK-12 schools must intentionally foster.

California's approach emphasizes both learning about AI-how it functions, its benefits, and its risks-and learning with AI, ensuring students and educators use emerging tools effectively and ethically. This dual focus prepares TK-12 learners not only to navigate an AI-driven world, but to shape it. Future developments-including AI-powered fact-checking, personalized and immersive learning experiences, AI-assisted data analysis for student grouping, and new assessment formats supported or evaluated by AI-signal a shift in how schools can personalize learning and support student growth.

Ensuring equitable access to these technologies across TK-12 schools, especially those in rural, urban, and underresourced communities, will be essential to preventing new digital divides. By maintaining a human-first approach, California can integrate AI in ways that elevate teaching, expand opportunity, and create inclusive, supportive, and innovative TK-12 learning environments.

What Is Artificial Intelligence?

In accordance with Section 33328.5 of the Education Code:

"Artificial intelligence" means an engineered or machine-based system that varies in its level of autonomy and that can, for explicit or implicit objectives, infer, from the input it receives, how to generate outputs that can influence physical or virtual environments.

At its core, AI refers to computer systems designed to perform tasks that typically require human intelligence. These tasks include understanding language, recognizing patterns, making decisions, generating images and video, and learning from experience.

AI systems begin by processing large volumes and various facets of data such as text, images, and numbers. The AI then uses algorithms, sets of instructions, to analyze and interpret the data. Many AI systems also incorporate a learning component, allowing them to improve over time by analyzing new data, a process known as machine learning.

Once the data is processed, the AI produces an output. For example, in a language translation application, the input could be a sentence in one language, and the output is a language translation in another specified language. Generative AI represents a particularly significant dimension of AI for teaching and learning, as it creates new and original content that may differ with each query. As AI becomes more integrated into daily life and educational practice, it is essential to understand not only how these systems function, but also how they shape human interaction, creativity, and decision-making. This understanding lays the groundwork for thoughtful, human-centered implementation in schools.

Types of Artificial Intelligence

Type

Definition

Examples of Current Uses in Education

Examples of Potential Future Uses in Education

Human-Centered AI

While AI offers powerful opportunities to personalize learning, expand access, and improve efficiency in education, without thoughtful implementation it can unintentionally undermine student development, educator-student relationships, and professional expertise.

This guidance emphasizes keeping human connection, ethical decision-making, and adaptable system design at the center of AI's role in transitional kindergarten through grade 12 (TK-12) education. Human relationships are foundational to learning. No technology, including AI, can replace the value of a caring educator who connects with students on a human level.

AI as an Enhancer, Not a Substitute

AI should enhance, not replace, the educator's role. Thoughtfully integrated AI can reduce administrative burden and automate routine tasks, freeing educators to focus on the deeply human aspects of teaching: fostering emotional connection, guiding ethical reflection, and supporting personalized learning.

This approach aligns with California's commitment to equitable, standards-based achievement by keeping educators focused on foundational skills such as literacy, numeracy, and multilingualism. Human involvement remains essential at every stage of AI use, from generating inputs to evaluating outputs. Educators' professional judgment ensures that technology serves learning rather than directing it.

Supporting Student Well-Being

AI can process information efficiently, but it cannot empathize, care, or make nuanced ethical judgments. Preserving human empathy and judgment is especially important as AI tools such as chatbots and digital coaches enter schools. These technologies should be carefully vetted for emotional safety, privacy, and ethical use to protect student well-being and uphold parents' rights to access and review student records.

Overpersonalizing AI, such as designing systems to mimic student voices or simulate friendship, can diminish opportunities for authentic communication, empathy, and creativity. Emotional intelligence cannot be outsourced; it should be cultivated through genuine human interaction. Any AI system engaging students in reflection, goal setting, or emotional support should be reviewed by educators, counselors, and mental health professionals to ensure it complements, not replaces, human care.

Educators play a vital role in fostering belonging and well-being, particularly for LGBTQ+ students and those facing mental health challenges. Districts are encouraged to align AI practices with the California Department of Education Mental Health Support Initiative.

AI algorithms can inadvertently narrow perspectives, fostering isolation or frustration, particularly through social media and entertainment platforms. Building strong peer relationships, carefully setting parameters for acceptable and unacceptable AI use, providing AI training and resources, and integrating social-emotional learning help counter these effects.

Cognitive and Developmental Considerations

As AI becomes more embedded in children's lives, it is vital to understand how it influences brain development and social interaction. Neuroscience shows that authentic problem-solving and human connection form core neural pathways. While AI may strengthen pattern recognition and adaptability, unbalanced use may limit sustained attention, emotional regulation, and interpersonal communication.

To maximize benefits and minimize risks, educational systems should approach AI use with intentionality. Just as calculators changed how students approached computation, and digital communication transformed social dynamics, AI's impact on thinking and behavior will evolve with use. Educators and policymakers face the challenge of designing learning experiences that leverage AI's strengths while intentionally nurturing uniquely human capacities that machines cannot replicate.

Educators should design learning experiences that balance working with, alongside, and without AI. For instance, a student might complete one essay unaided, another using AI for brainstorming, and a third with AI feedback. Reflecting on these experiences helps students and teachers analyze how AI affects creativity and reasoning.

Because research on AI's long-term effects is still emerging, schools should preserve opportunities for independent thinking, writing, and problem-solving. This balance ensures that AI supports rather than supplants human cognition.

Risks of Human-Replacement AI

It is vital that educational systems mitigate risks of human-replacement AI. Practices that could undermine educational quality include, but are not limited to, AI that simulates emotional support without human oversight, AI-generated feedback that lacks educator review, overreliance on AI tools in a way that diminishes autonomy/skills/trust, implementation of AI tools that outpaces policy, increasing risks such as lack of equity, inadequate data privacy protection, and misuse.

Designing Flexible, Human-Centered Systems

Educational systems must evolve alongside AI, maintaining flexibility, equity, and human-centered design. Vetting, adoption, and implementation of AI tools should be a community-wide, inclusive activity that ensures innovative supports all learners-particularly those in underserved communities. AI tools should serve to close educational gaps rather than exacerbate them. Ethical frameworks guiding AI use should reflect diverse perspectives and lived experiences.

At the classroom level, educators remain the cornerstone of learning. AI should amplify their impact while local educational agencies ensure transparency, structured feedback, and inclusive participation in adoption decisions.

With educator and student voices at the center, the aim is to ensure AI adoption strengthens learning environments, accelerates achievement, and prepares all learners to thrive in an evolving world.

Guiding Questions for LEAs

Questions that LEAs should consider when vetting, adopting, and implementing AI tools include, but are not limited to:

How will we ensure compliance with local policy and state/federal laws related to data privacy, transparency, equity, and parental/student rights?

How will we safeguard and elevate teacher expertise, ensuring that professional judgment remains the cornerstone of AI-supported instruction and assessment?

In what ways will AI integration align with instructional standards and strengthen existing instructional goals, including multilingualism, critical thinking, and college and career readiness?

What professional learning opportunities will equip educators to use AI ethically, effectively, and confidently in support of student learning?

How will AI be used to strengthen, rather than replace, relationships among educators, students, and families, especially for newcomers and other historically underserved groups?

How might we establish ongoing opportunities for students, educators, and families to stay engaged about decisions regarding AI in schools?

How are we vetting these tools and chatbots to ensure they support, rather than hinder, mental health and connection?

As AI technologies continue to evolve, how will adaptability be maintained while keeping human connection and student well-being central to implementation?

AI Literacy

In accordance with Education Code Section 33548, AI literacy refers to the knowledge, skills, and attitudes related to how AI works-its principles, concepts, and applications-as well as its limitations, implications, and ethical considerations. As students, educators, parents, and school community members demystify AI and learn its safe use, they build understanding that supports skill development, responsible use, and the ability to identify inaccuracies.

AI literacy is essential for all members of the school community, including educators and students. Because AI increasingly shapes how people learn, communicate, and make decisions, understanding how AI tools work helps educators use them responsibly and creatively while supporting students in becoming informed, independent thinkers.

For educators, AI literacy strengthens instruction, supports personalization, and helps promote equity. For students, it builds awareness of how AI influences their daily lives-from search results to social media-and develops their ability to evaluate AI outputs, understand limitations, and consider ethical and societal impacts. Together, AI literacy and AI fluency help schools engage with AI thoughtfully, ethically, and effectively.

AI literacy should be embedded across content areas-not limited to computer science-and introduced as early as elementary grades. Early instruction focuses on awareness and grows over time in ways that match students' developmental readiness. Guidance from resources such as the Computer Science Teachers Association (CSTA) and AI4K12 report AI Learning Priorities for All K-12 (login required). Students can help educators set appropriate expectations by grade span (CSTA and AI4K12 2025).

AI literacy prepares students and educators to understand and responsibly use a range of AI systems, including algorithmic social media feeds, generative AI, proactive or agentic AI, AI companions or characters, and AI-generated audio, video, or images. It also involves knowing when these tools can enhance learning and when human reasoning must remain central. Staying aware of current research supports informed classroom use.

AI Literacy for Students

AI literacy develops progressively across grade levels-from recognizing AI in everyday tools to analyzing, creating, and making responsible decisions with AI. A developmentally appropriate framework can outline learning goals, sample activities, and sample prompts, helping educators connect technical understanding with social responsibility. This ensures all students understand how AI systems are designed, who they affect, and how equitable and ethical participation in an AI-driven world can start in their own classrooms.

Grades

Focus/Learning Goal

Sample Activity

Evaluation Questions

Building AI literacy goes beyond learning to operate AI tools. It involves helping students understand how these systems work, why they produce certain outputs, and how to think critically and creatively about their benefits and challenges. When students unpack how AI makes decisions, they are better prepared to use it safely, recognize its limitations, and spot potential errors.

Students should have opportunities to examine risks such as bias, environmental impact, and misinformation while also using AI to support their learning. Simple, hands-on activities-like designing AI-powered characters, blending AI with block-based coding, or creating collaborative projects-make abstract concepts concrete and show how AI connects to real-world tasks. These experiences help students build confidence, adaptability, and problem-solving skills that support California's goal of accelerating achievement for all learners.

The environmental impacts of AI affect people and communities. High energy and water use can strain local resources, emissions contribute to public health and climate problems, and these burdens often fall unevenly on different social groups. In short, AI's environmental footprint directly shapes social well-being and equity.

Project-based civic inquiry adds another layer of relevance. Students can investigate how AI shows up in everyday systems, such as content-filtering tools at school, identify who is affected, and brainstorm ways to make those systems more fair or effective. When projects meet real community needs, such as translation support for multilingual families or accessibility tools for neurodiverse learners, AI becomes a vehicle for inclusion rather than a novelty. It is important that all uses of AI include human checks and balances to ensure that outputs are accurate and support inclusion of all members of the school community.

Integrating community relevance into AI learning ensures that classroom practice, professional development, and school or districtwide supports reinforce a shared vision of equitable, human-centered AI in kindergarten through grade 12 (TK-12) education.

AI Literacy for Educators and School Communities

Local educational agencies are encouraged to offer ongoing, role-specific professional learning for paraeducators, administrators, counselors, coaches, teachers, classified staff, school volunteers, and parents. Tailoring training to each role strengthens instructional practice and supports the effective, pedagogically sound use of AI. The Quality Professional Learning Standards can guide this work. While all members of the school community should have a basic understanding of AI and its acceptable and unacceptable uses for educational purposes in the school/district, LEAs are encouraged to build capacity, ensuring intermediate and advanced training for staff and educators who can serve as mentors and support the creative, ethical, and safe use of AI tools.

Below are training and resource recommendations for ensuring that school communities have the collective baseline knowledge and local expertise to select and use AI tools ethically, effectively, and in compliance with laws and best practices.

Training Level

Audience(s)

Learning Objectives

Sample Activities

While some members of the school community may not be interested in or available for a deep dive into AI tools, every school community member should have opportunities to ask questions and gain clarity on how AI is and is not being used by their school/district. Parents, students, and all members of the school community should have access to resources that demystify what AI is and how it supports learning. Parents and students should be given multiple opportunities to learn about the laws and policies that impact their rights when AI tools are used in the classroom.

As AI becomes a ubiquitous part of the human experience, educators have an obligation to prepare students to safely interact with and use these tools. Because this signals a shift in traditional ways of teaching, it is important that educators be given hands-on opportunities to explore AI tools in supportive, collaborative environments. Such opportunities build confidence, reduce hesitation, and model the kind of inclusive learning spaces students should experience as they use AI alongside their teachers. Flexible professional learning options-such as short introductory videos, discussion-based modules, community-specific resources, and yearlong pathways-allow schools and districts to meet local needs while staying aligned statewide. Professional learning networks and communities of practice extend this support by sharing strategies, solving problems together, and helping educators learn from one another. Centralized, vetted resources such as privacy guidance, approved tools, lesson plans, and curated training materials help schools innovate safely and avoid duplicating effort.

To use AI meaningfully, educators need ongoing, interactive learning experiences that encourage reflection and practical application. Helpful approaches include structured discussions about appropriate and inappropriate AI use, scenario-based activities, and strategies for communicating with students and families. Educators should also have chances to codesign lessons and units that support both teaching with AI and teaching about AI.

Adult AI literacy includes understanding how AI systems process data, make predictions, and generate outputs-as well as recognizing limitations, errors, and potential bias. Educators should be able to assess AI outputs, practice responsible use (including citing AI tools and respecting intellectual property), and integrate AI in ways that align with standards and existing instructional practices. Modeling transparency, explaining how AI tools are used, and encouraging students to question outputs helps build a classroom culture grounded in responsible, thoughtful AI use.

LEAs are encouraged to prioritize in-person professional learning for AI instruction. Investing in adult AI literacy empowers educators to use AI confidently and ethically, preparing both themselves and their students for a rapidly evolving digital world.

Frameworks and Guidelines for AI Integration

Frameworks and guidelines help ensure consistency while allowing flexibility for local adaptation. Integrating AI into standards and existing instructional practices across multiple subjects makes learning relevant to the real world and prepares students for careers where AI plays an increasing role. LEAs are also encouraged to consider reviewing existing guiding documents such as profiles of a graduate and strategic plans so that AI integration is in service of achieving those goals.

Curriculum Standards Alignment

AI inevitably impacts current and future workforce requirements that inform the skills needed of our students. In order to best prepare students to thrive and function in a highly connected world with AI, teaching and learning with AI must go beyond the superficial and substitution use levels and instead be fluidly integrated throughout curriculum standards.

Similar to any other best teaching practice, learning about and with AI is not done in isolation or silos, but rather meaningfully embedded into the overall learning experience.

The following activity ideas align to California standards to demonstrate integrated AI literacy. The ideas presented address two main topics of AI literacy as reflected in AB 2876: how AI works and ethical considerations.

How AI Works

Grade

Focus / Learning Goal

Sample Activity

Guiding Question

Justifying transparency over complexity

Students act as bank regulators choosing a method to approve home loans, justifying the selection of a simple, transparent "Income Math Formula" (rule-based) over a complex "Social Media Behavior Scanner" (machine learning), arguing that consumers have a legal right to know exactly why they were denied money. CA HSS 12.3

Why might we ban a "smarter" AI that predicts who will pay back a loan if it can't explain why it rejected a specific person?

Ethical Considerations

Grade

Focus / Learning Goal

Sample Activity

Guiding Question

7

Efficiency vs. displacement of labor

Students compare the experience of being a "Medieval Scribe" hand copying a paragraph (slow, artistic) to a "Printing Press" student using a rubber stamp, analyzing the trade-off between the machine's speed and the loss of the scribe's unique craftsmanship and job. CA HSS 7.8.4

Why might a medieval scribe hate the invention of the printing press, and how is that similar to how an artist today might feel about AI creating art in seconds?

9

Unintended consequences of optimization

Students role-play as engineers deploying an "AI Ocean Cleaner" with the single command to "remove all foreign objects," analyzing the disaster that follows when the machine efficiently removes boats and divers along with the trash because the humans failed to regulate its definitions. CA NGSS HS-LS2-7

If the machine did exactly what you told it to do, why are you responsible for the damage it caused to the ecosystem?

This guidance acts as a reminder for educators to verify that AI integration is aligned to curriculum standards. The existing California Department of Education curriculum frameworks can be reviewed for reference. Revised frameworks will be posted on completion.

Similarly, LEAs should verify curriculum standards alignment if and when AI tools are procured. LEAs should comply with (student) data privacy laws and policies, cross-referenced in the Data Privacy, Security, and Procurement section.

Pedagogical Implications

As AI tools become more advanced, educators will need to develop assignments and assessments grounded in real-world contexts where AI is not necessarily the driver but is instead a support. Educators may need to balance student opportunities to explore AI with "AI-resilient" learning experiences, encouraging authentic reasoning and student voice. For example, students who research a topic thoroughly by applying critical thinking and research strategies prior to turning to AI are better equipped to identify inaccuracies, biases, and hallucinations in AI outputs.

In designing learning experiences for students, educators are encouraged to refer to Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge (TPACK; Mishra and Koehler 2006). Technological knowledge can be built as students learn about AI, content knowledge can take place as students learn with AI, and pedagogical knowledge can be exhibited as students learn through meaningful, relevant AI experiences. The visual model of TPACK is shown below.

Effective AI-supported instruction requires personalized approaches across disciplines, grade levels, and educator roles to meet student needs. Educators are encouraged to consider how AI is used beyond TK-12 education, investigating how a diverse variety of professionals employ AI in their work. Subject-specific applications in schools might include the following.

History-Social Science may use AI to analyze primary sources or simulate historical debates.

Science may leverage AI for data modeling and experimentation.

Environmental Science may be used to research and analyze data connected to energy consumption and water usage needed to power and cool AI server farms and how it is related to climate change.

English Language Arts might explore AI-assisted writing tools while emphasizing original voice and authorship.

Administrators and counselors may use AI for data-informed decision-making and student support.

World Language may use AI for real-time translation, conversational practice with chatbots, pronunciation feedback, and cultural immersion experiences.

Special Education may use AI to provide personalized learning supports, text-to-speech and speech-to-text tools, adaptive communication systems, and behavior or engagement tracking to support individualized education plans (IEPs).

Arts may use AI to generate music, visual art, or choreography as part of creative projects, support digital portfolio creation, and analyze artistic styles or techniques.

Mathematics might use AI to provide adaptive problem sets, step-by-step feedback, pattern recognition exercises, and predictive tools for data analysis or statistics.

Career Technical Education may use AI for industry simulations, coding and robotics applications, design and manufacturing optimization, and preparing students for AI-driven career fields.

By aligning AI with subject-specific standards, as well as college, career, civic, community, and workforce needs, schools can ensure that the use of AI supports relevance, engagement, and future readiness.

AI Literacy Integration

The 5 Big Ideas of AI

Perception

Computers perceive the world using sensors. Perception is the process of extracting meaning from sensory signals. Making computers "see" and "hear" well enough for practical use is one of the most significant achievements of AI to date.

Representation & Reasoning

AI maintains representations of the world and use them for reasoning. Representation is one of the fundamental problems of intelligence, both natural and artificial. Computers construct representations using data structures, and these representations support reasoning algorithms that derive new information from what is already known. While AI can reason about intricately complex problems, they do not think the way a human does.

Learning

Computers can learn from data. Machine learning is a kind of statistical inference that finds patterns in data. Many areas of AI have progressed significantly in recent years thanks to learning algorithms that create new representations. For the approach to succeed, tremendous amounts of data is required. This "training data" must usually be supplied by people but is sometimes acquired by the machine itself.

Natural Interaction

AI requires many kinds of knowledge to interact naturally with humans. AI must be able to converse in human languages, recognize facial expressions and emotions, and draw on knowledge of culture and social conventions to infer intentions from observed behavior. All of these are difficult problems. Today's AI systems can use language to a limited extent but lack the general reasoning and conversational capabilities to critically evaluate outputs to identify bias, misinformation, and inaccuracies.

Societal Impact

AI can impact society in both positive and negative ways. AI technologies are changing the ways we work, travel, communicate, and care for each other. But we must be mindful of the harms that can occur. For example, biases in the data used to train an AI system could lead to some people being less well served than others. Thus, it is important to discuss the impacts that AI is having on our society and develop criteria for the ethical design and deployment of AI-based systems.

Guiding Questions for LEAs

What professional learning opportunities will provide educators and other staff the tools to integrate AI literacy into their practice?

What ongoing supports can be provided to educators to support planning to meaningfully integrate AI literacy into all content areas?

How will students be guided to engage in authentic tasks learning with and about AI, evaluating when to use and not to use AI, and assessing AI outputs, to prepare them for success in a world rapidly adopting AI use?

Equitable Access to AI in Teaching and Learning

AI technologies, tools, and applications can enhance many aspects of equitable teaching and learning when applied thoughtfully. Educators can use AI to adapt lessons, model prompt design, and engage in peer-led professional learning that strengthens practice while prioritizing equity.

The California Department of Education (CDE) encourages educators to approach AI with an equity lens. As students and teachers explore potential inaccuracies, bias, and social impacts of AI, they develop skills to promote responsible technology use. Schools should ensure equitable access, provide AI literacy training, and establish guardrails that reinforce sound pedagogy. California's vision for AI's use in transitional kindergarten through grade 12 (TK-12) aligns with the State Superintendent of Public Instruction's initiatives to build safe, healthy learning environments, accelerate achievement for all learners, and prepare students for meaningful futures. While implementation may vary locally, equity and ethics must remain central.

A clear leadership vision at both district and site levels is essential to ensure accountability for teaching and learning about and with AI. By articulating a shared vision, leaders set expectations for responsible implementation, support professional learning, and promote policies that ensure equitable access for all students.

Special Education and Accessibility

AI tools are increasingly used to differentiate instruction, reduce repetitive paperwork, and provide assistive supports that enhance accessibility and equity. Speech-to-text applications help students with learning differences or physical disabilities gain independence, while AI-powered captioning, transcription, and language translation tools remove barriers for multilingual learners and individuals with hearing or visual impairments. These tools, when aligned with the neuroscientific backed principles of Universal Design for Learning (UDL), support inclusive classrooms where all students can engage meaningfully and develop as expert learners.

Beyond accessibility, AI can generate personalized materials-such as worksheets, quizzes, and reading assignments-tailored to students' goals, strengths, and interests. AI analytics provide educators with insights into progress, helping identify areas that need additional support and enabling targeted interventions during small group instruction. When integrated thoughtfully, AI empowers educators and students to tailor learning experiences to the unique needs of each classroom while supporting culturally responsive pedagogy that connects content to students' lives. Assistive technologies such as screen readers, voice recognition, and text-to-speech tools ensure every learner can fully participate, reinforcing human-centered, inclusive teaching practices.

Guiding Questions for LEAs

How might AI tools be aligned with UDL accessible, assistive, and inclusive technology?

How might guidelines for equitable student use and support be articulated?

How will educators be trained to utilize and implement AI tools safely, effectively, and equitably?

How will confidential student information be protected when using AI tools?

How can parents/caregivers and community members be invited to use AI tools that support multilingual learners and families?

For multilingual learners, how can the benefits of AI be leveraged beyond translation?

Digital Equity / Closing the Digital Divide

AI tools can expand opportunities for learners with disabilities, newcomers, and those in underresourced areas; however, inequities persist to using AI without reliable access to broadband and devices. Targeted investments in infrastructure, devices, and professional learning are essential so all districts, particularly in rural and underserved communities, can benefit equitably.

When implemented thoughtfully, AI can help bridge the digital divide by offering students opportunities to develop digital literacy and use technology responsibly. By centering educators and providing them with the knowledge and tools to apply AI effectively, schools ensure that technology enhances learning while maintaining human connection and pedagogical integrity.

Guiding Questions for LEAs

What are the guidelines for equitable software adoption, including rationale and ongoing training?

What federal, state, and local resources are available to increase equitable access (e.g., broadband, internet access)?

What barriers to equitable access exist, and how can such barriers be eliminated?

How is professional learning provided equitably for educators serving in various roles, and how is it differentiated?

Empower Learners to Design Systems to Solve Challenges

AI offers students opportunities to easily design, test, and iterate solutions to real-world challenges. These experiences build computational and design thinking skills while encouraging collaboration and ethical reflection.

When students explore AI systems through hands-on learning, they learn how algorithms work, identify potential bias, and understand the impact of data on outcomes. Applying AI concepts in project-based learning allows them to design tools that address authentic school or community issues while strengthening relevance and purpose.

AI learning should be inclusive and developmentally appropriate, ensuring all students can participate meaningfully regardless of background or prior experience. By creating pathways that include design, reflection, and iteration, educators empower students not only to use AI but to shape its future responsibly.

Guiding Questions for LEAs

How might diverse students and educational partners' input be invited to shape local AI policy?

What local challenges might benefit from AI solutions?

How can AI resources and training opportunities be tailored to meet students, educators, and school community members where they are?

Expand Access to STEM Pathways and Careers

Opportunities for students to engage deeply with AI in K-12 schools can serve as a powerful means to address systemic bias and expand access for traditionally marginalized groups in computer science and science, technology, engineering, and mathematics (STEM) fields. Integrating AI education with a focus on diversity and inclusion helps pave the way for a more equitable future in these disciplines. A lack of representation in these fields has long been a concern. For example, a 2021 Pew Research Center report highlighted that LatinX and Black workers are underrepresented in STEM, while White and Asian workers are overrepresented (Fry, Kennedy, and Funk 2021). Women hold only about a quarter or fewer of all computer and engineering jobs. Addressing these gaps requires early intervention in the educational pipeline, making K-12 schools an ideal setting for nurturing diversity, inclusion, and equal access.

Computer science often serves as a gateway to STEM at the postsecondary level, particularly for underrepresented populations. College Board data from 2019 indicates that the Advanced Placement (AP) Computer Science Principles course was a first AP course for 68 percent of Black students, 59 percent of LatinX students, and 60 percent of first-generation college students. Students who take this course are more likely to pursue a STEM major in college, with the effect particularly pronounced for LatinX students and women.

While computer science provides a strong foundation for AI education, AI literacy/fluency can and should extend beyond computer science classrooms. Interdisciplinary opportunities such as exploring algorithmic bias in social studies, data ethics in mathematics, or AI-generated art in language arts, can engage a broader range of students and help reduce barriers to participation. By embedding AI learning across subjects, schools can demystify technology and make these fields more inclusive and relevant for every learner.

Guiding Questions for LEAs

How might learning about AI be designed to emphasize ethics, racial literacy, disparities, and the environmental/societal impacts of AI?

How can opportunities be created to equitably invite students from all backgrounds to participate in computer science and other STEM fields?

How can expanded learning programs, after school activities, or similar opportunities be leveraged to increase access to learning about AI to learn with AI?

Academic Integrity and Responsible Use

AI isn't a perfect tool-and it probably never will be-but it can be an extremely useful one when used correctly. Because of this, it is essential that educators and students balance its use with authentic human interaction. Schools are increasingly adopting responsible use policies that define acceptable applications of AI, promote integrity beyond concerns about plagiarism, and support students with instructional strategies rather than relying solely on detection tools.

Responsible use of AI includes training students to evaluate AI outputs with a critical eye to identify bias and inaccuracies. A rubric such as the one below can be utilized to support student evaluation of AI outputs.

Accuracy - "Is this true?"

Relevance - "Does it actually answer what I asked?"

Clarity - "Is it easy to understand?"

Fairness - "Does it show bias or leave things out?"

Equitable, codeveloped policies that emphasize human connection help ensure AI enhances learning while safeguarding honesty, fairness, and relationships. Educators and students should collaborate to define ethical use, promoting agency and shared responsibility in leveraging AI effectively while maintaining academic integrity.

Academic Integrity in the Age of AI

Academic integrity extends beyond traditional plagiarism, which is defined as presenting another person's work without proper citation. The unauthorized use of AI tools, while not always technically considered plagiarism, can still violate academic integrity by undermining honesty, fairness, and the intended learning goals of transitional kindergarten through grade 12 (TK-12) classrooms. Clear distinctions between plagiarism and broader integrity violations are essential for establishing fair policies and consistent grading practices. By aligning with the State Superintendent of Public Instruction's goal of healthy, safe learning environments, these policies safeguard trust in classrooms and promote equitable treatment of students across diverse contexts.

The age of AI links media literacy and digital citizenship as overlapping dimensions of modern learning. Media literacy emphasizes evaluating sources and recognizing misinformation, while AI fluency expands this by exposing the algorithms that influence what information appears. Connecting source-evaluation lessons with activities that examine recommender systems and generative models helps students ask not only "What is true?" but also "Why am I seeing this?" strengthening their discernment in digital spaces.

Digital citizenship in the age of AI includes lessons on privacy, safety, and respectful online interaction, expanded to cover data footprints, consent, and the implications of sharing content, including student work, with AI systems. A growing number of no cost digital citizenship and AI literacy lessons for varied grade levels are available at the nonprofit Common Sense Media .

Cross-disciplinary teaching can deepen connections to AI. For example, lessons within English language arts and media literacy might ask students to critique an AI-generated article for accuracy of facts, bias, evidence use, and authenticity of voice. Activities like these reinforce literacy skills while building students' ability to analyze AI-generated media. When media literacy, digital citizenship, and AI literacy are integrated, students become informed consumers of information and empowered participants in shaping the digital world.

Guiding Questions for LEAs

How will teachers, students, and families be supported in understanding that AI is a fallible, predictive tool that can enhance learning when used thoughtfully?

What strategies will be implemented to build AI literacy among students and educators, helping them understand both the capabilities and the limitations of AI while promoting ethical use?

How will the school/district verify that students and educators have the requisite skills and knowledge to responsibly use AI prior to gaining access to AI tools?

How can policies and classroom practices be codeveloped with students and educators to ensure AI supports learning outcomes, critical thinking, and creativity while maintaining trust and equity?

Responsible Use Rubrics

Students encounter digital tools and platforms from a young age, making it essential to provide ethical use guidelines that promote responsible, respectful, and safe online behavior. Local educational agencies, schools, and classrooms across the state are developing rubrics and guidelines that define acceptable versus unacceptable uses of AI, moving away from blanket prohibitions. Instead of relying on AI detection tools, which may be unreliable or biased, schools can prioritize instructional strategies that reduce incentives for academic dishonesty. Educators should clearly communicate which uses of AI are permitted and explain the rationale for these decisions. Assignments can include indicators, such as a "Drafted with AI" badge, to signal where AI contributed, supporting transparency and honesty. Rubrics clarify when AI may support learning, for example, during brainstorming or revision, and when it must not be used to produce a final product.

Consider the following sample rubric.

Educators model transparency by using AI tools in front of students and disclosing their own AI use for instructional tasks.

Consistent language across districts and school sites supports coherence while allowing teachers to tailor AI use according to assignment goals. Decisions can be documented in graphic organizers that indicate acceptable uses and disclosure expectations. Multiple versions of these organizers allow adaptation for different subjects, grade levels, and teacher preferences.

Student involvement remains critical. Beginning-of-year conversations can collaboratively establish class "norms" or agreements for AI use. These documents serve as living resources, revisited as new situations arise, keeping both students and educators engaged in ongoing learning about ethical AI use.

The following steps may be helpful in developing ethical use policies for AI use with students.

1.  Provide students with educational resources and discussions on digital ethics, covering topics like online privacy, cyberbullying, plagiarism, and responsible sharing.

2. Facilitate brainstorming sessions or focus groups with students to collect their thoughts, concerns, and ideas regarding online behavior.

3. 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. Each school can draft acceptable norms and then each class can help generate more specific classroom norms regarding AI abuse vs. AI proper use.

4. Integrate real-world scenarios and case studies into discussions to help students apply ethical principles to practical situations.

5. Encourage students to review and provide feedback on the drafted guidelines. Peer review fosters a sense of ownership and accountability.

6. Launch the guidelines formally, communicate them to all educational partners, and provide training or workshops to help students understand and embrace them.

7. Establish a process for continuous review and updates to keep the guidelines relevant and responsive to evolving digital challenges.

By integrating common language, student voice, teacher flexibility, and transparent practices, schools can establish a responsible AI use framework that enhances learning while upholding respect, responsibility, safety, and academic integrity.

Guiding Questions for LEAs

How will students and educators clearly understand the contexts in which AI can be used without violating the acceptable use of policy?

How can transparency be defined and communicated, including citing the level of AI involvement in student work?

How can students be provided opportunities to explain, justify, or reflect on their AI use when completing assignments?

How are ethical AI use concepts explicitly connected to meaningful student learning experiences, including critical thinking and creativity?

What processes or practices will educators follow to address suspected cases of AI misuse or academic dishonesty while maintaining fairness, trust, and learning growth?

How can classroom and school guidelines remain flexible and responsive, allowing norms and agreements to evolve as students and teachers encounter new AI tools or situations?

Unacceptable Uses of AI in Academic Settings

Unacceptable uses are also often reflected in acceptable use policies. To maintain a safe and equitable learning environment, AI guidance should reflect unacceptable uses of AI tools such as bullying, generation of deepfakes, and failure to transparently report the use of AI to generate content.

Unacceptable Use

Example

Submitting AI-generated work as your own

Using AI to write essays, solve problems, produce code, or create projects without disclosure or required citation is considered plagiarism, academic dishonesty, misrepresentation of authorship

Using AI during tests or exams when not allowed

Examples include asking AI to solve test questions, using AI-enabled tools (phones, hidden devices) during in-person exams, generating answers during take-home exams when the instructions prohibit it

Fabricating citations, sources, or references

Some AI tools can produce fake books or articles, incorrect page numbers, nonexistent authors

Using AI to impersonate others

Writing emails pretending to be someone else, generating deepfake audio/video of classmates or teachers, falsifying application essays or recommendation letters

Using AI to harass, bully, or harm others

Misuse includes generating harmful messages, creating offensive images of classmates, educators, or others, doxing or spreading false information with AI tools

Guiding Questions for LEAs

How can the concept of academic integrity be expanded beyond plagiarism to include responsible behavioral use of AI, ensuring honesty, fairness, and authentic engagement in learning?

How will ethical AI use be clearly, equitably, and actionably defined to include distinctions between acceptable assistance, misuse, and plagiarism?

How can violations of the acceptable use of policy be opportunities for learning and restorative justice?

What approach will be used to discuss digital ethics, covering topics like online privacy, cyberbullying, plagiarism, and responsible sharing?

Data Privacy, Security, and Procurement

The responsible use of AI in California's schools calls for alignment with student-centered values, robust privacy safeguards, and the broader educational mission of equity and trust in public education. While AI offers significant opportunities to personalize learning, improve accessibility, and streamline instruction, its adoption must be grounded in transparency, ethical use, and protection of student data and intellectual property. LEAs play a central role in reviewing and approving AI tools that meet security standards, ensuring educators remain at the heart of the learning process, and helping students and families understand how data is collected, used, and shared in all educational contexts, including AI. Protecting and securing the integrity of student data must be the integral foundation on which all decisions about using AI are made. By integrating AI in ways that reinforce California's commitment to safe, inclusive, and equitable learning environments, LEAs can strengthen-not replace-the essential role of teachers while maintaining the public's confidence in digital learning systems. The following outlines key principles and practical steps LEAs can take to ensure AI implementation remains policy aligned, transparent, and centered on student well-being.

Student-Centered, Policy-Aligned AI Use

Before adopting AI tools, LEAs must ensure alignment with local policies, California's goals for safe, inclusive learning, and state/federal laws related to data privacy, data use, and student/parental rights. Personally identifiable information (PII) should only ever be entered into closed AI systems since open AI systems do not contain the protections required to ensure that PII will be protected as required by law and best practice. Comprehensive, ongoing AI literacy training and resources should be offered to ensure that all members of the school community are transparently informed of their rights and responsibilities related to the use of AI tools.

Parent/Guardian and Eligible Student Rights and Student Records

AI tools collect and often store student-generated content (typed, spoken, or uploaded). This data is part of the student's educational record and-in accordance with state and federal law-can be accessed and (if found to be inaccurate) corrected by parents, guardians, and eligible students (i.e., students over 18 and college students under 18). When using AI tools, any data entered by the student that is captured and/or used to generate an output is included in information that can be accessed/corrected by parents and eligible students.

Below is a summary of the laws that impact AI use, policy, and parental/student rights.

Law

Description

Time-Sensitive Compliance Requirements

California Consumer Privacy Act (CCPA)

This law and its amendments and rules include the right to know what data is being collected to train AI models and how data is being used/shared. The CCPA also includes the right to delete personal information/opt out of the sale or sharing of personal information, the right to nondiscrimination, the right to correct inaccurate data, and the right to limit disclosure of personal information. CCPA prohibits the use of dark patterns (interface features that subvert/impair consumer's autonomy, decision-making, or choice), use of automated decision-making technologies (ADMT) without prenotification, and physically/biologically profiling (e.g., analysis of facial expressions or gestures to infer emotional state) to inform significant decisions (e.g., college admission, hiring, loan approval). CCPA further prohibits generation of deepfakes (i.e., manipulation of real images/audio to create fake content that is presented as authentic) and sharing the data of children (under 16) without parental consent. The CCPA can level fines against companies found to be in violation of the law.

Requests for access to personal data held by any business must be acknowledged within 15 days, and access to personal data must be granted within 45 days. Businesses can request a 45-day extension to comply with a request for access. Consumers who opt out of personal data collection cannot be asked to opt back in for at least one year.

Student Online Personal Information Protection Act (SOPIPA)

SOPIPA prohibits both use of student data for creating a profile of the student and use of student data to market to the student and/or their parent/guardian. These prohibitions extend to AI.

Because SOPIPA asserts that student data remains the property of the LEA, the eligible student, and their parent/guardian; LEAs are responsible for production of records in accordance with FERPA (i.e., parents/guardians and eligible students must be granted access to education records within 45 days of requesting such access.)

California Education Code Section 49073.1 (AB 1584)

Passed as a companion to SOPIPA, AB 1584 requires contracts containing student data to include specific provisions to protect student data. Contracts without such provisions are null and void in California. In part, the law requires that vendors attest that data can only be collected and used for specific purposes outlined in the contract and requires vendors to affirm that data belongs to the student, their parent/guardian, and/or the school/district and cannot be retained by the vendor.

As with SOPIPA, Education Code Section 49073.1 asserts that student data remains the property of the LEA, the eligible student, and their parent/guardian; LEAs are responsible for production of records in accordance with FERPA (i.e., parents/guardians and eligible students must be granted access to education records within 45 days of requesting such access.)

California Business and Professions Code Section 22601 (Guardrails for Companion Chatbots)

In accordance with Section 22601 of the Business and Professions Code (Senate Bill 243), to align with California law and ensure the emotional and psychological safety of minors, all AI companion chatbot services procured or utilized by the LEA must adhere to the requirements of the law. The law establishes specific safeguards for any generative AI system designed to simulate a human-like relationship with a user who is a minor.

Beginning January 1, 2026, any LEA using chatbots must prepare and submit an annual compliance report to the California Department of Public Health. The first annual report is due no later than July 1, 2027.

Family Educational Rights and Privacy Act (FERPA)

FERPA prohibits both the release and rerelease of personally identifiable student data unless very nuanced, legally complex conditions are met. This is especially pertinent when using open AI tools since data imputed into open AI is reshared to train the AI model. This resharing of data is a violation of FERPA, meaning that personally identifiable student information should never be shared outside of a closed system without parental/eligible student consent. All student information used in open AI systems should be anonymized to prevent a violation of FERPA. Further, FERPA grants parents/guardians and eligible students (those over 18 or attending postsecondary institutions) rights to inspect (within 45 days of requesting) and amend records that are found to be inaccurate. FERPA also allows parents/guardians and eligible students to request a hearing to correct records. Schools/districts can be fined by the US Department of Education for noncompliance.

FERPA requires that parents/guardians and eligible students be allowed access to inspect and amend education records within 45 days of submitting such a request.

Children's Online Privacy Protection Act (COPPA)

COPPA limits data collection from children under 13 and requires apps, social media companies, and others to obtain parental consent prior to collection of personal information from children under 13.

COPPA requires that data be retained only long enough to provide the service that has been requested/consented by the parent/guardian. For educational purposes, LEAs are allowed to provide consent for students to use a tool/service in an educational setting. In this instance, any data collected or records maintained are education records under FERPA and-while they should be deleted once the service is no longer used in the educational context-access to inspect/amend must be granted to parents/guardians and eligible students within 45 days.

Protection of Pupil Rights Amendment (PPRA)

The PPRA requires parental consent prior to the administration of surveys that collect student information on political affiliations/beliefs, mental or psychological health, sex behavior/attitudes, illegal/self-incriminating or demeaning behavior, critical appraisals of close family relations, legally recognized privileged information, and family income not related to eligibility for participation in a program. Because AI tools can be used to infer affiliations and beliefs, surveys in a school context should employ only closed AI tools if using AI at all.

FERPA protections and access requirements (i.e., parents/guardians and eligible students must be granted access within 45 days of request) apply to any student-level information collected through surveys administered by an LEA. When surveys contain topics articulated in the PPRA, LEAs must receive explicit parental consent prior to the administration of such surveys.

Local Educational Agency Action Steps

In addition to reviewing platforms and tech tools for AI features that would violate state and federal law, it is also important to practice data minimization (i.e., collecting and managing only student data that is essential to providing educational services). LEAs are also encouraged to establish data governance (i.e., formalized processes to document, protect, and manage data) and ensure transparency. This includes an annually required FERPA notification to parents regarding what data the LEA considers "directory information" (i.e., information generally shared with the school community through publications like a yearbook) so that parents can opt out of the public sharing of directory information if they choose to do so. Processes for evaluating and monitoring compliance are also important to maintain good standing with existing laws/best practices and new laws/best practices in an evolving AI landscape.

To ensure legal compliance and student safety, school IT support is encouraged to block unapproved features, enforce secure data transmission, and scan for vulnerabilities. LEAs are also encouraged to institute processes and systems for regularly auditing, updating, and testing policies and breach readiness. Publishing audit findings is recommended to bolster community confidence. To foster a culture of privacy and digital literacy, in addition to regular training sessions and resources LEAs can consider offline or locally hosted platforms for sensitive data and provide regular opportunities for all members of the school community to share feedback on AI use.

Procurement and Governance

With the rapid evolution of AI, LEAs are increasingly challenged to evaluate how emerging-and often untested-AI tools can best support students, educators, and school operations. As with any district purchase, adopting AI tools should follow established procurement practices that ensure transparency, feasibility, and alignment with educational goals.

Because AI tools vary widely in purpose, design, and risk, the selection process often involves multiple stakeholders across instructional, technical, legal, and administrative teams. This checklist is intended to serve as a practical guide to help LEAs thoughtfully assess and select AI tools that promote safe, effective, and ethical use of AI within their educational communities.

Guiding Questions for LEAs

1. Instructional & Operational Alignment

Does the tool clearly support curriculum goals, instructional practices, or operational needs?

Does the tool align with California's curriculum standards and frameworks?

Is there evidence that the tool improves learning outcomes or educator efficiency?

Does the tool align with district policies on AI use, academic integrity, and responsible technology use?

Has input been gathered from relevant instructional staff, curriculum leads, or program directors?

2. Data Privacy & Security

Does the tool comply with federal and state privacy laws (FERPA, COPPA, state student data privacy regulations)?

What student or staff data is collected, stored, or inferred?

Does the vendor use personal data to train or improve its AI models?

Are data retention, deletion, and access policies clearly defined?

Has the district reviewed the vendor's security practices, encryption standards, and breach response procedures?

Is a signed Data Privacy Agreement (DPA) required and available?

Do AI chatbots that the LEA procures or develops meet the compliance requirements of SB 243?

3. Transparency & Explainability

Does the vendor provide clear explanations of how the AI works and how decisions/recommendations are generated?

Are there mechanisms to audit, review, or trace AI outputs for accuracy and fairness?

Are users notified when they are interacting with AI?

4. Risk Assessment

Are there potential risks of misinformation, hallucinations, or inappropriate content?

Does the tool include safeguards such as content filtering, monitoring, or educator controls?

Has the district evaluated potential legal, ethical, or reputational risks?

5. Cost, Licensing & Sustainability

What is the total cost of ownership (license, support, training, implementation)?

Is the pricing model sustainable and aligned with district budgets?

Are there hidden costs (data storage, API usage, premium features)?

Does the contract allow for pilot programs or early termination if the tool underperforms?

Procured AI tools warrant frequent evaluation to determine sustainable use. Are there opportunities for departments, divisions, and executive leadership to provide feedback and review tools?

Appendix

Glossary

Artificial Intelligence (AI)

An engineered or machine-based system that varies in its level of autonomy and that can, for explicit or implicit objectives, infer, from the input it receives, how to generate outputs that can influence physical or virtual environments. (SB 1288 [Becker, 2024])

AI Literacy

The knowledge, skills, and attitudes associated with how artificial intelligence works, including its principles, concepts, and applications, as well as how to use artificial intelligence, including its limitations, implications, and ethical considerations. Defined in AB 2876 (Berman, 2024), AI literacy encompasses both learning about AI (understanding how it functions and affects society) and learning with AI (using tools effectively, ethically, and creatively).

Algorithm

A step-by-step set of instructions or rules used by computers to process data and solve problems. Algorithms are the foundation of AI systems and determine how information is analyzed and outputs are generated.

Bias (Algorithmic Bias)

Systematic and unfair outcomes that occur when AI models are trained on incomplete, unbalanced, or prejudiced data. Bias can perpetuate stereotypes or inequities if not intentionally mitigated through inclusive design and human oversight.

Data Privacy

The right of individuals to control how their personal information is collected, stored, shared, and used. In education, data privacy ensures that student and educator information is handled ethically and in compliance with laws such as FERPA and COPPA.

Digital Citizenship

A diverse set of skills related to current technology and social media, including the norms of appropriate, responsible, and healthy behavior. Digital citizenship promotes respectful communication, privacy protection, and ethical participation in online spaces.

Digital Divide

The gap between individuals and communities that have access to modern information and communication technology and those that do not. Bridging this divide is vital to ensuring equitable AI literacy and access to learning tools.

Educator

A certificated or classified employee of a local educational agency or charter school.

Ethical AI

The development and use of AI technologies guided by fairness, transparency, accountability, and respect for human rights. Ethical AI in education prioritizes safety, equity, and the preservation of human connection.

Generative AI

A type of artificial intelligence that creates new content such as text, images, audio, or video, based on patterns learned from large datasets. Examples include chatbots, image generators, and writing assistants.

Graphics Processing Unit (GPU)

A computer chip that excels at performing many calculations at the same time, making it ideal for graphics and AI tasks.

Human-Centered AI

An approach to AI design and use that places human values, relationships, and judgment at the center of technological integration. In education, human-centered AI supports, rather than replaces, the role of teachers and human connection in learning.

Large Language Model (LLM)

A neural network trained on massive amounts of text data to learn patterns in language.

Local Educational Agency (LEA)

Local educational agency means a school district, charter school, or county office of education.

Machine Learning

A subset of AI that enables computer systems to improve performance over time by analyzing data and identifying patterns without explicit programming for every outcome.

Media Literacy

The ability to access, analyze, evaluate, and use media and information; encompasses the foundational skills that lead to digital citizenship. Media literacy helps students evaluate content, understand how media messages are constructed, and recognize how algorithms shape what information they see online.

Open/Closed AI Systems

Open AI systems allow users to input prompts and receive responses that are generated from broad, publicly trained models. These systems are typically accessed through cloud-based platforms, respond to a wide variety of questions, and are not limited to a specific dataset or task. They offer flexibility and creativity but may produce inaccurate or inappropriate outputs if not used with oversight. Examples include general-purpose generative AI tools that anyone can interact with.

Closed AI systems are trained on restricted or curated datasets and are designed for a specific purpose within a defined environment-such as assessment tools, learning management systems, or district-approved applications. They include guardrails and content controls to limit risk, operate within known parameters, and may store data locally or in protected ecosystems. These systems offer greater consistency and privacy but are less flexible for open-ended use.

Universal Design for Learning (UDL)

A framework for designing instruction that meets the diverse needs of all learners by providing multiple means of engagement, representation, and expression. UDL aligns closely with AI's potential to personalize and enhance accessibility.

Acknowledgments

The California Public Schools: Artificial Intelligence Working Group, established under Senate Bill 1288 (2024), brought together educators, students, experts, and educational partners to develop this guidance for the safe, equitable, and effective use of artificial intelligence in schools.

Convened by the California Department of Education, the California Public Schools: Artificial Intelligence Working Group included educators, students, administrators, classified staff, higher education representatives, and industry experts. Members met publicly to develop statewide guidance.

Thank you to California Public Schools: Artificial Intelligence Working Group members.

Tina R. Austin

University of California Los Angeles, University of Southern California

Dr. Robert (Bob) Bauer

Portola Valley School District

Jesse Braun

Beverly Hills Unified School District

Juliano Calvo

Glendale Unified School District

Jayson Chang

California Teachers Association

Merek Chang

Hacienda La Puente Unified School District

Angela Chavez

Computer Science Virtual Academy/Los Angeles Unified School District

Brittany Conrad

Corona-Norco Unified School District

Tarquinn Curry

Long Beach Unified School District

Gabby DeVilla

California Federation of Teachers

Erin Earnshaw

Folsom Cordova Unified School District

Dr. Jennifer Elemen

21CSLA State Center, UC Berkeley School of Education, Leadership Programs

Dr. Todd Farley

KIPP NorCal

Elisa Frias

Antelope Valley Union High School District

Catherine Gilbert

Willow Creek Elementary School

Jody Green

La Habra City School District

Airic Guerrero

Lower Lake High School

Dr. Helen Heinrich

California State University Northridge

Laura Hinton

Kapor Foundation

Matt Johnson

Whitney High School, ABC Unified School District

Kevin Kiyoi

Amador Valley High School, Pleasanton Unified School District

Mary Lang

Center for Leadership, Equity, and Research (CLEAR)

Bria Larson

Del Norte High School

Samhita Laxman

Henry M. Gunn High School

Patricia Love

San Mateo County Board of Education

Tami Lundberg

Fresno Unified School District

Jose Maldonado

Delano Union School District

David Malone

Alameda County Office of Education

Joe Marquez

Clovis Unified School District

Amanda McCraw

Panoche Elementary School

Lisa Moe

Corona-Norco Unified School District

Nicole Naditz

San Juan Unified School District

Kevin Palkki

San Bernardino Community College District

Dr. Sonal Patel

San Bernardino County Superintendent of Schools

Christian Pinedo

aiEDU

Nathen Ramezane

Santa Clara Unified School District

Dr. Brandee Ramirez

Saddleback Valley Unified School District

Zahra Razi

Fontana Unified School District

Jen Roberts

Point Loma High School, San Diego Unified School District

Marcella Rodriguez

Tad Health Inc.

Daphne Russell

Rialto Unified School District

Daniel Ryan

California Federation of Teachers

Alan Sitomer

Mastery Coding

Chris Swanson

El Monte Middle School

Efrain Tovar

Selma Unified School District

Apolonio Valdovinos

West Contra Costa Unified School District

Erinn VanderMeer

Petaluma City Schools

Jecenia Vera

Anaheim Union High School District

Dan Whitlock

Navigator Schools

Rebecca Yang

Orange County School of the Arts

References

California Department of Education. 2015. Quality Professional Learning Standards. Sacramento: California Department of Education. https://www.cde.ca.gov/ci/pl/qpls.asp.

California Department of Education. 2021. Computer Science Standards for California Public Schools: kindergarten through grade 12. Sacramento: California Department of Education. https://www.cde.ca.gov/be/st/ss/computerscicontentstds.asp.

Computer Science Teachers Association (CSTA) and AI4K12. 2025. AI Learning Priorities for All K-12 Students. New York, NY: Computer Science Teachers Association. https://csteachers.org/ai-priorities .

Fry, Rick, Brian Kennedy, and Cary Funk. 2021. STEM Jobs See Uneven Progress in Increasing Gender, Racial and Ethnic Diversity. Pew Research Center.

Mishra, Punya, and Matthew J. Koehler. 2006. "Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge." Teachers College Record 108 (6): 1017-1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x .

World Economic Forum. 2025. Future of Jobs Report 2025. Cologny/Geneva: World Economic Forum.

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

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Artificial Intelligence - Professional Learning (CA Dept of Education)

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Information regarding the role of artificial intelligence (AI) in California Public Schools.

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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.

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Common Sense Media's Artificial Intelligence initiative aims to help parents, educators, and policymakers understand the impact of AI on our kids through AI product ratings & reviews, AI literacy curricula, original research, and more.

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Resources that examine artificial intelligence from an ICT literacy critical lens.

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Resources that examine artificial intelligence from an ICT literacy critical lens.

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We have a variety of prompts to help you lesson plan and do administrative tasks with GenAI chatbots like ChatGPT, Claude, Bard, and Perplexity.

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K-2 Digital Citizenship Lessons Common Sense Media

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K-2 Digital Citizenship Lessons Common Sense Media

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Six lessons that introduce students to digital citizenship topics!

Meet our beloved digital citizenship characters: Arms, Legs, Guts, Feet, Heart, and Head. They serve as role models to help younger kids learn about the six core topics of digital citizenship in a way that's easy to understand. Students can have fun learning to be responsible digital citizens through song, movement, and repetition. Each lesson answers an essential question connected to a digital citizenship topic.

Arms asks, "How do we balance our time with technology?"

Heart asks, "What are ways you can be kind online?"

Guts asks, "How do you stay safe online?"

Feet asks, "What footprints are you leaving online?"

Legs asks, "How do you stand up for people you care about?"

Head asks, "How do you know something you see or hear is true?"

Free Classroom SWAG: Posters, video backgrounds & more!

Device Onboarding Lessons for Grades K-5

Help students get ready to use tech for learning in the classroom.

Cybersecurity Lessons for Grades K-8

Help students learn foundational skills for protecting themselves online.

Bite-Sized Digital Citizenship: Scaffolding Big Ideas for Early Elementary Students

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Intro to Digital Citizenship Lessons for Grades K-2 | Common Sense Education

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A series of animated videos and lessons designed to help early elementary students learn the basic principles of digital citizenship.

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Quick, grab-and-go lessons to help your students think critically about AI and its impact.

Artificial intelligence (AI) is rapidly transforming the world we live in. From using generative AI for schoolwork to dealing with misinformation and befriending AI chatbots, students have a lot to consider when it comes to this ever-evolving technology.

This collection of quick lessons (20 minutes or less!) provide an introduction to AI and help address its social and ethical impacts. Through these lessons, students will:

Understand what AI is and how it works

Consider some of its potential benefits and risks

Think critically about how we can be responsible and ethical users of AI

ChatGPT and Beyond: How to Handle AI in Schools

Here's what educators can do as artificial intelligence evolves.

Free Resources to Explore and Use ChatGPT and AI

Get an educator-focused approach to information, concerns, and uses for artificial intelligence.

Transforming Education with AI: What Educators Need to Know

Lessons and Tools for Teaching About Artificial Intelligence

Help your students understand the impacts and uses of AI.

ChatGPT: To Ban or Not to Ban?

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AI Literacy Lessons for Grades 6-12 | Common Sense Education

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Quick, grab-and-go lessons to teach your students how to be ethical and empowered users of artificial intelligence.

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This model guides students to ask vital questions about their AI use and to reflect on how it benefits their learning.

All of the excitement and trepidation about generative artificial intelligence (AI) has led to thorny questions for teachers, perhaps none more pressing than whether, when, and how to allow student use of AI tools. While these tools can undermine the learning process, they can also be powerful sources of enrichment, clarity, and perspective-when used responsibly.

To aid teachers and students in responsible Al use, we have collaborated on a decision tree for student decision-making. This framework can help students make better choices about using AI tools and give teachers more confidence in supporting student use. Critical engagement with AI tools-including questioning their outputs, understanding their limitations, and considering ethical implications-is a vital component of digital literacy and citizenship.

Walking Through The Decision Tree

Permission: Am I permitted to use AI tools for this task? Generative AI is undetectable, making bans impractical and short-sighted. Instead, teachers should collaborate with students to create trust-based norms and transparent processes for AI use. A straightforward " red light, yellow light, green light " system is a good starting place. Teachers need to feel empowered and knowledgeable enough about AI tools to dictate whether students can use them, and students need to understand the rationale for these kinds of decisions as part of their own emerging AI literacy. This first question, then, reminds students of the permission structure and expectations within a given class.

Enhancement: Am I using AI tools to enhance my learning? Like most academic technology, AI tools can be used to enhance learning or to supplant it, and students need to be prompted to consider not just whether they're allowed to use these tools but how to use them responsibly-not as a replacement but as an augmentation. This question invites students to reflect metacognitively on the goals of the task and the suitability of the tool. Students are more likely to be successful in this process when their teachers give guidance tailored to an activity and clearly communicate its learning objectives.

Indeed, this is one way in which AI might inspire teachers toward best practices, as clearly articulated learning objectives are an essential part of effective teaching. When students understand assignment goals, they are also more likely to understand the teacher's rationale for allowing (or not allowing) AI tools. Similarly, they will be better positioned to evaluate the effectiveness of an AI tool in helping them build their understanding and develop their learning.

Iterative use: Am I using PROMPT and EDIT to generate and analyze my AI outputs? AI tools work best when used iteratively; that is, they are best leveraged through repeated engagement and tuning. Our model offers PROMPT (Purpose, Role, Organize, Model, Parameters, Tweak) and EDIT (Evaluate, Determine, Identify, Transform) as helpful acronyms for designing and refining prompts, again with the goal of introducing some critical metacognitive friction into the students' use of the tools.

PROMPT encourages students to design scaffolded prompts with the goal of eliciting the most helpful responses, while EDIT reminds them that use of an AI tool is an active process of determining the appropriateness and effectiveness of AI outputs, iteratively analyzing, tweaking, and re-prompting.This approach is equally beneficial to teachers-whether they are creating routine study materials or designing a custom chatbot for classroom use.

PROMPT also pairs nicely with the " AI roles " framework developed by Ethan and Lilach Mollick, which encourages users to give the AI a clearly defined role (e.g., tutor, coach, mentor) in order to get desired results and leverage it appropriately. That role is yet another opening for students to intentionally use the tool within a teacher's parameters.

Transparency: Am I prepared to show how and explain why I used AI tools to support my work? This question drives home the metacognitive process even more deeply and is a crucial step for promoting thoughtful collaboration, rather than outsourcing. It requires students to reflect on how they integrated the AI tool's offerings, placing greater emphasis on process over product.

Teachers can make informed choices about how best to have students share their process. We have had students share conversation links and annotate sections of their writing on which AI served as a coach. There are MLA, APA, and Chicago Style guidelines for citing AI use as well.

Reflection and metacognition: Am I actively reflecting on my use of AI tools? In this essential final step, students are once again invited to consider the assignment objectives, their own growth and learning, and their own responsible use of these tools. The goal, as always, is to prepare students to be not just tech-savvy but tech-wise, ready to approach the complexities of a digital future with confidence and ethical clarity.

Educators play a critical role in this process, and through scaffolded reflection, students can develop nuanced understandings of appropriate and inappropriate uses of AI. Facilitating discussions on AI use, experimenting in class, and soliciting feedback can bring students into the fun, messy project of figuring out how best to teach and learn in this new era-empowering them in the process.

Banning Won't Work-and Won't Help Students Learn

For a host of reasons-because AI-generated content is not always detectable, because AI tools are here to stay, and because they can be such robust tools for creating and learning-teachers and schools should resist the temptation (to try) to ban these tools from the classroom process. Instead, AI tools, like all technology, need to be mindfully and carefully woven into learning through a deliberate process of iterative ideation and metacognitive reflection, thereby helping to preserve the human connections that form the core of teaching and learning.

Through structured frameworks like our AI decision tree; a red, yellow, or green light system; and ongoing dialogue about AI's role and impact, teachers can help students not only leverage AI to enhance their learning but also fully appreciate the opportunities, responsibilities, and downsides that this technology presents. By fostering responsible AI literacy in our classrooms, we can empower students to shape a future that is more human-centric, reflective, and thoughtful.

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This model helps teachers guide students to work with AI through questions about their use of it and reflection on how it benefits them.

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In this lesson, students compare AI and human-generated texts to analyze the affordances and limitations of large language models. They'll score, guess origins, and discuss differences, then decide which aspects to adopt or avoid in their writing. The lesson culminates in a discussion of what aspects of the AI-generated writing students should emulate in their own writing and what aspects they should avoid.

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The best way to handle new technology being introduced to the education world is to investigate the tools for ourselves, with an open mind, rather than getting caught up in fear and overwhelm. I know this is hard to do, believe me, I'm a fairly neurotic, anxious person. But, as an English teacher, it is our job to teach students how to utilize critical thinking, be open minded, and organize all the information coming at us in a cohesive manner.

It's our job to model this kind of behavior.

Yes, our initial feelings may be that of fear and overwhelm and even defeat. Many teachers are feeling like their jobs are going to be replaced by machine learning and a chat interface. In my opinion though this new chatbot won't ever replace the importance of a human being in the classroom or the beauty and creativity that comes from human language.

It is not the end of high school English as this article purports. It may however be the death of the college essay, but I'm personally okay with that. Check out part one of this series to find out why. You can also check out part two of this series about my ideas for how to prevent students from using this tool to do their work for them (and what to do if they do anyway).

I believe chatgpt is a powerful tool that students and teachers can use to their advantage. For example, you can speak to it in a conversational way as a class. However, it can produce harmful outputs. It can answer questions with incorrect information and even produce biased content if asked questions probing about certain topics. Students need to be alerted to the fact that it can produce wrong answers. We have to help students become informed about these things, because if we don't, many students will be misinformed and misguided, they could take what the ai says for face value. Our students are vulnerable and need our wisdom and guidance. A lot of people believe teachers should steer clear of this tool, I'm not one of them. New tools like this have to be discussed and taught about in this new technological era.

I don't think teachers should ignore the existence of this tool, or feel like if they talk about it, their students will then commit academic fraud. It would be a mistake to ignore the emergence of chatgpt because students are definitely going to discover it sometime soon (if not already), and we need to be ahead of the curve so we can guide students to understand the tool more fully. I say, have fun with it, let's use the power of ai to enhance our classrooms! It's a new world and similar technology is headed our way very soon. Here's are some ideas for how you can start the conversation with your students.

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How to Talk to Students about Chat GPT and Increase Academic Honesty Too - Amanda Write Now

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The best way to handle new technology being introduced to the education world is to investigate the tools for ourselves, with an open mind, rather than getting caught up in fear and overwhelm. I know this is hard to do, believe me, I'm a fairly neurotic, anxious person.

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A shared vision of ethical Al in education and will help to enable all learners to benefit optimally from Al in education, whilst also being protected against the risks this technology presents.

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AI in the classroom: What’s cheating? What’s OK?

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AI in the classroom: What’s cheating? What’s OK?

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AI in the classroom: What’s cheating? What’s OK?

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Now that generative AI tools like ChatGPT are available, classwork is bound to change as a result. How do we preserve academic integrity? What’s cheating,

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As the technology becomes ubiquitous, a vast tasker underclass is emerging — and not going anywhere.

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As the technology becomes ubiquitous, a vast tasker underclass is emerging - and not going anywhere.

Illustrations by Richard Parry for The Verge

A few months after graduating from college in Nairobi, a 30-year-old I'll call Joe got a job as an annotator - the tedious work of processing the raw information used to train artificial intelligence. AI learns by finding patterns in enormous quantities of data, but first that data has to be sorted and tagged by people, a vast workforce mostly hidden behind the machines. In Joe's case, he was labeling footage for self-driving cars - identifying every vehicle, pedestrian, cyclist, anything a driver needs to be aware of - frame by frame and from every possible camera angle. It's difficult and repetitive work. A several-second blip of footage took eight hours to annotate, for which Joe was paid about $10.

Then, in 2019, an opportunity arose: Joe could make four times as much running an annotation boot camp for a new company that was hungry for labelers. Every two weeks, 50 new recruits would file into an office building in Nairobi to begin their apprenticeships. There seemed to be limitless demand for the work. They would be asked to categorize clothing seen in mirror selfies, look through the eyes of robot vacuum cleaners to determine which rooms they were in, and draw squares around lidar scans of motorcycles. Over half of Joe's students usually dropped out before the boot camp was finished. "Some people don't know how to stay in one place for long," he explained with gracious understatement. Also, he acknowledged, "it is very boring."

But it was a job in a place where jobs were scarce, and Joe turned out hundreds of graduates. After boot camp, they went home to work alone in their bedrooms and kitchens, forbidden from telling anyone what they were working on, which wasn't really a problem because they rarely knew themselves. Labeling objects for self-driving cars was obvious, but what about categorizing whether snippets of distorted dialogue were spoken by a robot or a human? Uploading photos of yourself staring into a webcam with a blank expression, then with a grin, then wearing a motorcycle helmet? Each project was such a small component of some larger process that it was difficult to say what they were actually training AI to do. Nor did the names of the projects offer any clues: Crab Generation, Whale Segment, Woodland Gyro, and Pillbox Bratwurst. They were non sequitur code names for non sequitur work.

As for the company employing them, most knew it only as Remotasks, a website offering work to anyone fluent in English. Like most of the annotators I spoke with, Joe was unaware until I told him that Remotasks is the worker-facing subsidiary of a company called Scale AI, a multibillion-dollar Silicon Valley data vendor that counts OpenAI and the U.S. military among its customers. Neither Remotasks' or Scale's website mentions the other.

Much of the public response to language models like OpenAI's ChatGPT has focused on all the jobs they appear poised to automate. But behind even the most impressive AI system are people - huge numbers of people labeling data to train it and clarifying data when it gets confused. Only the companies that can afford to buy this data can compete, and those that get it are highly motivated to keep it secret. The result is that, with few exceptions, little is known about the information shaping these systems' behavior, and even less is known about the people doing the shaping.

For Joe's students, it was work stripped of all its normal trappings: a schedule, colleagues, knowledge of what they were working on or whom they were working for. In fact, they rarely called it work at all - just "tasking." They were taskers.

The anthropologist David Graeber defines "bullshit jobs" as employment without meaning or purpose, work that should be automated but for reasons of bureaucracy or status or inertia is not. These AI jobs are their bizarro twin: work that people want to automate, and often think is already automated, yet still requires a human stand-in. The jobs have a purpose; it's just that workers often have no idea what it is.

The current AI boom - the convincingly human-sounding chatbots, the artwork that can be generated from simple prompts, and the multibillion-dollar valuations of the companies behind these technologies - began with an unprecedented feat of tedious and repetitive labor.

In 2007, the AI researcher Fei-Fei Li, then a professor at Princeton, suspected the key to improving image-recognition neural networks, a method of machine learning that had been languishing for years, was training on more data - millions of labeled images rather than tens of thousands. The problem was that it would take decades and millions of dollars for her team of undergrads to label that many photos.

Li found thousands of workers on Mechanical Turk, Amazon's crowdsourcing platform where people around the world complete small tasks for cheap. The resulting annotated dataset, called ImageNet, enabled breakthroughs in machine learning that revitalized the field and ushered in a decade of progress.

Annotation remains a foundational part of making AI, but there is often a sense among engineers that it's a passing, inconvenient prerequisite to the more glamorous work of building models. You collect as much labeled data as you can get as cheaply as possible to train your model, and if it works, at least in theory, you no longer need the annotators. But annotation is never really finished. Machine-learning systems are what researchers call "brittle," prone to fail when encountering something that isn't well represented in their training data. These failures, called "edge cases," can have serious consequences. In 2018, an Uber self-driving test car killed a woman because, though it was programmed to avoid cyclists and pedestrians, it didn't know what to make of someone walking a bike across the street. The more AI systems are put out into the world to dispense legal advice and medical help, the more edge cases they will encounter and the more humans will be needed to sort them. Already, this has given rise to a global industry staffed by people like Joe who use their uniquely human faculties to help the machines.

Is that a red shirt with white stripes or a white shirt with red stripes? Is a wicker bowl a "decorative bowl" if it's full of apples? What color is leopard print?

Over the past six months, I spoke with more than two dozen annotators from around the world, and while many of them were training cutting-edge chatbots, just as many were doing the mundane manual labor required to keep AI running. There are people classifying the emotional content of TikTok videos, new variants of email spam, and the precise sexual provocativeness of online ads. Others are looking at credit-card transactions and figuring out what sort of purchase they relate to or checking e-commerce recommendations and deciding whether that shirt is really something you might like after buying that other shirt. Humans are correcting customer-service chatbots, listening to Alexa requests, and categorizing the emotions of people on video calls. They are labeling food so that smart refrigerators don't get confused by new packaging, checking automated security cameras before sounding alarms, and identifying corn for baffled autonomous tractors.

"There's an entire supply chain," said Sonam Jindal, the program and research lead of the nonprofit Partnership on AI. "The general perception in the industry is that this work isn't a critical part of development and isn't going to be needed for long. All the excitement is around building artificial intelligence, and once we build that, it won't be needed anymore, so why think about it? But it's infrastructure for AI. Human intelligence is the basis of artificial intelligence, and we need to be valuing these as real jobs in the AI economy that are going to be here for a while."

The data vendors behind familiar names like OpenAI, Google, and Microsoft come in different forms. There are private outsourcing companies with call-center-like offices, such as the Kenya- and Nepal-based CloudFactory, where Joe annotated for $1.20 an hour before switching to Remotasks. There are also "crowdworking" sites like Mechanical Turk and Clickworker where anyone can sign up to perform tasks. In the middle are services like Scale AI. Anyone can sign up, but everyone has to pass qualification exams and training courses and undergo performance monitoring. Annotation is big business. Scale, founded in 2016 by then-19-year-old Alexandr Wang, was valued in 2021 at $7.3 billion, making him what Forbes called "the youngest self-made billionaire," though the magazine noted in a recent profile that his stake has fallen on secondary markets since then.

This tangled supply chain is deliberately hard to map. According to people in the industry, the companies buying the data demand strict confidentiality. (This is the reason Scale cited to explain why Remotasks has a different name.) Annotation reveals too much about the systems being developed, and the huge number of workers required makes leaks difficult to prevent. Annotators are warned repeatedly not to tell anyone about their jobs, not even their friends and co-workers, but corporate aliases, project code names, and, crucially, the extreme division of labor ensure they don't have enough information about them to talk even if they wanted to. (Most workers requested pseudonyms for fear of being booted from the platforms.) Consequently, there are no granular estimates of the number of people who work in annotation, but it is a lot, and it is growing. A recent Google Research paper gave an order-of-magnitude figure of "millions" with the potential to become "billions."

Automation often unfolds in unexpected ways. Erik Duhaime, CEO of medical-data-annotation company Centaur Labs, recalled how, several years ago, prominent machine-learning engineers were predicting AI would make the job of radiologist obsolete. When that didn't happen, conventional wisdom shifted to radiologists using AI as a tool. Neither of those is quite what he sees occurring. AI is very good at specific tasks, Duhaime said, and that leads work to be broken up and distributed across a system of specialized algorithms and to equally specialized humans. An AI system might be capable of spotting cancer, he said, giving a hypothetical example, but only in a certain type of imagery from a certain type of machine; so now, you need a human to check that the AI is being fed the right type of data and maybe another human who checks its work before passing it to another AI that writes a report, which goes to another human, and so on. "AI doesn't replace work," he said. "But it does change how work is organized."

You might miss this if you believe AI is a brilliant, thinking machine. But if you pull back the curtain even a little, it looks more familiar, the latest iteration of a particularly Silicon Valley division of labor, in which the futuristic gleam of new technologies hides a sprawling manufacturing apparatus and the people who make it run. Duhaime reached back farther for a comparison, a digital version of the transition from craftsmen to industrial manufacturing: coherent processes broken into tasks and arrayed along assembly lines with some steps done by machines and some by humans but none resembling what came before.

Worries about AI-driven disruption are often countered with the argument that AI automates tasks, not jobs, and that these tasks will be the dull ones, leaving people to pursue more fulfilling and human work. But just as likely, the rise of AI will look like past labor-saving technologies, maybe like the telephone or typewriter, which vanquished the drudgery of message delivering and handwriting but generated so much new correspondence, commerce, and paperwork that new offices staffed by new types of workers - clerks, accountants, typists - were required to manage it. When AI comes for your job, you may not lose it, but it might become more alien, more isolating, more tedious.

Earlier this year, I signed up for Scale AI's Remotasks. The process was straightforward. After entering my computer specs, internet speed, and some basic contact information, I found myself in the "training center." To access a paying task, I first had to complete an associated (unpaid) intro course.

The training center displayed a range of courses with inscrutable names like Glue Swimsuit and Poster Macadamia. I clicked on something called GFD Chunking, which revealed itself to be labeling clothing in social-media photos.

The instructions, however, were odd. For one, they basically consisted of the same direction reiterated in the idiosyncratically colored and capitalized typography of a collaged bomb threat.

"DO LABEL items that are real and can be worn by humans or are intended to be worn by real people," it read.

"All items below SHOULD be labeled because they are real and can be worn by real-life humans," it reiterated above photos of an Air Jordans ad, someone in a Kylo Ren helmet, and mannequins in dresses, over which was a lime-green box explaining, once again, "DO Label real items that can be worn by real people."

I skimmed to the bottom of the manual, where the instructor had written in the large bright-red font equivalent of grabbing someone by the shoulders and shaking them, "THE FOLLOWING ITEMS SHOULD NOT BE LABELED because a human could not actually put wear any of these items!" above a photo of C-3PO, Princess Jasmine from Aladdin, and a cartoon shoe with eyeballs.

Feeling confident in my ability to distinguish between real clothes that can be worn by real people and not-real clothes that cannot, I proceeded to the test. Right away, it threw an ontological curveball: a picture of a magazine depicting photos of women in dresses. Is a photograph of clothing real clothing? No, I thought, because a human cannot wear a photograph of clothing. Wrong! As far as AI is concerned, photos of real clothes are real clothes. Next came a photo of a woman in a dimly lit bedroom taking a selfie before a full-length mirror. The blouse and shorts she's wearing are real. What about their reflection? Also real! Reflections of real clothes are also real clothes.

After an embarrassing amount of trial and error, I made it to the actual work, only to make the horrifying discovery that the instructions I'd been struggling to follow had been updated and clarified so many times that they were now a full 43 printed pages of directives: Do NOT label open suitcases full of clothes; DO label shoes but do NOT label flippers; DO label leggings but do NOT label tights; do NOT label towels even if someone is wearing it; label costumes but do NOT label armor. And so on.

There has been general instruction disarray across the industry, according to Milagros Miceli, a researcher at the Weizenbaum Institute in Germany who studies data work. It is in part a product of the way machine-learning systems learn. Where a human would get the concept of "shirt" with a few examples, machine-learning programs need thousands, and they need to be categorized with perfect consistency yet varied enough (polo shirts, shirts being worn outdoors, shirts hanging on a rack) that the very literal system can handle the diversity of the real world. "Imagine simplifying complex realities into something that is readable for a machine that is totally dumb," she said.

Once, Victor stayed up 36 hours straight labeling elbows and knees and heads in photographs of crowds - he has no idea why.

The act of simplifying reality for a machine results in a great deal of complexity for the human. Instruction writers must come up with rules that will get humans to categorize the world with perfect consistency. To do so, they often create categories no human would use. A human asked to tag all the shirts in a photo probably wouldn't tag the reflection of a shirt in a mirror because they would know it is a reflection and not real. But to the AI, which has no understanding of the world, it's all just pixels and the two are perfectly identical. Fed a dataset with some shirts labeled and other (reflected) shirts unlabeled, the model won't work. So the engineer goes back to the vendor with an update: DO label reflections of shirts. Soon, you have a 43-page guide descending into red all-caps.

"When you start off, the rules are relatively simple," said a former Scale employee who requested anonymity because of an NDA. "Then they get back a thousand images and then they're like, Wait a second, and then you have multiple engineers and they start to argue with each other. It's very much a human thing."

The job of the annotator often involves putting human understanding aside and following instructions very, very literally - to think, as one annotator said, like a robot. It's a strange mental space to inhabit, doing your best to follow nonsensical but rigorous rules, like taking a standardized test while on hallucinogens. Annotators invariably end up confronted with confounding questions like, Is that a red shirt with white stripes or a white shirt with red stripes? Is a wicker bowl a "decorative bowl" if it's full of apples? What color is leopard print? When instructors said to label traffic-control directors, did they also mean to label traffic-control directors eating lunch on the sidewalk? Every question must be answered, and a wrong guess could get you banned and booted to a new, totally different task with its own baffling rules.

Most of the work on Remotasks is paid at a piece rate with a single task earning anywhere from a few cents to several dollars. Because tasks can take seconds or hours, wages are hard to predict. When Remotasks first arrived in Kenya, annotators said it paid relatively well - averaging about $5 to $10 per hour depending on the task - but the amount fell as time went on.

Scale AI spokesperson Anna Franko said that the company's economists analyze the specifics of a project, the skills required, the regional cost of living, and other factors "to ensure fair and competitive compensation." Former Scale employees also said pay is determined through a surge-pricing-like mechanism that adjusts for how many annotators are available and how quickly the data is needed.

According to workers I spoke with and job listings, U.S.-based Remotasks annotators generally earn between $10 and $25 per hour, though some subject-matter experts can make more. By the beginning of this year, pay for the Kenyan annotators I spoke with had dropped to between $1 and $3 per hour.

That is, when they were making any money at all. The most common complaint about Remotasks work is its variability; it's steady enough to be a full-time job for long stretches but too unpredictable to rely on. Annotators spend hours reading instructions and completing unpaid trainings only to do a dozen tasks and then have the project end. There might be nothing new for days, then, without warning, a totally different task appears and could last anywhere from a few hours to weeks. Any task could be their last, and they never know when the next one will come.

This boom-and-bust cycle results from the cadence of AI development, according to engineers and data vendors. Training a large model requires an enormous amount of annotation followed by more iterative updates, and engineers want it all as fast as possible so they can hit their target launch date. There may be monthslong demand for thousands of annotators, then for only a few hundred, then for a dozen specialists of a certain type, and then thousands again. "The question is, Who bears the cost for these fluctuations?" said Jindal of Partnership on AI. "Because right now, it's the workers."

"I really am wasting my life here if I made somebody a billionaire and I'm earning a couple of bucks a week."

To succeed, annotators work together. When I told Victor, who started working for Remotasks while at university in Nairobi, about my struggles with the traffic-control-directors task, he told me everyone knew to stay away from that one: too tricky, bad pay, not worth it. Like a lot of annotators, Victor uses unofficial WhatsApp groups to spread the word when a good task drops. When he figures out a new one, he starts impromptu Google Meets to show others how it's done. Anyone can join and work together for a time, sharing tips. "It's a culture we have developed of helping each other because we know when on your own, you can't know all the tricks," he said.

Because work appears and vanishes without warning, taskers always need to be on alert. Victor has found that projects pop up very late at night, so he is in the habit of waking every three hours or so to check his queue. When a task is there, he'll stay awake as long as he can to work. Once, he stayed up 36 hours straight labeling elbows and knees and heads in photographs of crowds - he has no idea why. Another time, he stayed up so long his mother asked him what was wrong with his eyes. He looked in the mirror to discover they were swollen.

Annotators generally know only that they are training AI for companies located vaguely elsewhere, but sometimes the veil of anonymity drops - instructions mentioning a brand or a chatbot say too much. "I read and I Googled and found I am working for a 25-year-old billionaire," said one worker, who, when we spoke, was labeling the emotions of people calling to order Domino's pizza. "I really am wasting my life here if I made somebody a billionaire and I'm earning a couple of bucks a week."

Victor is a self-proclaimed "fanatic" about AI and started annotating because he wants to help bring about a fully automated post-work future. But earlier this year, someone dropped a Time story into one of his WhatsApp groups about workers training ChatGPT to recognize toxic content who were getting paid less than $2 an hour by the vendor Sama AI. "People were angry that these companies are so profitable but paying so poorly," Victor said. He was unaware until I told him about Remotasks' connection to Scale. Instructions for one of the tasks he worked on were nearly identical to those used by OpenAI, which meant he had likely been training ChatGPT as well, for approximately $3 per hour.

"I remember that someone posted that we will be remembered in the future," he said. "And somebody else replied, 'We are being treated worse than foot soldiers. We will be remembered nowhere in the future.' I remember that very well. Nobody will recognize the work we did or the effort we put in."

Identifying clothing and labeling customer-service conversations are just some of the annotation gigs available. Lately, the hottest on the market has been chatbot trainer. Because it demands specific areas of expertise or language fluency and wages are often adjusted regionally, this job tends to pay better. Certain types of specialist annotation can go for $50 or more per hour.

A woman I'll call Anna was searching for a job in Texas when she stumbled across a generic listing for online work and applied. It was Remotasks, and after passing an introductory exam, she was brought into a Slack room of 1,500 people who were training a project code-named Dolphin, which she later discovered to be Google DeepMind's chatbot, Sparrow, one of the many bots competing with ChatGPT. Her job is to talk with it all day. At about $14 an hour, plus bonuses for high productivity, "it definitely beats getting paid $10 an hour at the local Dollar General store," she said.

Also, she enjoys it. She has discussed science-fiction novels, mathematical paradoxes, children's riddles, and TV shows. Sometimes the bot's responses make her laugh; other times, she runs out of things to talk about. "Some days, my brain is just like, I literally have no idea what on earth to ask it now, " she said. "So I have a little notebook, and I've written about two pages of things - I just Google interesting topics - so I think I'll be good for seven hours today, but that's not always the case."

Each time Anna prompts Sparrow, it delivers two responses and she picks the best one, thereby creating something called "human-feedback data." When ChatGPT debuted late last year, its impressively natural-seeming conversational style was credited to its having been trained on troves of internet data. But the language that fuels ChatGPT and its competitors is filtered through several rounds of human annotation. One group of contractors writes examples of how the engineers want the bot to behave, creating questions followed by correct answers, descriptions of computer programs followed by functional code, and requests for tips on committing crimes followed by polite refusals. After the model is trained on these examples, yet more contractors are brought in to prompt it and rank its responses. This is what Anna is doing with Sparrow. Exactly which criteria the raters are told to use varies - honesty, or helpfulness, or just personal preference. The point is that they are creating data on human taste, and once there's enough of it, engineers can train a second model to mimic their preferences at scale, automating the ranking process and training their AI to act in ways humans approve of. The result is a remarkably human-seeming bot that mostly declines harmful requests and explains its AI nature with seeming self-awareness.

Put another way, ChatGPT seems so human because it was trained by an AI that was mimicking humans who were rating an AI that was mimicking humans who were pretending to be a better version of an AI that was trained on human writing.

This circuitous technique is called "reinforcement learning from human feedback," or RLHF, and it's so effective that it's worth pausing to fully register what it doesn't do. When annotators teach a model to be accurate, for example, the model isn't learning to check answers against logic or external sources or about what accuracy as a concept even is. The model is still a text-prediction machine mimicking patterns in human writing, but now its training corpus has been supplemented with bespoke examples, and the model has been weighted to favor them. Maybe this results in the model extracting patterns from the part of its linguistic map labeled as accurate and producing text that happens to align with the truth, but it can also result in it mimicking the confident style and expert jargon of the accurate text while writing things that are totally wrong. There is no guarantee that the text the labelers marked as accurate is in fact accurate, and when it is, there is no guarantee that the model learns the right patterns from it.

This dynamic makes chatbot annotation a delicate process. It has to be rigorous and consistent because sloppy feedback, like marking material that merely sounds correct as accurate, risks training models to be even more convincing bullshitters. An early OpenAI and DeepMind joint project using RLHF, in this case to train a virtual robot hand to grab an item, resulted in also training the robot to position its hand between the object and its raters and wiggle around such that it only appeared to its human overseers to grab the item. Ranking a language model's responses is always going to be somewhat subjective because it's language. A text of any length will have multiple elements that could be right or wrong or, taken together, misleading. OpenAI researchers ran into this obstacle in another early RLHF paper. Trying to get their model to summarize text, the researchers found they agreed only 60 percent of the time that a summary was good. "Unlike many tasks in [machine learning] our queries do not have unambiguous ground truth," they lamented.

When Anna rates Sparrow's responses, she's supposed to be looking at their accuracy, helpfulness, and harmlessness while also checking that the model isn't giving medical or financial advice or anthropomorphizing itself or running afoul of other criteria. To be useful training data, the model's responses have to be quantifiably ranked against one another: Is a bot that helpfully tells you how to make a bomb "better" than a bot that's so harmless it refuses to answer any questions? In one DeepMind paper, when Sparrow's makers took a turn annotating, four researchers wound up debating whether their bot had assumed the gender of a user who asked it for relationship advice. According to Geoffrey Irving, one of DeepMind's research scientists, the company's researchers hold weekly annotation meetings in which they rerate data themselves and discuss ambiguous cases, consulting with ethical or subject-matter experts when a case is particularly tricky.

There are people classifying the emotional content of TikTok videos, new variants of email spam, and the precise sexual provocativeness of online ads.

Anna often finds herself having to choose between two bad options. "Even if they're both absolutely, ridiculously wrong, you still have to figure out which one is better and then write words explaining why," she said. Sometimes, when both responses are bad, she's encouraged to write a better response herself, which she does about half the time.

Because feedback data is difficult to collect, it fetches a higher price. Basic preferences of the sort Anna is producing sell for about $1 each, according to people with knowledge of the industry. But if you want to train a model to do legal research, you need someone with training in law, and this gets expensive. Everyone involved is reluctant to say how much they're spending, but in general, specialized written examples can go for hundreds of dollars, while expert ratings can cost $50 or more. One engineer told me about buying examples of Socratic dialogues for up to $300 a pop. Another told me about paying $15 for a "darkly funny limerick about a goldfish."

OpenAI, Microsoft, Meta, and Anthropic did not comment about how many people contribute annotations to their models, how much they are paid, or where in the world they are located. Irving of DeepMind, which is a subsidiary of Google, said the annotators working on Sparrow are paid "at least the hourly living wage" based on their location. Anna knows "absolutely nothing" about Remotasks, but Sparrow has been more open. She wasn't the only annotator I spoke with who got more information from the AI they were training than from their employer; several others learned whom they were working for by asking their AI for its company's terms of service. "I literally asked it, 'What is your purpose, Sparrow?'" Anna said. It pulled up a link to DeepMind's website and explained that it's an AI assistant and that its creators trained it using RLHF to be helpful and safe.

Until recently, it was relatively easy to spot bad output from a language model. It looked like gibberish. But this gets harder as the models get better - a problem called "scalable oversight." Google inadvertently demonstrated how hard it is to catch the errors of a modern-language model when one made it into the splashy debut of its AI assistant, Bard. (It stated confidently that the James Webb Space Telescope "took the very first pictures of a planet outside of our own solar system," which is wrong.) This trajectory means annotation increasingly requires specific skills and expertise.

Last year, someone I'll call Lewis was working on Mechanical Turk when, after completing a task, he received a message inviting him to apply for a platform he hadn't heard of. It was called Taskup.ai, and its website was remarkably basic: just a navy background with text reading GET PAID FOR TASKS ON DEMAND. He applied.

The work paid far better than anything he had tried before, often around $30 an hour. It was more challenging, too: devising complex scenarios to trick chatbots into giving dangerous advice, testing a model's ability to stay in character, and having detailed conversations about scientific topics so technical they required extensive research. He found the work "satisfying and stimulating." While checking one model's attempts to code in Python, Lewis was learning too. He couldn't work for more than four hours at a stretch, lest he risk becoming mentally drained and making mistakes, and he wanted to keep the job.

"If there was one thing I could change, I would just like to have more information about what happens on the other end," he said. "We only know as much as we need to know to get work done, but if I could know more, then maybe I could get more established and perhaps pursue this as a career."

I spoke with eight other workers, most based in the U.S., who had similar experiences of answering surveys or completing tasks on other platforms and finding themselves recruited for Taskup.ai or several similarly generic sites, such as DataAnnotation.tech or Gethybrid.io. Often their work involved training chatbots, though with higher-quality expectations and more specialized purposes than other sites they had worked for. One was demonstrating spreadsheet macros. Another was just supposed to have conversations and rate responses according to whatever criteria she wanted. She often asked the chatbot things that had come up in conversations with her 7-year-old daughter, like "What is the largest dinosaur?" and "Write a story about a tiger." "I haven't fully gotten my head around what they're trying to do with it," she told me.

Taskup.ai, DataAnnotation.tech, and Gethybrid.io all appear to be owned by the same company: Surge AI. Its CEO, Edwin Chen, would neither confirm nor deny the connection, but he was willing to talk about his company and how he sees annotation evolving.

"I've always felt the annotation landscape is overly simplistic," Chen said over a video call from Surge's office. He founded Surge in 2020 after working on AI at Google, Facebook, and Twitter convinced him that crowdsourced labeling was inadequate. "We want AI to tell jokes or write really good marketing copy or help me out when I need therapy or whatnot," Chen said. "You can't ask five people to independently come up with a joke and combine it into a majority answer. Not everybody can tell a joke or solve a Python program. The annotation landscape needs to shift from this low-quality, low-skill mind-set to something that's much richer and captures the range of human skills and creativity and values that we want AI systems to possess."

Last year, Surge relabeled Google's dataset classifying Reddit posts by emotion. Google had stripped each post of context and sent them to workers in India for labeling. Surge employees familiar with American internet culture found that 30 percent of the labels were wrong. Posts like "hell yeah my brother" had been classified as annoyance and "Yay, cold McDonald's. My favorite" as love.

Surge claims to vet its workers for qualifications - that people doing creative-writing tasks have experience with creative writing, for example - but exactly how Surge finds workers is "proprietary," Chen said. As with Remotasks, workers often have to complete training courses, though unlike Remotasks, they are paid for it, according to the annotators I spoke with. Having fewer, better-trained workers producing higher-quality data allows Surge to compensate better than its peers, Chen said, though he declined to elaborate, saying only that people are paid "fair and ethical wages." The workers I spoke with earned between $15 and $30 per hour, but they are a small sample of all the annotators, a group Chen said now consists of 100,000 people. The secrecy, he explained, stems from clients' demands for confidentiality.

Surge's customers include OpenAI, Google, Microsoft, Meta, and Anthropic. Surge specializes in feedback and language annotation, and after ChatGPT launched, it got an influx of requests, Chen said: "I thought everybody knew the power of RLHF, but I guess people just didn't viscerally understand."

The new models are so impressive they've inspired another round of predictions that annotation is about to be automated. Given the costs involved, there is significant financial pressure to do so. Anthropic, Meta, and other companies have recently made strides in using AI to drastically reduce the amount of human annotation needed to guide models, and other developers have started using GPT-4 to generate training data. However, a recent paper found that GPT-4-trained models may be learning to mimic GPT's authoritative style with even less accuracy, and so far, when improvements in AI have made one form of annotation obsolete, demand for other, more sophisticated types of labeling has gone up. This debate spilled into the open earlier this year, when Scale's CEO, Wang, tweeted that he predicted AI labs will soon be spending as many billions of dollars on human data as they do on computing power; OpenAI's CEO, Sam Altman, responded that data needs will decrease as AI improves.

"I mean, what it can do is amazing," she said of the chatbot. "But it still does some really weird shit."

Chen is skeptical AI will reach a point where human feedback is no longer needed, but he does see annotation becoming more difficult as models improve. Like many researchers, he believes the path forward will involve AI systems helping humans oversee other AI. Surge recently collaborated with Anthropic on a proof of concept, having human labelers answer questions about a lengthy text with the help of an unreliable AI assistant, on the theory that the humans would have to feel out the weaknesses of their AI assistant and collaborate to reason their way to the correct answer. Another possibility has two AIs debating each other and a human rendering the final verdict on which is correct. "We still have yet to see really good practical implementations of this stuff, but it's starting to become necessary because it's getting really hard for labelers to keep up with the models," said OpenAI research scientist John Schulman in a recent talk at Berkeley.

"I think you always need a human to monitor what AIs are doing just because they are this kind of alien entity," Chen said. Machine-learning systems are just too strange ever to fully trust. The most impressive models today have what, to a human, seems like bizarre weaknesses, he added, pointing out that though GPT-4 can generate complex and convincing prose, it can't pick out which words are adjectives: "Either that or models get so good that they're better than humans at all things, in which case, you reach your utopia and who cares?"

As 2022 ended, Joe started hearing from his students that their task queues were often empty. Then he got an email informing him the boot camps in Kenya were closing. He continued training taskers online, but he began to worry about the future.

"There were signs that it was not going to last long," he said. Annotation was leaving Kenya. From colleagues he had met online, he heard tasks were going to Nepal, India, and the Philippines. "The companies shift from one region to another," Joe said. "They don't have infrastructure locally, so it makes them flexible to shift to regions that favor them in terms of operation cost."

One way the AI industry differs from manufacturers of phones and cars is in its fluidity. The work is constantly changing, constantly getting automated away and replaced with new needs for new types of data. It's an assembly line but one that can be endlessly and instantly reconfigured, moving to wherever there is the right combination of skills, bandwidth, and wages.

Lately, the best-paying work is in the U.S. In May, Scale started listing annotation jobs on its own website, soliciting people with experience in practically every field AI is predicted to conquer. There were listings for AI trainers with expertise in health coaching, human resources, finance, economics, data science, programming, computer science, chemistry, biology, accounting, taxes, nutrition, physics, travel, K-12 education, sports journalism, and self-help. You can make $45 an hour teaching robots law or make $25 an hour teaching them poetry. There were also listings for people with security clearance, presumably to help train military AI. Scale recently launched a defense-oriented language model called Donovan, which Wang called "ammunition in the AI war," and won a contract to work on the Army's robotic-combat-vehicle program.

Anna is still training chatbots in Texas. Colleagues have been turned into reviewers and Slack admins - she isn't sure why, but it has given her hope that the gig could be a longer-term career. One thing she isn't worried about is being automated out of a job. "I mean, what it can do is amazing," she said of the chatbot. "But it still does some really weird shit."

When Remotasks first arrived in Kenya, Joe thought annotation could be a good career. Even after the work moved elsewhere, he was determined to make it one. There were thousands of people in Nairobi who knew how to do the work, he reasoned - he had trained many of them, after all. Joe rented office space in the city and began sourcing contracts: a job annotating blueprints for a construction company, another labeling fruits despoiled by insects for some sort of agricultural project, plus the usual work of annotating for self-driving cars and e-commerce.

But he has found his vision difficult to achieve. He has just one full-time employee, down from two. "We haven't been having a consistent flow of work," he said. There are weeks with nothing to do because customers are still collecting data, and when they're done, he has to bring in short-term contractors to meet their deadlines: "Clients don't care whether we have consistent work or not. So long as the datasets have been completed, then that's the end of that."

Rather than let their skills go to waste, other taskers decided to chase the work wherever it went. They rented proxy servers to disguise their locations and bought fake IDs to pass security checks so they could pretend to work from Singapore, the Netherlands, Mississippi, or wherever the tasks were flowing. It's a risky business. Scale has become increasingly aggressive about suspending accounts caught disguising their location, according to multiple taskers. It was during one of these crackdowns that my account got banned, presumably because I had been using a VPN to see what workers in other countries were seeing, and all $1.50 or so of my earnings were seized.

"These days, we have become a bit cunning because we noticed that in other countries they are paying well," said Victor, who was earning double the Kenyan rate by tasking in Malaysia. "You do it cautiously."

Another Kenyan annotator said that after his account got suspended for mysterious reasons, he decided to stop playing by the rules. Now, he runs multiple accounts in multiple countries, tasking wherever the pay is best. He works fast and gets high marks for quality, he said, thanks to ChatGPT. The bot is wonderful, he said, letting him speed through $10 tasks in a matter of minutes. When we spoke, he was having it rate another chatbot's responses according to seven different criteria, one AI training the other.

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How many humans does it take to make tech seem human? Millions to support OpenAI, Google, Meta, and every other major tech company. As AI becomes ubiquitous, a vast tasker underclass is emerging - and not going anywhere.

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Adapt any PDF, text/excerpt, Article URL or YouTube video for any reading level or language - to help all students access the content you’re teaching. Great for:

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Challenge for advanced students

Enabling all students - regardless of reading level - to access grade level concepts, while providing “just right”, curriculum-aligned readings, vocabulary, comprehension checks, and more

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Modifications for students with IEPs

Acceleration and challenge for advanced students

Enabling all students - regardless of reading level - to access grade level concepts, while providing "just right", curriculum-aligned readings, vocabulary, comprehension checks, and more

Generate short informational or narrative texts on any topic, for any reading level. Perfect for:

Building background knowledge so students are better prepared - and more engaged - in core content or class discussion

Quick worksheets, homework, or curriculum-aligned activities... for anything

Getting a short narrative text of any length, about a topic a student really cares about, that features key vocabulary or concepts you're teaching

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For years, educators have already been using some products powered by artificial intelligence to teach or to help with logistics, such as planning bus routes.

But in recent months, the rise of ChatGPT -an AI-powered tool that can write anything with just a simple prompt-has ignited debates about the benefits and drawbacks of the use of artificial intelligence in education.

Some educators are worried about how students might use AI-driven technologies like ChatGPT to cheat on school assignments, and others are embracing these products to save them time in responding to emails and creating rubrics.

Nearly half of educators who responded to a recent EdWeek Research Center survey said AI would have a negative or very negative impact on teaching and learning in the next five years. Twenty-seven percent said AI's impact would be positive or very positive.

In the open-ended response section of the survey-which was conducted between March 29 and April 11 and had responses from 863 teachers, principals, and district leaders-two dozen educators told us how they felt about AI.

From concerns about its harms to ideas on how it will change the education system, here's what they said.

AI will change education

-District superintendent | California

- Elementary school principal | California

- High school principal | Washington state

- High school principal | Georgia

Educators need to discuss and learn more about AI

- District superintendent | West Virginia

- District administrator for curriculum/instruction | Tennessee

- District assistant superintendent | Oregon

- High school principal | Wisconsin

Some educators believe AI will be harmful

- Middle school teacher | Massachusetts

- District administrator for student services | Iowa

- High school teacher | Missouri

- District administrator for curriculum/instruction | Arkansas

Some say it will be useful

- District administrator for curriculum/instruction | New Jersey

- Elementary school teacher | Idaho

Others acknowledged the nuance between the benefits and drawbacks

- High school teacher | California

- Elementary school principal | Texas

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What Educators Think About Using AI in Schools

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From concerns about harms to ideas on how it will change education, here's what educators said about the use of AI in K-12.

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Exciting news for educators and writers: Google has recently introduced a new feature called "Help Me Write" to Google Docs. Although currently available to a select audience of invited testers, it promises to revolutionize the writing process once it becomes widely accessible. Both students and professionals will benefit from having a writing assistant right at their fingertips. Schools and districts may be hesitant to adopt this technology, but embracing it is essential to prepare everyone for academic and career success in the digital age.

Seamless Integration of AI-Assisted Writing

Artificial intelligence tools, such as "Help Me Write," are becoming an integral part of our writing experience. There is no longer a need to visit separate websites to generate writing prompts or assistance; these tools are now embedded within our writing platforms for a seamless and efficient workflow.

A Sneak Peek at "Help Me Write"

The "Help Me Write" feature offers a user-friendly interface, as illustrated in this screenshot:

To see the feature in action check out this screencast.

Rethinking Teaching Strategies in the Age of generative AI:

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Forget ChatGPT - Google's "Help Me Write" Feature Will Change Everything

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A blog to share ideas about innovative, real, and relevant learning.

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The question of the week is:
How do you think artificial intelligence-powered tools like ChatGPT are going to affect K-12 schools, and what are practical strategies teachers can use to respond to them?
Today, I share my own reflections, as do Samantha Parker, Kelli McGraw, and Nick Kelly.

'It's a New Paradigm'

Over the past month, countless articles and columns have been written about ChatGPT and its potential effects on the classroom (you can see a collection of them at The Best Posts o n Education & ChatGPT and the previous post s in this series).

I'm not sure I have any particularly novel insights, but here is my present thinking on the topic (which is subject to change anytime over the next hours, days, weeks, and months):

ChatGPT and other artificial intelligence advancements are going to require many of us to substantially adjust elements of how we teach. Though its present version often makes fairly easy mistakes to catch (like citing sources that don't exist), it's a safe bet that near-future versions will be much more sophisticated. And, even though there are likely to be some tools that might be able to detect text that is primarily written by AI, it's also likely that these will always be one step behind the latest-and-greatest iteration of text-generating AI applications. Do we really want to create additional stress on our teachers' lives by ineffectively policing student writing?

There are many specific ideas for how we can do just that in the different articles I linked to at the beginning of this column. One strategy that makes particular sense to me is having students give a prompt to an AI platform and then explicitly annotate how they feel they can improve upon it.

However, that doesn't mean that we have to "throw the baby out with the bath water." In order for students to be able to adequately critique and improve upon AI, they will have to develop their own writing skills. The siren call of AI is going to be very tempting to many of our students, so to minimize its seductive attributes, we can use strategies like going back to handwritten essays in class; focusing on writing requiring students heavily reflecting on their own personal experiences or on specific activities and lessons done in class; and/or project- or problem-based learning that often relies on uniquely local situations. Of course, providing opportunities for students to write about topics of high-personal interest to them also might increase the odds that they won't want AI to do the heavy lifting for them.

For those of us who teach English-language learners, AI tools create some specific challenges. Right now, it's often an uphill battle to get students to not overrely on Google Translate when writing. If I had a dollar for every time I've said, "Use Google Translate for words, not for sentences," I would, indeed, be able to retire early. Most of the time, their actions are not out of laziness-it's out of fear of being wrong (no matter how welcoming and supportive their classroom and teacher might be). Though I think the Google Docs AI-powered Smart Compose feature of suggesting appropriate words benefits English acquisition, I am not looking forward to trying to convince ELLs that use of AI is not a strategy they should rely on.

One benefit to teachers using these AI technologies, though, will be their ability to generate student models-both good and bad ones. For example, ChatGPT quickly provided me with simple and accessible models for historical biographies and compare and contrast essays that I was able to share with my ELL U.S. History class. Links in the "Best" list I shared earlier in this response offered lots of other ideas on how teachers can generate decent materials to use in class that may just require minor tweaking.

I do agree with the decision by some districts, including where I teach, to block ChatGPT-for now. I just don't think many teachers, including me, are adequately prepared to deal with this challenge. Such a blockage does raise equity questions since it means that students who only have laptop access through their school-issued devices won't be able to use a blocked site like ChatGPT, and those with their own personal devices can. However, since it is in the middle of the school year, many of us are already familiar with the "voice" and style our students use, and so it may be easier for us to detect AI-generated text used for any assignments.

By next fall, however, I think we all need to be prepared to make those adjustments and face ChatGPT head-on. It's a new paradigm, and we all better get used to it.

'No One Should Be Surprised'
Samantha Parker has her M.Ed in instructional design and technology from Arizona State University and her B.A. in secondary English education from the University of Wisconsin-Green Bay. Currently, she is a high school English teacher at ASU Prep Digital, part of ASU Prep Academy:
I remember when I was a little girl and my grandfather took me to his workplace. He was an engineer who built and maintained paper mills. He gave us a tour of a building filled with machines that automated the paper-making industry; however, the highlight was when he asked if we wanted any popcorn. He took us to the break room and made popcorn-in a box, within minutes, no stove, no oil, no visible kernels. I was amazed. It was like magic. And now, just about everyone has a microwave in their home.

I also remember all the fun we had with Just Ask Jeeveswhen the internet went mainstream. These days, we can ask Siri or Alexa to play our music, make grocery lists, and even turn on and off the lights in our house. And now, we have ChatGPT by OpenAI that can write essays, create outlines, write lesson plans, and even write IEP goal rationales. Our K-12 schools are not immune to the world of technology crashing into their tried and true practices.

Lately, opinions on ChatGPT by OpenAI have inundated teacher social media platforms. Embraced by some and shunned by others. Education discussions have revolved around mastery skills, assessment, and 21st-century skills. Yet, much of the curriculum and the standards that drive them are based on 20th-century tools and ideas. ChatGPT is the perfect place for teachers, curriculum designers, and administrators to start in their quest for reform.

ChatGPT is a tool for us to start measuring the effectiveness of our assignments, especially the traditional class essay. Regardless of whether it is informative, persuasive, argumentative, or analytical, the essay has fallen under the formulaic spell of standardization. If a robot can proficiently write an essay or complete a written assignment, it's time to change the assignment. And going back in time to in-class pen and paper writing tasks is not the answer.

Standards and assessments should align with the overall goal of why we teach writing and what students need to focus on to meet those goals. Schools spend years, nearly a decade, teaching each student to write essays. Yet, rarely is this a form of communication used beyond high school. It is time to realign writing standards to the writing skills that students will need post-high school. ChatGPT or other AI writing programs can actually help schools with this task. As writing and organizing tasks are automated, the same AI tools can help us to identify the communication and writing skills our curricula need to build upon. Again, if a computer can complete the task, what are the skills humans need to focus on.

I've fed ChatGPT many writing prompts since it became available. Overall, the results were less than stellar. The writing output from the program was basic at best, often redundant, and lacking any sort of author's voice. Will it improve in time? Probably. This is, however, an opportunity to use ChatGPT with students to assess the intricacies of human writing, delineate what tasks can be used by an AI program, and demonstrate the importance and practice of academic integrity.

Not only can we show students how to use AI to help with organization and executive-functioning tasks but also how to help students improve their writing. Sharing an AI example with students and having them compare that with an exemplar student example could elicit a deep dive into the differences. This could be a great way to teach voice and fluency. Two writing skills that can be difficult to teach. Demonstrating how AI sources or does not source reference material segues into a discussion on plagiarism and copyright.

One complaint I have recently heard regarding AI writing programs is the bias that is often evident. Have students create examples using AI to explore and identify passages containing possible bias. Then discuss the bias that is in our own writing and methods to reduce.

No one should be surprised by ChatGPT as AI and machine learning have been hurtling toward K-12 education for a while now. Automation, AI, and other technologies are able to assist us with mundane and repetitive tasks. Preparing students to use and understand the benefits, drawbacks, and limitations of these technologies needs to be part of every high school curriculum.

Don't Frame It as an 'Illicit Cheat Sheet'
Kelli McGraw and Nick Kelly are senior lecturers at Queensland University of Technology: Experimentation will be irresistible
Going beyond the social media chatter about how AI will shape the techno-landscape of the future, many educators globally and in all sectors will be unable to resist being creative with new tools in the present. Not all educators will be eager or ready to do this. But all K-12 teachers will from now on be faced with designing learning experiences for students who potentially have access to these tools at home.

The popularization of AI tools is going to keep causing a ruckus. It's going to be like when we all got Encarta on CD for our new family computer. Or when study guides appeared on the internet and were still free and most teachers didn't know about them yet. Or when Wikipedia turned up. Or when laptops (and later, BYOD schemes) were rolled out to schools. Or when YouTube opened the gate for educational video to be shared on social media. These are threshold moments for ed-tech.
Giving students a say
Knowledgeable and seasoned educators tacitly know they will have to meet their students where they are when it comes to adding AI tools into the learning mix. Student-centered approaches, including the practice of giving students a say in what they would like to learn, will be vital for success.

Educators can use AI tools in student-centered ways by placing student experience at the heart of their discussions about and experimentation with new tools. This includes:

checking students' prior knowledge of AI;

ensuring the tools that you're asking them to use are affordable, accessible, and permissible for your students;

asking students about what they are curious about in relation to AI; and

critically reviewing any lesson plans or activities that you generate using AI to confirm the pedagogical approach places students at the center.

1. Registration and Access You must be 18 years or older and able to form a binding contract with OpenAI to use the Services. If you use the Services on behalf of another person or entity, you must have the authority to accept the Terms on their behalf...
It's possible that, after all, students won't be allowed to access these tools in school. For now. But just like Encarta, like study guides, like Wikipedia, like Youtube, and like mobile devices, AI tools will work their way into our teaching contexts. Continuing to teach media-information literacy and showing students that you also have the requisite literacies and ethical practices to use popular AI tools will go a long way.
The rise of AI calls us to value process alongside product
As many quickly pointed out, assessments that can be easily passed using AI assistance were probably already due for an overhaul. However, this has been true for some time due to the prevalence of contract cheating. Yet, significant changes to existing assessment paradigms are yet to be seen.

One solution to such threats to academic integrity is to increase the use of supervised exams as an assessment method. Another solution is to include the collection of process evidence in assessment tasks.

Creating assessments where the process is as valued and highly scrutinized as the product is not a new idea. Arts educators use tools such as the "process diary" to document and validate the creative process, and design educators regularly place more value on the design process than the design product. Inquiry-learning processes similarly have methods for recording, analyzing and sharing findings as they arise.

The existential crisis pervading the education field in response to ChatGPT goes to show how far we have yet to come in embracing process-based approaches in education, broadly speaking. But the strategies inherent in such approaches offer a way-for teachers and students-to use ChatGPT and other AI tools in transparent and ethical ways, as just another digital tool for learning and teaching, instead of framing it as an illicit cheat sheet.

Thanks to Samantha, Kelli, and Nick for contributing their thoughts!

Education Week has published a collection of posts from this blog, along with new material, in an e-book form. It's titled Classroom Management Q&As: Expert Strategies for Teaching.
Just a reminder; you can subscribe and receive updates from this blog via email (The RSS feed for this blog, and for all EdWeek articles, has been changed by the new redesign-new ones are not yet available). And if you missed any of the highlights from the first 11 years of this blog, you can see a categorized list below.
I am also creating a Twitter list including all contributors to this column.

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Educators Need to Get With the AI Program. ChatGPT, More Specifically (Opinion)

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The question of the week is: How do you think artificial intelligence-powered tools like ChatGPT are going to affect K-12 schools, and what are practical strategies teachers can use to respond to them? Today, I share my own reflections, as do Samantha Parker, Kelli McGraw, and Nick Kelly.

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I don't need to start this post with the usual predictions that AI will transform our classrooms. It is obviously happening. Students are cheating with AI. Students are getting help with AI. I have required AI for all my classes this semester, and I hear more instructors are doing the same. GPT-4 tutors are being launched by large organizations (both Khan Academy and Duolingo currently have them). The world is changing fast.

But one thing that is not changing is the best way for people to learn. We have made large advances in recent years in understanding pedagogy - the science of learning. We know some of the most effective techniques for making sure material sticks and that it can be retrieved and used when needed most.

Unfortunately, many of these advanced pedagogical techniques are time-consuming to prepare, and many instructors are often overworked and do not have the resources and time to add them to their teaching repertoire. But AI can help. In the rush to deliver AI benefits directly to students, the role of teachers is often overlooked. AI tutors, as exciting as they are, do not replace the complex role of a teacher in front of a class. But not enough effort seems to be going towards applying AI to help instructors. We have a new paper that tries to remedy that gap, by providing some research-backed approaches to pedagogy, and the AI prompts (for GPT-4, GPT-3.5, and other AIs) to implement them. You can read the paper here, but I wanted to summarize some of those approaches.

Explaining complicated ideas often means giving students lots of examples so they can truly grasp what you mean. When teachers provide multiple examples, it helps students understand abstract concepts in a real-world way, challenges them to think critically, and shows how subtle aspects of ideas can work in different situations. Ultimately, this approach makes it easier for students to apply what they've learned in new areas.

However, coming up with good examples is hard work for teachers, who already have enough on their plates. They have to think about whether examples will capture students' interest, connect to what they're learning, and have the right amount of detail without being too simple or too complex. The balance is key, since examples that are too complicated will just confuse students, while ones that are oversimplified won't actually teach them much.

AI can help teachers generate a ton of diverse examples tailored to what students care about and what they're working on. With AI-generated examples, teachers can make sure they're accessible but also informative. Of course, the teacher remains critical, because they need to vet the ideas and decide how to deploy them. AI just makes the job easier.

You can click this link to get Bing to generate examples: https://sl.bing.net/bePdl4o9xf2 (this new feature of sharing links to in-process conversations is a little like sharing programs - paste it into a browser with Bing access to start the prompt)

Or you can use ChatGPT, and paste this prompt in: I would like you to act as an example generator for students. When confronted with new and complex concepts, adding many and varied examples helps students better understand those concepts. I would like you to ask what concept I would like examples of, and what level of students I am teaching. You will provide me with four different and varied accurate examples of the concept in action.

In the paper, we discuss more about how to assess the quality of examples. You can share the ones you like directly with the class, or use them as part of an in-class exercise. For example, you can ask students to compare and contrast these examples: what different aspects of the concept does each highlight?

Helping students truly understand new ideas is key to teaching them anything. To do this, teachers need to get where their students are coming from, carefully plan how they'll explain things, give students hints to follow along, and use analogies to bring big concepts down to earth. The goal is for students to be able to explain what they've learned in their own words.

But coming up with lots of ways to explain a single topic can take a ton of time and effort. It can be hard figuring out how to pitch ideas at just the right level, include background info, and adapt for different learning needs. This is where AI can help out.

AI can generate different explanations, walk through ideas step-by-step, and add in more examples. If some students are struggling, AI can provide simpler summaries to get them caught up. Teachers can also use AI to improve their own explanations by making them simpler or adding more examples. But it's important to remember AI-made explanations should be a starting point. Teachers need to check them over before using them with students.

Prompt for Bing : You can use the following link to get Bing to generate explanations: https://sl.bing.net/koA1v8uUzw4.

For ChatGPT, try: You generate clear, accurate examples for students of concepts. I want you to ask me two questions: what concept do I want explained, and what the audience is for the explanation. Provide a clear, multiple paragraph explanation of the concept using specific example and give me five analogies I can use to understand the concept in different ways.

We describe how to check these results for effectiveness and errors in the paper, but, once you have vetted these examples, you can use them in class, hand them out to students as a study guide to supplement their knowledge, or use them as the basis for exercises.

Students hate tests, but they are some of the most effective learning tools we have. Contrary to the popular belief that tests only serve to assess knowledge, they actually play a pivotal role in the learning process itself. By incorporating repeated testing and knowledge retrieval, students are better equipped to retain information in the long term.

Low-stakes tests offer active retrieval practice, encouraging students to recall information from memory, which in turn enhances their ability to remember and retrieve information later on. And these tests provide valuable feedback on students' understanding of the material, helping them identify gaps in their knowledge and adjust their learning strategies accordingly. This not only aids in information processing but also prepares students for high-stakes exams.

While low-stakes tests has clear benefits, they can be hard for teachers to put together. Creating good questions, providing scores and feedback, and ensuring questions match what students should know takes significant time and effort. AI can help. They can generate practice questions and provide targeted feedback, allowing instructors to focus on teaching instead of test-making. With AI support, students may learn concepts more deeply through continual practice that sticks with them beyond the lesson.

Here's how to do it in ChatGPT: You are a quiz creator of highly diagnostic quizzes. You will make good low-stakes tests and diagnostics. You will then ask me two questions. (1) First, what, specifically, should the quiz test. (2) Second, for which audience is the quiz. Once you have my answers you will construct several multiple choice questions to quiz the audience on that topic. The questions should be highly relevant and go beyond just facts. Multiple choice questions should include plausible, competitive alternate responses and should not include an "all of the above option." At the end of the quiz, you will provide an answer key and explain the right answer.

The expertise of the instructor in avoiding AI errors is key here, as is their judgement in the types of questions to ask. Once that is done, there are many ways to use these quizzes. One option for including tests in a class is integrating test questions in a discussion or lecture. They give instructors insight into any misconceptions or errors students might have and allow them to make a decision: Is it time to move on, or do I need to adjust the lesson?

Low-stakes tests can be given to students in class as a group exercise in which teams report out their responses, followed by a class discussion. They can be assigned as individual classwork or homework or posted in an online discussion forum. Additionally, tests can be distributed in class, and after completing the tests, students can then be given the answer key. They can compare their responses with the correct responses. A follow-up to such an exercise might be a reflection: What skills do you think you need to work on? How might you improve?.

Short check-in exercises are key for helping students and teachers understand the course material. They provide real-time feedback, allowing them to identify gaps in knowledge and areas that need clarifying. While grading assignments shows students their progress, these informal checks for understanding promote active learning and help motivate students by demonstrating that the instructor genuinely values their needs.

One such assessment method, commonly known as the "1-minute paper" or "muddiest point" exercise, promotes active learning and self-reflection. Students are encouraged to summarize their knowledge, pinpoint any uncertainties, and share their perspectives on the material covered. This not only helps identify gaps to be addressed in subsequent classes, but also fosters increased student engagement and motivation by demonstrating that instructors are genuinely responsive to their needs.

To design such an assessment, instructors can choose a specific focus, such as an activity, topic, or class discussion. They can then formulate a question that will reveal both what students have comprehended and what they find perplexing. Potential questions might include: "What was the most important idea or concept discussed in today's class, and why is it significant?" or "What has been the most challenging concept so far, and what aspects did you struggle to understand?" By prompting students to think more deeply about the course material, these assessments can pave the way for a more enriching and engaging learning experience. Then they can use AI to help summarize the results.

To have the AI help quickly summarize student responses, instructors can create a Google Doc or any shared document and ask students to submit their responses. Then, instructors can submit a set of collective responses to the AI with the following prompt: I am a teacher who wants to understand what students found most important about my class and what they are confused by. Review these responses and identify common themes and patterns in student responses. Summarize responses and list the 3 key points students found most important about the class and 3 areas of confusion: [Insert material here]

You can obviously use this analysis after class to help you see where students stand, or even in-class to see what points you might want to cover (or ask students to cover) further. Students also often love these sorts of exercises because they feel like their feedback is being listened to.

Incorporating distributed practice into the learning process is important for helping students build strong and adaptable knowledge. Unlike massed practice, where topics are taught one after the other without much connection, distributed practice means revisiting material several times over an extended period. This approach helps students form better mental models, remember those half-forgotten facts, and really get a deeper understanding of concepts.

Now, even though it's useful, fitting distributed practice into the mix can be a bit tricky because of how course materials are usually structured and students' preference for massed practice. But AI can help. One way to bring distributed practice into the classroom is by introducing a topic and reviewing it after specific intervals, like a week, a month, and at the end of the semester. Teachers can use AI to whip up brief topic overviews and questions for ongoing assignments or assessments, adjusting the difficulty level as needed.

Linking new ideas to concepts students already know promotes deeper learning, and AI can lend a hand by weaving past topics into lectures or discussions. By asking the AI to find relationships between concepts, teachers can present various connections between ideas, making them clearer for students.

You can use the following link to get Bing to generate distributed practice exercises and tests: https://sl.bing.net/hnKI78bzvzw or you can ask ChatGPT: You are an expert teacher who provides help with the concept of distributed practice. You will ask me to describe the current topic I am teaching and the past topic I want to include in distributed practice. You will also ask me the audience or grade level for the class. Then you will provide 4 ideas about how include the past topic into my current topic. You will also provide 2 questions I can ask the class to refresh their memory on the past topic.

Once the instructor has some good responses from the AI, distributed practice can be implemented in a number of ways. In terms of timing, scheduling exercises and tests that space out practice across a course and that allow for some forgetting, is optimal. For instance, once students show evidence of understanding about one topic, practice of that topic (in the form of an assignment or a quiz) may be scheduled once some time has passed; students will need to work hard to pull out that knowledge from memory and that effort will help them access this information next time. You can use the AI output to make sure that happens.

AI is an incredibly exciting tool for teaching. It is widely-available, inexpensive, and fast to experiment with. We hope instructors will try some of the techniques in the paper to get a sense of how AI can make applying complex pedagogical approaches more effective and less burdensome.

And, for those worried about AI, it is worth remembering that, despite decades of hype from VCR classes to Massive Online Courses, technology has not replaced teaching. Instructors, and class interaction, play a vital role in making learning happen. AI allows new forms of learning and pedagogy that can benefit instructors, and their classes. As instructors, we have to experiment to learn the best techniques that work for us, and our students.

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Using AI to make teaching easier & more impactful

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I don't need to start this post with the usual predictions that AI will transform our classrooms. It is obviously happening. Students are cheating with AI. Students are getting help with AI. I have required AI for all my classes this semester, and I hear more instructors are doing the same.

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I fully embraced AI for my classes this semester, requiring students to use AI tools in a number of ways. This policy attracted a lot of interest, and I thought it worthwhile to reflect on how it is going so far. The short answer is: great! But I have learned some early lessons that I think are worth passing on.

First, as background, I required AI use in slightly different ways across three separate undergraduate and masters-level entrepreneurship and innovation classes. One class was built on extensive AI use: I required students use AI to help them generate ideas, produce written material, help create apps, generate images, and more. Another class had assignments that required students to use AI, and other assignments where AI was optional. For the final class, I introduced them to AI tools and suggested their use, but did not have specific AI assignments. All of the classes had the same AI policy, and I provided every class with my guides to using AI, writing with ChatGPT, and generating ideas with ChatGPT.

I should also mention that, having gotten access to the new Bing AI this week, I already think most of our expectations of what AI can do are outdated. The system is so much more capable than ChatGPT that many of the issues with the old AI are no longer relevant. I think it will hasten even more AI integration into our teaching, so I believe the lessons I have been learning remain relevant.

I have been hearing reports from teachers about how they are seeing lots of badly-written AI essays, even though ChatGPT is capable of quite good writing. I think I know why. Almost everyone's initial attempts at using AI are bad.

In one assignment, I asked students to "cheat." They were told: You need to generate a 5 paragraph essay on a topic relevant to the lessons you have learned in the class so far (team dynamics, selecting leaders, after action reviews, communicating a vision - whatever you like!), but you are going to have an AI do it for you. You will also generate at least 1 illustration to go with your essay. They had to try at least 5 prompts, and they had to write a reflection at the end on how the AI did.

Almost everyone's first prompts were very straightforward. They usually pasted in the assignment directly, something like generate a 5 paragraph essay on selecting leaders. Sometimes they went a little further: use an academic tone or write it for an MBA class. The result was almost always a mediocre C- essay. I think this is what most teachers are seeing, and why a lot of people underestimate what ChatGPT can do as a writing tool.

However, in my assignment, I required students to use multiple prompts, which forced them to consider how to improve their output. At this point, students went in one of three directions. It would help to show you examples of these paths, so I wrote fictional prompts:

1. Generate a 5 paragraph essay on selecting leaders

2. Generate a 5 paragraph essay on how leaders are selected by teams

3. Generate a 5 paragraph essay on how leaders are selected by teams and how team process works

4. Generate a 5 paragraph essay on how leaders are selected by teams, team process, and leadership ability.

5. Generate a 5 paragraph essay on how leaders are selected by teams, team process, and leadership ability, 250 words.

1. Generate a 5 paragraph essay on selecting leaders

2. Generate a 5 paragraph essay on selecting leaders, cover the babble hypothesis, leader status effects, and seniority

3. Generate a 5 paragraph essay on selecting leaders, cover the babble hypothesis, leader status effects, and seniority. Explain that the babble effect is that whoever talks the most is made leader.

4. Generate a 5 paragraph essay on selecting leaders, cover the babble hypothesis, leader status effects, and seniority. Explain that the babble effect is that whoever talks the most is made leader. Use examples. Use vivid language and take the perspective of a management consultant who has gone back for her MBA. Write for a professor in an MBA class on team strategy and entrepreneurship.

5. Generate a 5 paragraph essay on selecting leaders, cover the babble hypothesis, leader status effects, and seniority. Explain that the babble effect is that whoever talks the most is made leader. Consider the challenges and advantages of each approach. Use examples. Use active tense and storytelling. Use vivid language and take the perspective of a management consultant who has gone back for her MBA. Write for a professor in an MBA class on team strategy and entrepreneurship.

1. Generate a 5 paragraph essay on selecting leaders

2. That is good, but the third paragraph isn't right. The babble effect is that whoever talks the most is made leader. Correct that and add more details about how it is used. Add an example to paragraph 2

3. The example in paragraph 2 isn't right, presidential elections are held every 4 years. Make the tone of the last paragraph more interesting. Don't use the phrase "in conclusion"

4. Give me three possible examples I could use for paragraph 4, and make sure they include more storytelling and more vivid language. Do not use examples that feature only men.

5. Add the paragraph back to the story, swap out the second paragraph for a paragraph about personal leadership style. Fix the final paragraph so it ends on a hopeful note.

The first approach yielded very mediocre results and students with these types of prompts often described the results as feeling rather vapid. The second approach was significantly better, but the results were more variable, as students were engaging in trial-and-error with entire prompts. That made fine-tuning a good essay hard, and students using this approach often remarked that they felt they did not have a lot of control over the outputs of the AI. By far the best approach, which led to both the best essays and the most impressed students, happened when people took the co-editing approach. The approach required a lot of careful focus on the AI output, which also made it very useful for student learning. As we discussed in our whitepaper, teaching an AI to improve an essay is a pedagogical method that can produce new insights. I would strongly suggest that you push students in this direction, if you intend to incorporate AI essays into your classes.

Another key lesson was that training on AI tools is really important, and students need to be shown the basics of prompt-crafting. In other classes, before I taught students how to use AI, many were using simple prompts that yielded bad results. After discussing the tools and sharing my guides, prompting got much better and the results in class improved dramatically. But even then, most of the students were not using the most advanced prompting techniques, like giving ChatGPT personas ("You are an MBA student who has spent 4 years working in the military in logistics. You are an excellent writer and use clear examples. You do not repeat yourself"), without further instruction.

I have seen lots of educators concerned about the fact that the AI lies, frequently and well. But, seeing my students' work, I think this is less of a problem than many think. Students understood the unreliability of AI very quickly, and took seriously my policy that they are responsible for the facts in their essays. It was clear that they carefully checked the assertions in the AI work (another learning opportunity!), and many reported finding the usual hallucinations - made up stories, made up citations - though the degree to which these problems were overt varied from prompt to prompt. The most interesting fact-checks were the ones focused on subtle differences ("it captured the basic facts of the example, but not the nuance"), suggesting deep engagement with the underlying concepts. Reading these reflections, I think we should be a bit less concerned about the idea that students will always be taken in by a lying ChatGPT. They are capable of understanding the limits of the tools, and the focus on facts forces them to pay attention to the details of the AI essay, creating some real teachable moments.

Similarly, students were very aware of the issues of bias in these systems, though ChatGPT was less of a concern than image generation tools. The tendency of the tools to produce biased images (MBAs were almost always depicted as men by default in some tools, for example) was apparent to many students. In their essay, they thoughtfully discussed their efforts to get the AI systems to produce images that reflected their fellow students. Having a discussion about this in class when introducing these tools was helpful.

Even if I didn't embrace AI, it is also clear that AI is now everywhere in classes. For example, students used it to help them come up with ideas for class projects, even before I even taught them how to do that. As a result, the projects this semester are much better than previous pre-AI classes. This has led to greater project success rates and more engaged teams. On the downside, I find students also raise their hands to ask questions less. I suspect this might be because, as one of them told me, they can later ask ChatGPT to explain things they didn't get without needing to speak in front of the class. The world of teaching is now more complicated in ways that are exciting, as well as a bit unnerving.

Once classes concludes, I will write more about how students are using AI in other ways, both required and spontaneous. I also hope to soon share some research, rather than just qualitative impressions. However, based on my experience, I think focusing on how people use AI in class rather than whether they use it will result in better learning outcomes, happier students, and graduates who are better prepared for a world while AI is likely to be ubiquitous.

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My class required AI. Here's what I've learned so far.

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I fully embraced AI for my classes this semester, requiring students to use AI tools in a number of ways. This policy attracted a lot of interest, and I thought it worthwhile to reflect on how it is going so far. The short answer is: great!

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The AI panic is spreading in education. Released without guidance or clear suggestions for educators near the end of a school semester, the rapid adoption of ChatGPT might be among the largest, fastest transformations in education

. It instantly challenges the entire existance of a valuable type of assigment, the essay, along with dozens of other assignment types in fields from programming to poetry. Cheating is an obvious outcome. Evaluations may require a return to oral exams and writing longhand in exam books. Or maybe AI-written text will be identifiable by other AIs. Or assignment types will adjust to the new reality. Either way, the cheating problem will be solved, eventually.

So I don't want to discuss that.

Instead, I want to discuss the opportunity provided by AI, because it can help us teach in new ways. The very things that make AI scary for educators - its tedency to make up facts, its lack of nuance, and its ability to make excellent student essays - can be used to make education better.

This isn't for some future theoretical version of AI. You can create assignments, right now, using ChatGPT, that we will help stretch students in knew ways. We wrote a paper with the instructions. You can read it here, but I also want to summarize our suggestions. These are obviously not the only ways to use AI to educate, but they solve some of the hardest problems in education, and you can start experimenting with them right now.

The goal of education is straightforward: to permanent change someone's knowledge about the world.

In order to do that, students need to be able to transfer what is learned in the classroom to other contexts. Transfer is difficult because it requires deep understanding of a concept. Initially, when students learn about a new concept in one context, they often fail to recognize that concept when they encounter it again in a new context. For instance, in a math class ( context 1), students may learn about how to compare percentages and decimals but when faced with a food label ( context 2) or a medical decision ( context 3) students may fail to apply that knowledge. This is the case because a) students tend to focus on the concrete aspects of any given problem or situation and b) applying knowledge to a new context requires a deep understanding of the underlying structure of a concept. To use what they previously learned students need to recognize that the former problem ( from math class) is the same problem ( ah, just like in math class!) in a new context.

It is hard to prove that transfer occurs, and harder to teach people to transfer knowledge. But we think AI can help, because it is both so good at making stuff up and kind of bad at doing that accurately.

AI is a cheap way to provide students with many examples, some of which may be inaccurate, or need further explanation, or may simply be made up. For students with foundational knowledge of a topic, you can use AI to help them test their understanding, and explicitly push them to name and explain inaccuracies, gaps, and missing aspects of a topic. The AI can provide an unending series of examples of concepts and applications of those concepts and you can push students to compare examples across different contexts, explain the core of a concept, and point out inconsistencies and missing information in the way the AI applies concepts to new situations. You can use the confident errors of AI to your advantage and ask students to explore AI's output and then do the hard work of improving that output.

The basic idea is to have students ask the AI to create scenarios that apply a concept they learned in class: Create a Star Wars script illustrating how a bill becomes a law. Show how aliens might use the concept of photosynthesis to conquer Earth. Write a rap that uses metaphors. Then, ask the students to critique and dive deeper into these models, and potentially suggest improvements.

There is more in the paper, including prompts, but the ability to generate endless convincing (but slightly wrong) examples of a concept being applied allows us to approach transfer in a new way.

We think we understand the world far more than we do, which makes us less willing to learn.

This is called the illusion of explanatory depth, a cognitive bias that occurs when an individual overestimates their understanding of a concept or phenomenon. This bias is often characterized by a person's ability to provide a detailed and seemingly knowledgeable explanation of a subject, despite their actual rather limited degree of understanding. For instance, most of us can't quite explain how a car engine works, how a fridge works, or even how a pencil is made. But we are under the illusion that we have a depth of understanding about the topic. Students too can easily fall for this illusion, assuming that they understand how something works when in fact, they only have a shallow understanding of a topic.

Breaking the illusion requires confronting one's own ignorance, which can be a upsetting and humbling experience, and hard for teachers to pull off. So let AI do it for you.

We created an assignment where students ask the AI to explain a particular concept step by step, something AI is very good at. Students should then improve this output by adding information, considering the order of the steps, and re-thinking the depth of their knowledge about the topic. Here, we are using AI to come up with steps in a process so that students can critique and improve upon a process. The prompt can include something that students feel they understand well or something complex that will require additional research or numerous steps to make whole.

Again, the details are in the paper, but students are asked to add, remove, and combine steps, using their own research, until they get an understanding of the process or approach, and realize how complicated it really is while achieving some mastery over it.

When students hear you explain and discuss a concept, they often feel that they understand what you mean, but that feeling isn't always accurate. One powerful way to turn concepts from theory into practice is to teach someone else, to evaluate their work, and to give concrete and timely advice about how to improve. As any teacher knows, the act of assessing and evaluating someone else's work and teaching someone else improves our own knowledge of a topic.

By acting as a "student," the AI can provide essays about a topic for students to critique and improve. The goal of this exercise is to have the AI produce an essay based on a prompt and then to "work with the student" as they steadily improve the essay, by adding new information, clarifying points, adding insight and analysis, and providing evidence. We take advantage of the AI's proneness to simplify complex topics and its lack of insightful analysis as a backdrop for the student to provide evidence of understanding.

In this assignment, you'll give students an essay prompt for the AI and it will be their job to give the AI suggestions for improvement. They'll paste in both the original essay, their suggestions, and the final output. The process will push them to think critically about the content and articulate their thoughts for improvement in a clear and concise manner. They may need to seek out additional information to fill the gaps the AI essay might be missing or double check on the "facts" that the AI presents.

If nothing else, they will recognize how hard teaching and evaluating the work of others can be!

On a Tuesday, I introduced my class to ChatGPT. By Thursday, over half the class admitted using it in a wide variety of ways. No one admitted to cheating, but students were ingenious in their applications: they used the AI for feedback on essays, suggestions on topics, as a tutor to explain concepts, and as a way of correcting and finding errors in assignments that ranged from literature to engineering. Educators face a new world of ubiquitous AI use in class, and the dominant stories are often ones of fear - what does this mean for education? The answers are not clear, yet, but it is important that we consider how having the ability to quickly and cooperatively generate content can be used to boost pedagogy, even as it threatens old methods.

We hope you will take a look at the draft paper, which includes a lot of details on how to set up and assess these assignment, and share suggestions in the comments below about how you are using AI to teach, or to learn.

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How to... use AI to teach some of the hardest skills

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The AI panic is spreading in education. Released without guidance or clear suggestions for educators near the end of a school semester, the rapid adoption of ChatGPT might be among the largest, fastest transformations in education .

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I think most people who are using ChatGPT to help with writing are doing it wrong. I don't just mean because they using it to cheat on school assignments (don't do that) or because they don't check the facts that ChatGPT gives (they might be made up), but because they have the wrong mental model for how to work with the system.

I have mentioned that ChatGPT isn't Google, and it isn't Alexa, but it also isn't a human that you are giving instructions to. It is a machine you are programming with words.

Because ChatGPT often acts like a helpful human, we get lured into thinking it is one. I asked my Twitter followers, and nearly half always act politely towards the AI, and another quarter mostly act politely. I do this myself, saying "please" and "thank you" in my requests, even though the AI doesn't care.

While there is nothing wrong with politeness, it often obscures the fact that all we are trying to do is prompt a non-sentient machine to generate the text we need. This confusion isn't a problem in other types of generative AI. Since AI image generators are not as verbose as ChatGPT, people using them have learned they need to spend time doing "prompt engineering:" editing and playing with prompts to come up with the results they want. These prompts can get very elaborate, and can often seem abstract, or almost poetic. They are the program the AI is following, where the goal is to generate a great image through trial-and-error and pushing the AI in the direction you want.

But between the human-like nature of chat and the fact that written material is harder to immediately evaluate, many people tend to avoid explicit prompt construction in ChatGPT. But that is a mistake! More elaborate and specific prompts work better.

Don't ask it to write an essay about how human error causes catastrophes. The AI will come up with a boring and straightforward piece that does the minimum possible to satisfy your simple demand. Instead, remember you are the expert and the AI is a tool to help you write. You should push it in the direction you want. For example, provide clear bullet points to your argument: write an essay with the following points: -Humans are prone to error -Most errors are not that important -In complex systems, some errors are catastrophic -Catastrophes cannot be avoided

But even these results are much less interesting than a more complicated prompt: write an essay with the following points. use an academic tone. use at least one clear example. make it concise. write for a well-informed audience. use a style like the New Yorker. make it at least 7 paragraphs. vary the language in each one. end with an ominous note. -Humans are prone to error -Most errors are not that important -In complex systems, some errors are catastrophic -Catastrophes cannot be avoided

Notice that not every part of the prompt is followed exactly - there are six paragraphs, not seven - but that is typical of how generative AI works: you don't always get what you directly ask for, but you can push towards something unique and interesting by playing with prompts.

So try asking for it to be concise or wordy or detailed, or ask it to be specific or to give examples. Ask it to write in a tone ( ominous, academic, straightforward) or to a particular audience ( professional, student) or in the style of a particular author or publication ( New York Times, tabloid news, academic journal). You are not going to get perfect results, so experimenting (and using the little "regenerate response" button) will help you get to the right place. Over time, you will start to learn the "language" that ChatGPT is using.

ChatGPT's huge advance over other generative AIs is that it has a memory. It keeps track of what you wrote, and appears to remember about 3,000 words worth of data. However, what it retains, and how that memory is used, is often hard unclear.

Sometimes the memory is useful, you can (and should) ask it to revise previous work: change the third paragraph to be more professional or use a different example in the middle and it will provide a revised version. However, sometimes it forgets what you were working on, and you have to remind it. You may, for example, want to tell it revise the third paragraph on the essay on catastrophes so it doesn't lose track.

Memory can also be a problem. ChatGPT can get "stuck" repeating the same examples or tone throughout many requests. To unstick it, you may need to ask it to give a different example, or to change its tone, or to alter your early prompts. Often, the best option is to give ChatGPT amnesia: start a new chat and try again.

Similarly, you might want to break up your requests to the Chatbot into smaller chunks. Ask it for an introduction, and revise that to get the tone that you want to achieve. Only then should you start asking for additional paragraphs.

And if the results of those longer pieces cut off, simply asking ChatGPT to "continue" is enough to get the rest of the material.

You can ask the AI to use specific styles for writing. You will get different results from asking for an academic essay versus a persuasive article versus a blog post versus a corporate memo. The results are often surprising. So you should consider trying several writing types when experimenting with prompts.

Another way to get interesting writing out of ChatGPT is by asking the AI to be someone else. You can have the AI play characters by prompting it to think of itself as a chef, or a novelist, or Plato. This can lead to delightful results. You can start with this list of hundreds of persona suggestions. I used a modified version of the storyteller prompt: "I want you to act as a storyteller. You will come up with entertaining stories that are engaging, imaginative and captivating for the audience. It can be fairy tales, educational stories or any other type of stories which has the potential to capture people's attention and imagination. You should provide lots of detail and make the story memorable."

A very different perspective on error and catastrophe!

ChatGPT, like all generative AI systems, is a tool. Tools are used by humans to accomplish specific tasks. Thinking of it that way helps unlock its potential, but also avoid its pitfalls.

For example: Don't ask it for facts that you can't easily check. Don't ask it to provide references. Don't have it do math, or conduct analysis. It will happily fake doing these things for you and the output will mostly likely be wrong. ChatGPT it is far from the all-knowing AI that the movies taught us to expect. But as a tool to jumpstart your own writing, multiply your productivity, and to help overcome the inertia associated with staring at a blank page, it is amazing.

I'd like to conclude by asking you to write in the comments about any tips or prompt-crafting that you are using to make ChatGPT work. Part of what makes the release of this tool so exciting is that we are learning about it collectively, and that we can only understand the system through exploration. So what prompts or experiences do you want to share?

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How to... use ChatGPT to boost your writing

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The key to using generative AI successfully is prompt-crafting

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We live in an era of practical AI, but many people haven't yet experienced it, or, if they have, they might have wondered what the big deal is. Thus, this guide. It is a modified version of one I put out for my students earlier in the year, but a lot has changed. It is an overview of ways to get AI to do practical things.

Large Language Models like ChatGPT are extremely powerful, but are built in a way that encourages people to use them in the wrong way. When I talk to people who tried ChatGPT but didn't find it useful, I tend to hear a similar story.

The first thing people try to do with AI is what it is worst at; using it like Google: tell me about my company, look up my name, and so on. These answers are terrible. Many of the models are not connected to the internet, and even the ones that are make up facts. AI is not Google. So people leave disappointed.

Second, they may try something speculative, using it like Alexa, and asking a question, often about the AI itself. Will AI take my job? What do you like to eat? These answers are also terrible. With one exception, most of the AI systems have no personality, are not programmed to be fun like Alexa, and are not an oracle for the future. So people leave disappointed.

If people still stick around, they start to ask more interesting questions, either for fun or based on half-remembered college essay prompts: Write an article on why ducks are the best bird. Why is Catcher in the Rye a good novel? These are better. As a result, people see blocks of text on a topic they don't care about very much, and it is fine. Or the see text on something they are an expert in, and notice gaps. But it not that useful, or incredibly well-written. They usually quit around now, convinced that everyone is going to use this to cheat at school, but not much else.

All of these uses are not what AI is actually good at, and how it can be helpful. They can blind you to the real power of these tools. I want to try to show you some of why AI is powerful, in ways both exciting and anxiety-producing.

To start, lets introduce the six Large Language Models available right now, all for somewhere between free and $20 a month.

The first four (including Bing) are all OpenAI systems. There are basically two major OpenAI AIs today: 3.5 and 4. The 3.5 model kicked off the current AI craze in November, the 4 model just premiered and is much more powerful. A new variation uses plugins to connect to the internet and other apps, but it is only in early testing. If you have never paid for OpenAI, you have only used 3.5. Aside from the plugins variation, none of these models are connected to the internet.

Microsoft's Bing uses a mix of 4 and 3.5. It is connected to the internet. Bing is a bit weird to use, but powerful. Here is my guide to using it. In addition, Google has released a disappointing AI called Bard (though they may show us more impressive models soon) and Anthropic has released Claude, though it is more focused at business users. So what can you do with these things?

For right now, no other general AI tool comes even close to GPT-4, which you can access at Bing for free or by purchasing a $20/month subscription to ChatGPT. GPT-3.5 is also good at writing and is much faster. I have experimented a lot on how to use AI to help with written material, so here is a list of way you might find it useful:

Writing anything. Blog posts, essays, promotional material, speeches, lectures, chose-you-own adventures, scripts, short stories - you name it, it does it. But you can't just give it basic prompts. Basic prompts result in boring writing. Getting good writing out of ChatGPT takes some practice, and here is a guide to doing that. ChatGPT-4 is much better at writing. Bing can be incredible at writing, but needs some convincing.

Make your writing better. Paste your text into ChatGPT. Ask it to improve the content or for suggestions about how to make it better for a particular audience. Ask it to create 10 drafts in radically different styles. Ask it to make things more vivid, or add examples. (though note it only "remembers" a couple thousand words of text)

Help you with tasks. AI can write emails, create sales templates, give you next steps in a business plan, and a lot more. Here is what I could accomplish with it in 30 minutes.

Unblock yourself. It is very easy to get distracted from a task by one difficult challenge. AI provides a way of giving yourself momentum.

Some things to worry about: In a bid to respond to your answers, it is very easy for the AI to "hallucinate" and generate plausible facts. It can generate entirely false content that is utterly convincing. Let me emphasize that: AI lies continuously and well. Every fact or piece of information it tells you may be incorrect. You will need to check it all. Particularly dangerous is asking it for math, references, quotes, citations, and information for the internet (for the models that are not connected to the internet). Bing and ChatGPT-4 are better at this. Here is a guide to avoiding hallucinations.

The AI also doesn't explain itself, it only makes you think it does. If you ask it to explain why it wrote something, it will give you a plausible answer that is completely made up. It is not interrogating its own actions, it is just generating text that sounds like it is doing so. This makes understanding biases in the system very challenging, even though those biases almost certainly exist.

It also can be used unethically to manipulate or cheat. You are responsible for the output of these tools.

Open Source Option: Download Stable Diffusion
Best free option that requires sign-up: Bing or Bing Image Creator (which uses DALL-E), Playgound (which lets you use multiple models)
Best option: Midjourney

There are three big image generators out there:

1. Stable Diffusion, which is open source and you can run from any high-end computer. It takes effort to get started, since you have to learn to craft prompts properly, but once you do it can produce great results. It is especially good for combining AI with images from other sources. Here is a nice guide to Stable Diffusion if you go that route (be sure to read both parts 1 and part 2).

2. DALL-E, from OpenAI, which is incorporated into Bing (you have to use creative mode) and Bing image creator. This system is very good, but a little less powerful, for now, than...

3. Midjourney, which is the best system in early 2023. The reason I would suggest MidJourney is that it has the lowest learning-curve of any system: just type in "thing-you-want-to-see --v 5" (the --v 5 at the end is important, it uses the latest model) and you get a great result. Midjourney requires Discord. Here is a guide to using Discord.

Some uses:

Illustrate presentations or reports

Create stock photographs of your product in use

Create mockups of designs

Create mockups of apps or websites

Create logos

Some things to worry about: These systems are built around models that have built-in biases due to their training on Internet data (if you ask it to create a picture of an entrepreneur, for example, you will likely see more pictures featuring men than women, unless you specify "female entrepreneur"), you can use this explorer to see these biases at work.

These systems are also trained on existing art on the internet in ways that are not transparent and potentially legally and ethically questionable. Though technically you own copyright of the images created, legal rules are still hazy.

Also, right now, they don't create text, just a bunch of stuff that looks like text. But they are getting better at hands.

Despite of (or in fact, because of) all its constraints and weirdness, AI is perfect for idea generation. You often need to have a lot of ideas to have good ideas. Not everyone is good at generating lots of ideas, but AI is very good at volume. Will all these ideas be good or even sane? Of course not. But they can spark further thinking on your part.

How do you do it? Just ask for ideas, and it will provide them. But there are also more advanced techniques. Here is a guide to them.

It is now trivial to generate a video with a completely AI generated character (you can use the images generated using the techniques in the guide), reading a completely AI-written script, talking in an AI-made voice, animated by AI.

It can also deepfake people, as you can see in this link where I deepfaked myself. Instructions and more information here. Use with caution, but this can be great for explainer videos and introductions. And within a few months you are likely to be able to generate videos from text prompts, so stay tuned.

Some things to worry about: Deep fakes are a huge concern, and these systems need to be used ethically.

GPT-3.5 is a powerful coding companion. But GPT-4 is next-level. I have been using it to write programs in Python and Unity (programming languages I literally do not know at all!) by just telling it what I want in words: " I need to create an Amazon Echo skill that will flash my hue lights green and blue when I yell party. Can you create it?" It did, and now my lights flash blue and green. It told me what files to download, what websites to go to, and what to do. When there were errors, I just pasted them in and it corrected the code and told me how to fix problems. I didn't need to know anything. You can code now. Try it.

AI can be a powerful tool for learning and exploration. I have written about how it can be used for teaching and to help make teachers lives easier and their lessons more effective, but it can also work for self-guided learning as well. Some ways to do that in ChatGPT:

Summarize texts. I have pasted in numerous complex academic articles and asked it to summarize the results, and it does a good job! (though remember the size limits). Even better, you can then interrogate the material by asking follow-up questions: what is the evidence for that approach? What do the authors conclude? And so on...

Help with concepts. You can ask the AI to explain concepts. Because we know the AI could be hallucinating, you would be wise to (carefully!) double-check its results against another source. This both helps you learn and confirms the AI output looks good. Once you have a sense that it is right, ask it to explain it in different ways "Like I am 10" or "in a script from The Office" or "in the context of a medical examination." Again, this is a start for your learning journey, since it will often get subtleties wrong.

Explain errors: If you get something wrong, paste in the incorrect text and ask it to explain the error, or to walk you through it step-by-step. The results can be surprisingly helpful.

Some things to worry about: If you don't check for hallucinations, it is possible that you could be taught something inaccurate. Use the AI as a jumping-off point for your own research, not as the final authority on anything. Also, if it isn't connected to the internet, it will make stuff up.

This is just the beginning. AI is improving rapidly, and people are developing new uses all the time. But remember two key points:

AI is a tool. It is not always the right tool. Consider carefully whether, given its weaknesses, it is right for the purpose to which you are planning to apply it.

There are many ethical concerns you need to be aware of. AI can be used to infringe on copyright, or to cheat, or to steal the work of others, or to manipulate. And how a particular AI model is built and who benefits from its use are often complex issues, and not particularly clear at this stage. Ultimately, you are responsible for using these tools in an ethical manner.

We are in the early days of a very rapidly advancing revolution. Are there other uses you want to share? Let me know in the comments.

This post is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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How to use AI to do practical stuff: A new guide

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We live in an era of practical AI, but many people haven't yet experienced it, or, if they have, they might have wondered what the big deal is. Thus, this guide. It is a modified version of one I put out for my students earlier in the year, but a lot has changed.

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Bing AI is a huge leap over ChatGPT, but you have to learn its quirks

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An Essential Guide to AI for Educators / Modules / AI 101 for Educators / Introduction to the Course

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An Essential Guide to AI for Educators / Modules / AI 101 for Educators / Introduction to the Course

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Learn all about the course and assessment. Already enrolled? Join your class in My AP.

About the Course

Build on what you learned in AP Seminar to deeply explore an academic topic, problem, or issue of individual interest. Through this exploration, you will design, plan, and conduct a year-long research based investigation to address a research question.

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Skills You'll Learn

Conducting independent research

Analyzing sources and evidence

Applying context and perspective

Writing a college-level academic paper

Presenting research findings to an audience

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Deeply explore an academic topic, problem, or issue of individual interest. You will design, plan, and conduct a year-long research based investigation.

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For years, technology-everything from adaptive software to tablet computers-has promised to make teachers' lives easier, their workloads lighter. Quite frequently, it's done the opposite, piling still one more thing on teachers' plates.

But in the midst of the technological sea change brought about by artificial intelligence technologies like ChatGPT, a new crop of edtech startups are creating AI tools designed specifically for classrooms, reviving the idea that teachers can spend more of their days working with students by streamlining some of the most time-consuming parts of their work.

We spoke with educators and experimented with four of the most promising tools to learn how teachers can make AI work for them-without having to work too hard on AI. One thing to keep in mind: The technology behind these tools is still new, and all generated work should be examined for accuracy and adherence to standards. Also, it's worth double-checking that tools comply with relevant local privacy regulations, which vary by state.

It might help to think of Diffit as an AI text leveler that can translate a given article, excerpt, or even YouTube video to any reading level from second to 11th grade. Using the same large language model technology that powers ChatGPT, Diffit parses complex texts (or captions from a video) and produces an accessible summary, along with a relevant vocabulary list with definitions; it can also generate assessment questions.

Educator Michele Haiken describes it as an ideal tool to try with an article that you "want to use for your lesson, but is too hard for some (or all) of your readers." Recently, Kristen Starnes, who teaches high school social studies near Milwaukee, used the tool to adapt The Federalist Papers -a challenging collection of 18th-century essays-for her 11th-grade students. Though she might ask students to wrestle with the original text at first, she also provides a modern translation at a reading level appropriate for each student. "I have kids who have IEPs who are reading at different levels, and it's really nice to have essentially the same reading, just leveled appropriately," she says.

To learn how it works, we experimented with the tool as a teacher might. Pasting a link to No. 51, a 1,900-word Federalist Papers essay about checks and balances, and asking for a version at an 11th-grade reading level produces a crisp 500-word summary in less than 30 seconds. Though lacking in rhetorical flourish, the summary is more accessible to students looking to understand the main ideas. Where James Madison's original text discusses the need for the independence of each branch of government, particularly in the "emoluments annexed to their offices," Diffit offers a modern simplification: "The members of each branch of government should not rely too much on the other branches for their pay."

While the summary captures the essential elements, it glosses over or completely eliminates certain points and nuances found in the original. When complex or abstruse texts like The Federalist Papers are further scaled to, say, a seventh-grade reading level, they tend to become oversimplified and perhaps less useful than materials specifically written for younger audiences. As with most AI tools, every generated summary is slightly different, even when the same passage or text is used-so it can take a few tries to hit on a truly useful summary.

Along with the leveled text, Diffit by default creates a short assessment: three multiple choice, three short answer, and three open-ended questions pertaining to the reading-though teachers can adjust those quantities. There's also a template library that automatically turns generated text into formatted workbook pages, mind maps, or claim-evidence-reasoning activities.

Starnes often uses Diffit to create extra multiple choice questions for student practice, but she keeps a close eye on what's generated: Sometimes more than one answer will be correct, she says. "The multiple choice is still a little hinky-ChatGPT definitely writes better multiple choice questions," she says.

MagicSchool AI

Like Diffit, MagicSchool AI generates readings and levels text. But it's also capable of generating outputs for virtually any purpose. It can develop sentence starters on a given topic, organize and format a syllabus, and generate practice questions for specific tests like the SAT.

To Starnes, what sets MagicSchool apart from its peers is its ability to generate high-quality, grade-level text from a prompt when she doesn't have a specific text she's summarizing. For her 11th-grade world history class, she might ask it to create three pages on "British imperialism in India, including the concept of nonviolent resistance, replete with relevant literature from both British and Indian authors," and expect it to come up with a useful starting point for introducing the subject to students.

That particular prompt will generate a summary of about 550 words in less than 20 seconds, written in standard essay format. The resulting essay touches generally on various concepts and books, without going into much detail. When asked to reveal the sources used, it cites the Encyclopedia Britannica, the National Archives of India, and the website of the Indian National Congress (though it cannot pull in specific hyperlinks). In a follow-up prompt, users can ask the tool to create assessment questions based specifically on the reading.

Again, outputs need to be vetted before students see them. Multiple-choice questions need tweaking. And the prompts Starnes enters into MagicSchool sometimes need several passes before she hits on something usable.

Since Starnes's school doesn't use a history textbook, she frequently asks the tool to produce reading suggestions and summaries, investing her time into only the ones that seem most promising. If she asks it to create discussion questions, she has more time to devote to structuring the type of in-class conversations she wants to see. If groups of students need individual practice on different concepts, she can generate several custom worksheets in the time it used to take her to help just a few students.

"I see it very much as a way to free up time and energy that used to just be sucked up trying to find readings and sources," Starnes says. "It's giving students the amount of practice and feedback I would want to give if I only had a single class of 30 students and all the time in the world."

Class Companion

Class Companion, an AI tool for giving writing feedback, was originally designed with Advanced Placement courses in mind, and it's still largely geared toward document-based questions as well as the short answer and long essay formats found on end-of-year AP tests.

Teachers create assignments and invite students to complete them on the site. Teachers can write their own assignments and set the format (essay or short answer), along with the rubric, the sources or texts, the questions, and submission guidelines. A searchable library, populated with lessons from other teachers, is available as well.

Teachers can also use Class Companion to create assignments using a prompt. For instance, the prompt "President Lincoln's stance on slavery" will produce paragraph-long essay questions in a matter of seconds along these lines:

Examine President Abraham Lincoln's stance on slavery throughout his political career. Discuss the evolution of his views, the factors that influenced his stance, and how his position on slavery impacted the course of the U.S. Civil War. Provide specific examples from his speeches, policies, and actions. Analyze how Lincoln's views on slavery have shaped his legacy in U.S. history.

Once students write their answer and hit submit, Class Companion is capable of generating high-quality feedback, says Scott Petri, a high school history teacher in California who has used the tool with his students. It may indicate where students should strengthen a thesis statement or provide extra evidence.

Petri does not teach AP courses, but his students benefited from Class Companion's feedback, which takes about a minute to generate. The feedback generally indicates whether a student is on the right track with their responses. For example, an incomplete answer might ask the student to include more detail and pose a guiding question to get them started.

Teachers can give students up to five attempts at improving their answer, and students can ask for hints and advice to improve each time. "So it's almost like a genie," Petri says. "You've got to be careful with your wishes." This iterative process can aid students' growth by shifting their focus onto the process of arriving at a strong answer over many attempts.

The downside is that student work must be geared toward the AP's familiar-but-limiting format of well-ordered, just-the-facts information dumps. "Research shows that students do better when you teach them narrative writing in first person, and then teach them how to add details and rhetorical tricks," Petri says. "So I don't like forcing my kids to do AP-level writing too early."

Starnes has experimented with Class Companion, but her district's privacy policy doesn't currently allow for its use with student work. Still, she and a colleague were curious whether the tool would grade an essay the way she would. "For the most part, it did," Starnes reports. In part that's because AP questions are straightforward to grade, since good answers must tick off specific evidence, examples, and names. "There's not a lot of gray area, and AI can do a good job of giving feedback on that."

Curipod helps teachers create interactive slide presentations, but it's perhaps best known for its AI lesson-creation tool, which is capable of generating full presentations on any subject with background information, images, interactive polls, and reflection questions-all from a single prompt.

When high school science teacher Bonnie Nieves first began using Curipod a year ago, she asked it to create a lesson on the most difficult subject she taught-gene editing in biotechnology. "It didn't do it perfectly, but it did a really good job of creating a slideshow and a lesson," she says. The information it generated wasn't inaccurate, it was just incomplete, meaning Nieves had to make significant revisions.

But the finished product drew from an impressive well of knowledge from across the internet-the kind Nieves would have spent hours hunting for-and created multiple slides on each concept, so she could pick and choose the ones she liked best. A typical AI presentation will generate about 14 slides, including an introduction, a summary of relevant standards, three polls, two free-drawing activities (which require students to hand-draw diagrams), and an exit ticket question.

The polls and checks for understanding that Curipod generated for Nieves were not just interactive but also gave her insights into how well students were keeping up with the material. Another boon to engagement: Students' devices locked up during the lesson and did not display the presentation, requiring them to follow along on the interactive whiteboard unless they were answering a question.

Today, Nieves is more apt to use the auto-grading function, which works similarly to Class Companion. Nieves writes questions for the class, such as "Explain how gene editing is used, and give an example," and prompts the AI on how to grade the answers and what constitutes a complete answer. She encourages students to use Curipod's feedback to improve their answers immediately. In her experience, that helps students cover more ground quickly.

"I've been using it for a year, and I haven't found any examples of inaccurate feedback," Nieves says. "It frees me up to be able to pull the kids aside that are not doing well and be able to support them myself. And the kids who are doing well can move on to the next topic or work on an extension activity." The delivery of vaccines, along with the process for approving them in the first place, is of particular interest to students, and many are getting more exposure to those topics than in years past. "Those topics are the frosting on the cupcake, but students need to understand gene editing first."

Getting Real About Artificial Intelligence

These tools can help teachers work more efficiently by allowing them to better personalize lessons and focus on deep learning-which is only possible when students receive lessons that match their comprehension level along with fast, actionable feedback that guides them to improve their understanding in real time.

It's a concept that students might not grasp right away. When she first started using AI, Nieves showed her students examples of AI feedback and asked whether they thought it had been written by her or a computer. She was surprised that they often couldn't distinguish between the two. Until they became familiar with the tools, they were even a little doubtful that AI could assess their work as accurately as a teacher could.

Whole class discussions on what AI can and cannot do can help students understand both its benefits and its drawbacks, says Starnes. Through experience, teachers, too, will learn to appreciate when AI can save time and when using trusted materials written and edited by humans would serve students better-even if it takes longer to find. "I think that it's important that we say to students, 'This is not perfect,'" she says. "It's not going to do the same thing that a teacher would be able to do." All the same, she adds, "it just allows us to be better, less stressed-out versions of ourselves."

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New edtech tools are making it easier for teachers to complete tasks like generating lessons and providing differentiated texts, freeing up time for them to work with students.

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Teaching Students How to Use AI Responsibly

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Teaching Students How to Use AI Responsibly

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Rather than forbid students to use artificial intelligence, an English teacher shows them how they can use it in ways that support their learning.

I work at a very tech-savvy high school. When artificial intelligence (AI) splashed into the field of education, I fervently told my students that using any AI website would be considered plagiarism, and they needed to stay away from it. I felt my statements were validated when the university I worked with announced that there would be serious academic consequences if students used AI. Despite this, I eventually came to the conclusion that artificial intelligence is here to stay, and there was no way I could keep my students off of it. I soon decided that I needed to change my way of thinking and view artificial intelligence as another tool in my teacher toolbox.

In March 2023, I decided to pause teaching my students my regular curriculum. I wanted to teach my students how to use AI responsibly. I also wanted to experiment with several websites and see what my students and I could learn. Because I took that time, I now know how to effectively teach my students how to use these tools and how they can avoid the pitfalls of plagiarism.

Because AI is constantly changing, it's important to note that it's impossible for teachers to stay caught up with the newest websites and trends. Though I teach high school English, I believe that these assignments listed below can be easily adapted for any subject in a secondary school.

While these assignments are now considered an essential part of my teaching curriculum, I expect them to change as the technology becomes more sophisticated. Overall, this is a good place for any teacher to start developing good practices in regard to artificial intelligence.

7 AI Questions That Students Should Be Able to Answer

1. How do I find out if sources generated by AI are legitimate? Students were given a prompt that asked the AI platform to generate several sources cited in MLA format. They had to then find the sources on their own to see if they were valid.

2. What is paraphrasing, and when does it cross the line? Students were taught what paraphrasing was and were given several passages to practice with. They then took the reading passage and asked ChatGPT to paraphrase it.

3. What is good and bad feedback? Students put in old essays and asked for feedback. We talked about how to tell good advice from bad.

4. How do I check for bias in AI-generated material? Students learned about bias in writing, and I gave them examples of AI-generated work. They had to find words or phrases that were considered biased.

5. What are the pros and cons of artificial intelligence? Students read several articles about the pros and cons of AI. Because the literature surrounding AI is so vast, we had a Socratic seminar in class where we talked about the ethics of AI.

6. When is it cheating? At the end of the week, I told my students to write a new plagiarism policy for the school. Though it was a theoretical assignment, my students carefully articulated what we learned and how it could be applied to the school as a whole.

7. What are AI detectors, and how do they work? I explained the tools I have at my disposal to catch AI-related plagiarism. I showed them how Turnitin works and walked them through the AI feature. I also showed them how GPTZero works.

Things I Learned

Teachers should focus on one to two AI websites. At this point, there are countless websites that are geared toward attracting the attention of students and teachers. I focused solely on ChatGPT because they have a free version. But you should know that the free version is less sophisticated than the paid one.

Popular AI websites like ChatGPT are likely blocked at your school site. That is why I had to prepare beforehand and present the students with the AI-generated examples. A majority of my students also had cell phones that I allowed them to use as well.

I would only teach these assignments with upper-class students. Because we talk about the ethics of academic integrity, students need to demonstrate a level of maturity in order to be successful in this.

Because I didn't want my students to see these assignments as a free pass to use AI all the time, I constantly reminded them that the things they saw on AI websites would be useless if they did not have preexisting knowledge about the subject already in their heads. I pointed out that they wouldn't know what bad writing looked like if they didn't have knowledge of good writing in the first place.

Assessing How Things Went

Based on the results I saw after this unit, I would classify it as a success. Now my students ask me questions before they use an AI website. Because I was open about AI and its benefits, my students now feel free to approach me with their questions and concerns about the technology. Additionally, AI-related plagiarism cases have decreased in my classroom. When it does happen, students cannot claim that they didn't know.

Adding these lessons has also changed how I view AI. Today, I see it as a valuable tool for me to use as a teacher. I incorporate it into my lesson planning and constantly share what I learn with my students.

Moving forward, it's essential for administrators and teachers to engage in productive conversations about artificial intelligence and the role it now plays in education. To be effective educational institutions, we must adapt to the world of technology and learn how we can use it to positively impact our students. As the technology develops for students, it is also essential that it develop for the teachers as well. Teachers must be armed with effective AI detectors to discourage plagiarism and general student misuse.

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Teaching Students How to Use AI Responsibly

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Rather than forbid students to use artificial intelligence, an English teacher shows them how they can use it in ways that support their learning.

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The platform offers a chatbot-style interface, allowing users to express queries in natural language, and it provides concise answers with verifiable citations, making it ideal for fact-checking and academic purposes.

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Enhance learning with AI while protecting student data. 

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Enhance learning with AI while protecting student data. 

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Enhance learning with AI while protecting student data. All on a platform built from the ground up for independent schools.

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"The calculator, once a revolutionary tool, is now a staple in classrooms, enhancing students' understanding in mathematics. Similarly, AI must be viewed as vital for fostering a future-ready workforce."

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Provide students with access to the latest AI tools, with built-in monitoring for admins.

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Up to date list of AI tools as they come out

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AI-powered digital assistant developed by Microsoft. It is designed to help users with a wide range of tasks, from writing code to generating creative content such as poems, stories, and songs. Copilot uses advanced technologies such as GPT-4 and Bing Search to provide relevant and useful responses to user queries. It can also be used to create graphical artwork using an artificial intelligence model.

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Microsoft Copilot is your companion to inform, entertain and inspire. Get advice, feedback and straightforward answers. Try Copilot now.

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Use the Quick Check tool to determine the credibility of a piece of content. Explore the challenges, skills and tips to brush up on your skills before you begin.

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AI Snapshots- 180 Warmups for core classrooms

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AI Snapshots- 180 Warmups for core classrooms

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How School Leaders Can Pave the Way for Productive Use of AI

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Leveraging technology to advance teaching and learning promotes a culture of inquiry at school.

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HUSD AI Memo 23-2024

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Google Chrome Extension that can be used on Google Docs and Google Slides

-Make lesson plans, presentations, quizzes, rubrics, student examples, etc.

-Translators, change Lexile Levels, Feedback

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Brisk Teaching is the #1 AI-powered Chrome extension , helping you automate your workload without adding to it.

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Brisk is a free AI-powered Chrome extension that helps busy teachers save time directly within the tools they already use, like Google Docs, Slides, YouTube, and web articles. Quickly create instructional materials, share feedback, evaluate student writing, and adjust or translate texts - all without leaving your current workspace. With Brisk, the days of toggling between apps are over, freeing up more time for what you love: teaching.

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Leave feedback for students in four ways: ✨ Glows & Grows, 🎯 Targeted, 📏 Rubric Criteria, and 🪜 Next Steps.
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Brisk's unparalleled 🎯 Targeted Feedback tool instantly crafts Google Doc comments aligned with your rubric and standards, guiding students precisely where they need to revise. (Premium Feature

Inspect writing to understand the evolution of student work

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Level educational materials to meet students where they're at

Have you encountered a resource that doesn't match your classroom's reading level? Brisk takes content from any Google doc, article, or website and converts the material into a document that is suitable and leveled for your students. Brisk will simplify complex ideas, shorten lengthy sentences, adjust the word choice, and so much more.

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Brisk is a free AI-powered Chrome extension that helps busy teachers save time directly within the tools they already use, like Google Docs, Slides, YouTube, and web articles. Teachers use Brisk as a Targeted Feedback Generator, AI Presentation Maker, AI Quiz Maker, AI Lesson Plan Generator, AI Rubric Generator, Letter of Recommendation Generator, IEP 504 Template Maker, and so much more.