Artificial Intelligence (AI)

Overview

The integration of Artificial Intelligence in K-12 education includes a broad range of tools, resources, and curriculum materials. This webpage features a selection of resources to explore and implement AI in meaningful ways.

Please note that these resources are intended to support and enhance high-quality classroom instruction, not replace the essential role of teachers.

Colorado K-12 AI Roadmap

AI Computer Science Resources

Colorado Education Initiative (CEI)

AI Guidance for Schools and Districts

Profiles

Profile of an AI-Competent Student

Colorado envisions students who navigate a world shaped by artificial intelligence with curiosity, judgment, creativity, and care. Across districts, communities, learning environments, backgrounds, abilities, and levels of access to technology, AI-competent students understand both the possibilities and limitations of AI. They use that understanding to deepen learning, explore ideas, solve problems, create, and contribute to their communities.

AI-competent students see AI as a resource for extending thought—not as an authority, a substitute for human connection, or a shortcut around learning. AI can illuminate possibilities, provide feedback, and open new paths for inquiry, but students remain responsible for their learning, decisions, and intellectual integrity. Curiosity, effort, experience, creativity, judgment, and independent thought remain at the center of their development.

Human-Centered Learning

An AI-competent student places human relationships, experiences, and well-being at the center of learning and technology use.

Human Skills and Relationships: Students value empathy, communication, collaboration, creativity, and genuine relationships with their families, peers, educators, and communities. They understand that AI can simulate human language and interaction but does not possess feelings, lived experience, personal judgment, or the capacity for authentic connection.
Nondigital Learning: Students move thoughtfully between digital and nondigital ways of learning, recognizing the unique value of discussion, reflection, hands-on experience, physical experimentation, creative practice, and learning alongside others.
Social and Environmental Awareness: Students' understanding of AI includes its broader effects on individuals, communities, the workforce, and the environment. They are attentive to the implications of data collection, energy and resource use, automation, and unequal access to technology.

Self-Directed Learning

An AI-competent student takes ownership of learning and uses AI in ways that strengthen understanding, independence, and growth.

Agency and Ownership: Students take an active role in shaping the direction, pace, and depth of their learning. They draw from AI alongside teachers, peers, texts, experiences, and other resources, choosing support that advances their goals and strengthens their understanding.
Productive Struggle: Students understand that effort, uncertainty, revision, and persistence are essential parts of learning. They may seek a hint, example, explanation, or question to help them move forward while preserving the intellectual work needed to develop knowledge, skill, creativity, and independence.
Reflection and Discernment: Students reflect on how AI influences the learning process. They recognize when it has deepened understanding, expanded possibilities, or supported progress, as well as when it has reduced engagement, independence, creativity, or meaningful effort.

Purposeful Inquiry

An AI-competent student approaches AI with clear purpose, thoughtful questions, and sound judgment about when and how it can contribute.

Quality Prompts: Students provide clear, accurate, relevant, and respectful instructions when interacting with AI. They understand that specific, well-constructed prompts can improve the quality of a response but cannot guarantee that it will be accurate, complete, or appropriate.
Questioning and Refinement: Students communicate ideas and goals with clarity, providing relevant context, details, criteria, and constraints. They treat interaction with AI as an iterative process, refining questions and strategies as new information, limitations, and possibilities emerge.
Tool Judgment: Students understand that different tools serve different purposes. Their choices reflect the learning goal, the capabilities and limitations of the technology, expectations for its use, and considerations of privacy, safety, and appropriateness. They recognize that AI is not always the best tool—or a necessary one.

Critical Evaluation

An AI-competent student approaches AI-generated information with informed curiosity, healthy skepticism, and a commitment to evidence-based understanding.

Verification and Evidence: Students view AI-generated content as information to examine rather than a final or authoritative answer. They evaluate important claims, calculations, quotations, evidence, and citations using primary sources, authoritative references, disciplinary practices, peer-reviewed research, and relevant expertise when appropriate.
Bias and Limitations: Students understand that AI systems generate outputs from patterns in data and are shaped by their training, design, and context of use. They are alert to missing perspectives, bias, uncertainty, and the possibility that an output may sound convincing while still being inaccurate, incomplete, fabricated, or misleading.
Meaning-Making: Students bring together AI-generated information, evidence, prior knowledge, lived experience, disciplinary understanding, and multiple perspectives. From these sources, they develop and communicate their own interpretations, conclusions, solutions, and creative ideas.

Responsible and Ethical Use

An AI-competent student uses AI in ways that reflect integrity, sustain trust, and respect the rights, safety, and well-being of others.

Intellectual Ownership: Students maintain a clear sense of authorship. Their work reflects their own understanding, reasoning, voice, and creativity, and they can explain how their ideas developed and how AI contributed to the process.
Transparency and Trust: Students understand and follow the expectations established by teachers, courses, schools, districts, and assignments. They communicate honestly about when and how AI supported their work. They disclose, document, or cite AI use when appropriate.
Privacy, Safety, and Fairness: Students consider the rights, dignity, and well-being of others when using AI. Their choices reflect awareness of personal and sensitive information, consent, intellectual property, attribution, digital footprints, fairness, and the broader consequences of AI-assisted content, actions, and decisions.

Profile of an AI-Competent Educator

Colorado envisions educators who navigate a world shaped by artificial intelligence with professional judgment, pedagogical expertise, curiosity, and care. Across districts, communities, school settings, grade levels, content areas, and levels of access to technology, AI-competent educators understand both the possibilities and limitations of AI. They use that understanding to strengthen learning design, assessment, feedback, differentiation, professional practice, and collaboration.

AI-competent educators see AI as a resource for extending professional capacity, not as an authority, a substitute for human relationships, or a replacement for expertise. AI may help educators explore possibilities, generate ideas, respond to student needs, and reduce time spent on routine tasks, but educators remain responsible for the materials, decisions, feedback, assessments, and learning experiences they create or use. Knowledge of students, content, pedagogy, and context remains central to their practice.

Human-Centered Practice

An AI-competent educator places relationships, belonging, student agency, and human development at the center of teaching and technology use.

Relationships and Belonging: Educators prioritize empathy, trust, communication, and genuine knowledge of their students. They understand that AI cannot understand students’ lived experiences, form authentic relationships, exercise care, or meet students’ social and emotional needs.
Inclusive and Accessible Practice: Educators attend to students’ cultural, linguistic, developmental, and accessibility needs, as well as differences in access to technology. Their use of AI supports participation and maintains high expectations without reinforcing stereotypes, deficit perspectives, or barriers to learning.
Human Learning and Agency: Educators recognize the unique value of direct instruction, discussion, collaboration, hands-on investigation, creative practice, physical experimentation, reflection, and productive struggle. They use AI only when it improves access or supports progress. They limit its use when independent effort will better develop persistence, fluency, and creativity.

Pedagogical Agility

An AI-competent educator integrates AI into instruction only when it strengthens the coherence, responsiveness, and quality of the learning experience.

Learning Design: Educators design meaningful learning experiences aligned with the Colorado Academic Standards, clear learning objectives, appropriate cognitive demand, and relevant success criteria. They consider how AI may contribute to the learning goal rather than using it for its own sake.
Assessment Literacy: Educators design formative and summative assessments that provide valid evidence of student knowledge, reasoning, process, and application. Their expectations for AI use, whether permitted, documented, limited, or excluded, reflect the purpose of the assessment and the learning it is intended to reveal.
Responsive Instruction: Educators draw on multiple sources of evidence, including student work, observation, conversation, and assessment data, to respond to learner needs. Approved AI tools may support the development of scaffolds, extensions, differentiated materials, and feedback, while educators ensure that these resources are accurate, accessible, developmentally appropriate, and instructionally sound.

Purposeful AI Use

An AI-competent educator approaches AI with clear purpose and sound judgment about when, why, and how a particular tool may contribute.

Purposeful Prompting: Educators begin with a defined instructional or professional goal and provide relevant context, criteria, examples, and constraints when interacting with AI. Their prompts reflect knowledge of the intended audience, learning environment, and desired outcome.
Iterative Refinement: Educators treat interaction with AI as a process of questioning, reviewing, and revising. They refine prompts and strategies when outputs are unclear, incomplete, inaccurate, or unhelpful, understanding that effective prompting may improve relevance but cannot guarantee quality, fairness, or instructional appropriateness.
Tool Selection and Appropriate Use: Educators select tools based on the learning goal, student age and developmental readiness, accessibility, privacy and security protections, approved uses, and the tool’s capabilities and limitations. They recognize when another person, resource, technology, or nondigital approach will better support the intended purpose.

Critical Evaluation and Professional Judgment

An AI-competent educator approaches AI-generated information with informed curiosity, disciplined evaluation, and accountability for consequential decisions.

Verification and Evidence: Educators view AI-generated content as material to examine rather than as a finished or authoritative product. They verify important claims, calculations, citations, translations, examples, data summaries, and instructional materials using reliable sources, disciplinary knowledge, and relevant expertise before using them with students.
Bias, Representation, and Limitations: Educators understand that AI outputs are shaped by training data, system design, and context. They examine tools and outputs for missing perspectives and cultural, racial, gender, linguistic, geographic, socioeconomic, and ability-related bias, recognizing that polished or confident responses may still be inaccurate, incomplete, fabricated, or misleading.
Professional Judgment: Educators interpret AI-generated information through their knowledge of students, content, pedagogy, assessment, evidence, context, and policy. AI is one of many tools that may inform educator’s work, but it does not replace educator judgment or serve as the sole basis for grading, intervention, placement, discipline, or other consequential decisions about students.

Ethical Leadership and Professional Learning

An AI-competent educator models responsible use, reflects on the effects of AI, and contributes to a learning community capable of adapting thoughtfully as technology changes.

Ethical Stewardship: Educators use AI transparently and protect personally identifiable, confidential, and sensitive information. Their practice reflects requirements related to privacy, security, consent, copyright, licensing, attribution, and ownership, as well as consideration of AI’s effects on fairness, access, student identity, communities, human labor, and the environment.
Reflective and Ongoing Learning: Educators continue developing their understanding of AI capabilities, limitations, risks, evidence-informed applications, and applicable policies. They consider how AI affects instructional quality, workload, student thinking, engagement, independence, creativity, equity, and access, and they adjust their practice when its use does not support the intended purpose.
Collaboration and Shared Expectations: Educators examine emerging tools and practices with colleagues, share effective approaches, and contribute to coherent expectations across classrooms, grade levels, content areas, and schools. They communicate these expectations clearly with students and families, helping build a shared culture of thoughtful, safe, and responsible AI use.

Profile of an AI-Competent Administrator

Colorado envisions administrators who lead schools and systems shaped by artificial intelligence with strategic judgment, ethical responsibility, instructional focus, and care for people. Across districts, communities, school settings, leadership roles, and levels of access to technology, AI-competent administrators understand both the possibilities and limitations of AI. They use that understanding to strengthen systems, support educators, communicate with communities, allocate resources, and create conditions in which every student can thrive.

AI-competent administrators see AI as a resource for extending organizational capacity, not as an authority or a replacement for leadership, relationships, or professional expertise. AI may reduce administrative burden, reveal patterns, and support planning and communication, but administrators remain accountable for the policies, tools, systems, decisions, and outcomes they approve or implement. Human judgment, transparency, equity, safety, and the well-being of students, staff, families, and communities remain central to their leadership.

Human-Centered Approach

An AI-competent administrator places relationships, trust, belonging, and human expertise at the center of organizational change and technology use.

People and Relationships: Administrators distinguish between tasks that AI may support and professional roles that depend on empathy, creativity, judgment, and authentic relationships. They understand that AI may change how work is completed, but it does not replace the essential contributions of educators, staff, students, families, or school leaders.
Psychological Safety and Change: Administrators create a culture in which educators and staff can explore approved AI tools, ask questions, identify errors, and learn through reflection and revision. They address concerns about automation openly and reinforce that responsible experimentation is part of professional growth.
Equity and Access: Administrators consider how access to devices, connectivity, approved tools, accessibility features, language supports, and professional guidance shapes participation. They work to prevent AI from widening opportunity gaps and seek equitable access to safe and effective tools.

Strategic Systems Leadership

An AI-competent administrator uses AI purposefully to improve systems, reduce administrative burden, and reclaim time for instructional leadership and student support.

Workflow Improvement: Administrators examine repetitive or high-friction processes, such as scheduling, substitute coverage, transportation, staffing, reporting, and routine documentation. They use AI when it can improve efficiency, accuracy, or responsiveness while maintaining appropriate human review.
Resource Allocation: Administrators combine AI-supported analysis with local knowledge, financial information, staffing data, enrollment patterns, and evidence of student need. They may use predictive tools to anticipate budget, staffing, or operational challenges, while keeping resource decisions transparent, equitable, and grounded in professional judgment.
Time Reinvestment: Administrators intentionally direct time recovered through automation toward work that benefits from human presence and expertise. This includes classroom walkthroughs, teacher coaching, collaborative planning, student interaction, family engagement, and relationship-building across the school community.

Ethical Governance and Data Stewardship

An AI-competent administrator establishes safeguards that protect privacy, clarify expectations, and maintain accountability for how AI is selected and used.

Privacy and Data Stewardship: Administrators protect student and staff information by ensuring that AI tools align with FERPA, COPPA, district policy, and applicable privacy and security requirements. They understand how tools collect, retain, share, and use data and promote practices that limit data collection to what is necessary for the intended purpose.
Policy and Accountability: Administrators establish clear expectations for approved tools, appropriate uses, transparency, consent, and human oversight. They help students, staff, and families understand how AI may be used, what information may be collected, and who remains responsible for decisions supported by the technology.
Incident Readiness: Administrators prepare their communities to respond when deepfakes, misinformation, harmful AI-generated content, privacy breaches, or other AI-related incidents arise. Their response reflects a commitment to safety, accurate information, timely communication, appropriate investigation, and the restoration of trust.

Critical Evaluation

An AI-competent administrator evaluates AI tools and recommendations with disciplined inquiry, attention to bias, and a long-term view of the school system.

Vendor and Tool Evaluation: Administrators look beyond features and marketing claims when evaluating AI products. They consider the evidence supporting a tool, how data is used, what protections are in place, and how the system addresses cultural, racial, gender, linguistic, socioeconomic, disability-related, and other forms of bias.
Transparency and Human Oversight: Administrators expect AI-supported recommendations to be understandable and open to human verification. They recognize that automated systems should not serve as the sole basis for student placement, intervention, discipline, staffing, evaluation, or other consequential decisions.
Sustainability and Alignment: Administrators consider how new tools fit with existing systems, workflows, accessibility requirements, and long-term priorities. They weigh costs, support needs, interoperability, environmental impact, vendor dependence, and the risk of fragmented data before committing resources.

Communication and Professional Capacity

An AI-competent administrator uses AI to strengthen communication, elevate community voice, and build the collective capacity needed for thoughtful implementation.

Empathetic and Inclusive Communication: Administrators may use AI to draft, personalize, or translate school communications, expanding access for families who use different home languages. They maintain human review for accuracy, empathy, cultural responsiveness, tone, and context, especially during crises or other sensitive situations.
Community Voice: Administrators may use AI to synthesize feedback from students, families, and staff and to identify patterns across large amounts of information. They interpret these findings carefully, preserve perspectives that may not appear in dominant trends, and engage directly with community members before drawing conclusions.
Professional Learning and Shared Capacity: Administrators support ongoing, hands-on learning that moves beyond how to operate a tool and focuses on how AI may strengthen human expertise and professional practice. They collaborate with educators and staff to examine emerging tools, share effective approaches, establish coherent expectations, and communicate those expectations clearly with students and families.

Profile of an AI-Competent Special Service Provider

Colorado envisions specialized service providers (SSPs) who navigate a world shaped by artificial intelligence with professional judgment, systems thinking, collaboration, and care. Across districts, communities, school settings, professional disciplines, grade levels, and levels of access to technology, AI-competent SSPs understand both the possibilities and limitations of AI. They use that understanding to strengthen analysis, consultation, intervention design, communication, and coordinated systems of support.

AI-competent SSPs see AI as a resource for extending professional capacity, not as an authority or a substitute for expertise, relationships, contextual understanding, or multidisciplinary judgment. AI may help them synthesize information, explore patterns, organize resources, and reduce time spent on routine tasks, but SSPs remain accountable for the interpretations, recommendations, communications, and services they provide.

Human-Centered Practice

An AI-competent SSP places student well-being, relationships, identity, and context at the center of technology use and professional practice.

Student-Centered Understanding: SSPs center each student’s strengths, needs, goals, experiences, and learning environment. They understand that AI cannot fully represent a student’s lived experience or replace direct observation, professional assessment, meaningful interaction, or knowledge developed through sustained relationships.
Family and Culturally Responsive Partnership: SSPs may use AI to help organize, draft, simplify, or translate information for families. They review all AI-generated communications and translations for accuracy, accessibility, cultural responsiveness, tone, and context before sharing them. Their use of technology supports, rather than replaces, authentic communication and partnership with families.

Data-Informed Insight

An AI-competent SSP uses AI to extend analysis while maintaining professional responsibility for how data is represented, interpreted, and applied.

Data Synthesis and Visualization: SSPs may use approved AI tools to organize, summarize, and visualize aggregated or de-identified data. SSPs review the underlying data, calculations, visual representations, and conclusions for accuracy and clarity before using or sharing them.
Pattern and Systems Analysis: SSPs explore patterns across classrooms, grade levels, schools, or districts, including referral trends, intervention effectiveness, and multi-tiered system of supports (MTSS) outcomes. They examine AI-identified patterns alongside other evidence, consider alternative explanations, and account for local context before drawing conclusions or recommending changes.

Consultation and Teaming

An AI-competent SSP uses AI to strengthen shared understanding, structured problem-solving, and meaningful collaboration across teams.

Consultation and Facilitation: SSPs may use AI to develop problem-solving frameworks, guiding questions, meeting structures, scenarios, or accessible explanations of specialized concepts. They review and adapt these materials to ensure they are accurate, relevant, inclusive, and appropriate for the participants and purpose.
Collaborative Judgment: SSPs bring AI-supported information into conversation with professional expertise and the perspectives of students, families, educators, and other specialists. They understand that multidisciplinary decisions emerge through dialogue, evidence, context, and shared responsibility, not from an automated summary or recommendation.

Systems and Intervention Design

An AI-competent SSP connects responsive support for individual students with the design of coherent, accessible systems across teams and learning environments.

Evidence-Informed Supports: SSPs may use AI to locate, compare, and organize strategies, interventions, professional resources, intervention menus, or resource banks. They verify the source, evidence base, accuracy, accessibility, and relevance of AI-generated or AI-identified materials before incorporating them into practice.
Systems Alignment and Time Reinvestment: SSPs may use AI to reduce duplication, streamline planning, and support consistent processes across teams, including MTSS structures. They review AI-supported plans and materials before implementation and direct recovered time toward student support, consultation, family partnership, collaboration, and other work that benefits from human expertise and presence.

Professional Judgment

An AI-competent SSP uses AI purposefully, evaluates its outputs critically, and remains accountable for professional decisions and outcomes.

Purposeful Tool Use: SSPs select approved tools based on the professional goal, intended audience, accessibility, privacy protections, and the tool’s capabilities and limitations. They provide relevant context, criteria, and constraints when prompting and recognize when another resource, colleague, or nondigital approach is more appropriate.
Critical Review and Human Oversight: SSPs evaluate AI-generated frameworks, materials, translations, summaries, visualizations, and recommendations for accuracy, completeness, bias, accessibility, and alignment with professional standards. AI may contribute to their thinking, but no output is used as a substitute for professional interpretation, individualized understanding, or human decision-making.
Privacy and Ongoing Learning: SSPs protect confidential, personally identifiable, and sensitive information and favor aggregated or de-identified data when using AI. They follow applicable legal, professional, ethical, and district requirements and continue developing their understanding of AI capabilities, limitations, risks, and evidence-informed applications as technologies and practices evolve.

Profiles in Practice

An AI-Competent Learner: Examples in Practice

Purpose

This resource provides examples of how the competencies in the Colorado AI-Competent Learner Profile may appear in classrooms and other learning environments.

The examples are illustrative, not exhaustive or prescriptive. They should be adapted based on Students’ ages, abilities, disciplines, learning goals, access to technology, and local expectations for AI use. They do not suggest that students should use AI for every task. In some situations, choosing not to use AI is the most competent decision.

Human-Centered Learning

Seeks help from a teacher, counselor, family member, or peer for a personal or emotional concern rather than relying on AI for care or judgment.
Closes digital devices during a discussion to listen carefully and respond to others’ ideas.
Builds, experiments, sketches, rehearses, or practices hands-on when those approaches better support the learning goal.
Considers whether an AI-supported activity is accessible to classmates with different devices, languages, abilities, or support needs.
Examines how AI data collection, resource use, and automation may affect people, communities, workers, and the environment.

Self-Directed Learning

Chooses among AI, course materials, teachers, peers, and hands-on activities based on what will best support the learning goal.
Uses AI to request additional practice, a different explanation, an analogy, or questions that reveal gaps in understanding.
Asks for a hint or guiding question rather than requesting a complete answer.
Recognizes when AI is completing too much of a task and returns to working independently.
Reflects on whether AI deepened understanding or simply made the task faster and adjusts future use accordingly.
Continues working when AI is unavailable, prohibited, or inappropriate.

Purposeful Inquiry

Defines the problem, examines its context, identifies needed information, and considers relevant criteria, constraints, and affected people before using AI.
Provides a clear goal, relevant context, specific details, criteria, and constraints when prompting AI.
Requests feedback based on a rubric or specific criteria rather than asking whether work is simply “good.”
Refines a prompt when the initial response is too broad, unclear, incomplete, or irrelevant.
Uses a calculator, library database, primary-source archive, accessibility tool, or human expert when it is better suited to the task.
Considers whether a tool is approved and whether information is private, sensitive, or copyrighted before uploading it.

Critical Evaluation

Checks AI-generated claims, calculations, quotations, and citations against reliable sources or disciplinary methods.
Opens cited sources to confirm that they exist and support the claims attributed to them.
Evaluates the author, date, evidence, purpose, relevance, and context of information before using it.
Looks for stereotypes, bias, missing perspectives, and differences in how AI may perform across languages, cultures, identities, or abilities.
Recognizes that a confident response may still contain invented sources, inaccurate information, or unsupported conclusions.
Combines AI-generated information with evidence, prior knowledge, course content, and multiple perspectives to develop an original conclusion.

Responsible and Ethical Use

Uses AI to brainstorm or receive feedback while making the final decisions and preserving an authentic voice.
Does not submit AI-generated work that cannot be explained, evaluated, or defended.
Reviews assignment expectations and asks for clarification when permitted uses of AI are unclear.
Discloses, documents, or cites AI assistance when required.
Does not enter personal, confidential, or sensitive information into an AI system.
Does not upload or imitate another person’s writing, image, voice, or personal information without permission.
Considers whether an AI-supported action could deceive, exclude, disadvantage, or harm others.

An AI-Competent Educator: Examples in Practice

Purpose

This resource provides examples of how the competencies in the Colorado AI-Competent Educator Profile may appear in classrooms and other professional settings.

The examples are illustrative, not exhaustive or prescriptive. They should be adapted based on students’ ages, abilities, disciplines, learning goals, access to technology, and local expectations for AI use. They do not suggest that educators should use AI for every task. In some situations, choosing not to use AI is the most competent decision.

Human-Centered Practice

Prioritizes direct interaction, relationship-building, and knowledge of individual students when empathy, trust, context, or social and emotional support are essential.
Designs opportunities for discussion, collaboration, hands-on investigation, creative practice, reflection, and productive struggle.
Uses AI to improve access or provide support without reducing expectations for student thinking, participation, or independence.
Reviews AI-supported materials for cultural and linguistic responsiveness, accessibility, developmental appropriateness, and possible stereotypes or deficit perspectives.
Considers whether students have equitable access to approved tools, devices, connectivity, accessibility features, and alternative ways to participate.
Recognizes when direct instruction, peer collaboration, physical materials, or another nondigital approach will better support the learning goal.

Pedagogical Agility

Begins with the Colorado Academic Standards, learning objectives, cognitive demand, and success criteria before considering whether AI adds instructional value.
Uses AI to generate possible lesson ideas, examples, scaffolds, or extensions, then reviews and adapts them to the content, students, and learning context.
Designs assessments that provide valid evidence of student knowledge, reasoning, process, and application.
Determines whether AI should be permitted, documented, limited, or excluded based on the purpose of an assignment or assessment.
Uses student work, observation, conversation, assessment data, and other evidence to guide differentiation and instructional decisions.
Reviews AI-generated feedback before sharing it to ensure it is accurate, specific, constructive, and aligned with the intended learning.

Purposeful AI Use

Identifies a clear instructional or professional goal before selecting an AI tool.
Provides relevant context, criteria, examples, audience information, and constraints when prompting AI.
Refines prompts when an initial response is unclear, incomplete, inaccurate, overly broad, or misaligned with the intended purpose.
Selects tools based on instructional value, student age and readiness, accessibility, privacy and security protections, approved uses, and known limitations.
Uses a calculator, database, curriculum resource, colleague, specialist, or nondigital approach when it is better suited to the task.
Avoids entering student records, personally identifiable information, or other sensitive information into an AI system.

Critical Evaluation and Professional Judgment

Treats AI-generated content as a draft or starting point that requires review before it is used with students.

Verifies claims, calculations, citations, translations, examples, and data summaries using reliable sources, disciplinary knowledge, and relevant expertise.
Examines outputs for missing perspectives and cultural, racial, gender, linguistic, socioeconomic, geographic, and disability-related bias.
Recognizes that polished or confident language may still contain inaccurate, incomplete, fabricated, or misleading information.
Interprets AI-generated recommendations through knowledge of students, content, pedagogy, assessment, evidence, context, and policy.
Does not use AI as the sole basis for grading, intervention, placement, discipline, or other consequential decisions about students.

Ethical Leadership and Professional Learning

Uses AI transparently and follows applicable requirements for privacy, security, consent, copyright, licensing, attribution, and ownership.
Communicates clear expectations for student AI use and explains when disclosure, documentation, or citation is required.
Models how to review, question, revise, and responsibly use AI-generated content.
Reflects on how AI affects instructional quality, workload, student thinking, engagement, independence, creativity, equity, and access.
Adjusts or discontinues an AI-supported practice when it does not improve the intended learning or professional purpose.
Collaborates with colleagues to examine tools, share effective practices, and develop coherent expectations across classrooms, grade levels, content areas, and schools.
Continues learning about AI capabilities, limitations, risks, evidence-informed applications, and changing policies.

An AI-Competent Administrator: Examples in Practice

Purpose

This resource provides examples of how the competencies in the Colorado AI-Competent Administrator Profile may appear in schools, districts, and other educational leadership settings.

The examples are illustrative, not exhaustive or prescriptive. They should be adapted based on leadership role, organizational context, community needs, local policies, available resources, and access to technology. They do not suggest that administrators should use AI for every task or decision. In some situations, choosing not to use AI is the most competent and responsible leadership decision.

Human-Centered Approach

Prioritizes direct interaction, relationship-building, and professional expertise when trust, empathy, context, collaboration, or judgment are essential.
Distinguishes between tasks that AI may support and leadership responsibilities that require human decision-making, accountability, and relationships.
Engages educators and staff in conversations about how AI may affect their work, addressing concerns about automation, changing responsibilities, workload, and professional identity.
Creates psychologically safe opportunities for staff to explore approved AI tools, ask questions, identify errors, share lessons learned, and improve practice.
Considers whether educators, students, and families have equitable access to approved tools, devices, connectivity, accessibility features, language supports, training, and alternative ways to participate.
Reviews AI-supported practices for potential effects on students with disabilities, multilingual learners, students from historically underserved communities, and others who may experience barriers to access or participation.
Recognizes when a conversation, professional judgment, collaborative process, or other human-centered approach is more appropriate than using AI.

Strategic Systems Leadership

Begins with a clearly identified organizational, instructional, or operational need before considering whether AI may add value.
Examines repetitive, time-intensive, or high-friction processes such as scheduling, staffing, reporting, documentation, communication, substitute coverage, transportation, or data analysis for appropriate opportunities to improve efficiency.
Uses AI-supported analysis alongside enrollment, staffing, financial, operational, and student data to identify possible patterns, risks, or resource needs.
Reviews AI-generated projections or recommendations against local knowledge, organizational priorities, available evidence, and professional expertise before acting on them.
Establishes appropriate human review for AI-supported workflows, particularly when errors could affect students, staff, resources, or services.
Evaluates whether efficiencies gained through AI actually reduce workload or unintentionally create new processes, costs, or administrative burden.
Intentionally reinvests time saved through automation into classroom visits, instructional leadership, educator coaching, collaborative planning, student engagement, family partnerships, and other high-value leadership activities.
Adjusts or discontinues an AI-supported process when it does not improve effectiveness, efficiency, service quality, or organizational outcomes.

Ethical Governance and Data Stewardship

Uses only AI tools and systems that comply with applicable federal and state requirements, district policies, privacy protections, security standards, and approved-use expectations.
Understands what information an AI system collects, how long data is retained, whether information is used to train models, who may access the data, and whether information may be shared with third parties.
Avoids entering student records, personally identifiable information, personnel information, confidential communications, or other sensitive data into AI systems unless the use is explicitly authorized and appropriately protected.
Establishes clear expectations for which AI tools are approved, which uses are appropriate, and when disclosure, documentation, consent, or human review is required.
Communicates with staff, students, and families about how AI is being used, what safeguards are in place, and who remains accountable for decisions.
Develops procedures for responding to deepfakes, misinformation, impersonation, harmful AI-generated content, privacy breaches, inappropriate AI use, or other emerging incidents.
Coordinates with technology, legal, communications, student services, human resources, and other appropriate personnel when responding to AI-related concerns.
Reviews policies and safeguards periodically as technology, law, guidance, risks, and organizational practices evolve.

Critical Evaluation

Evaluates AI products based on demonstrated need and evidence of effectiveness rather than novelty, marketing claims, or pressure to adopt emerging technology.
Examines vendor claims, privacy practices, security protections, accessibility, interoperability, implementation requirements, costs, and available evidence before approving or purchasing a tool.
Investigates how AI systems have been developed and tested and whether they may produce different outcomes across cultural, racial, gender, linguistic, socioeconomic, geographic, disability-related, or other groups.
Includes educators, technology staff, specialists, students, families, or other affected stakeholders when evaluating tools that may significantly influence teaching, learning, services, or school operations.
Requires appropriate transparency and human verification when AI generates recommendations, predictions, classifications, or summaries that may influence decisions.
Does not use AI as the sole basis for student placement, intervention, discipline, special education decisions, educator evaluation, hiring, staffing, resource allocation, or other consequential decisions.
Considers how a proposed AI tool aligns with existing instructional systems, technology platforms, strategic priorities, accessibility requirements, and professional practices.
Evaluates total cost of ownership, professional learning needs, technical support, vendor dependence, data portability, system interoperability, environmental considerations, and long-term sustainability before making commitments.
Periodically reviews adopted tools to determine whether they continue to provide sufficient benefit to justify their cost, risk, and organizational impact.

Communication and Professional Capacity

Uses AI to support drafting, summarizing, organizing, or translating routine communications while maintaining human review for accuracy, tone, empathy, cultural responsiveness, and context.
Uses additional care and direct human involvement for communication involving crises, conflict, personnel matters, student concerns, discipline, sensitive family situations, or other high-stakes issues.
Uses AI-supported translation or accessibility features to expand access while reviewing important communications for accuracy and meaning when possible.
Uses AI to organize or summarize large amounts of feedback from surveys, listening sessions, meetings, or community engagement activities.
Examines summarized feedback for perspectives that may be underrepresented, obscured by dominant themes, or inadequately represented in quantitative analysis.
Engages directly with students, educators, families, and community members before drawing significant conclusions or making decisions based on AI-supported analysis.
Provides ongoing professional learning that helps staff understand AI capabilities, limitations, appropriate uses, privacy expectations, bias, verification, and human oversight.
Creates opportunities for educators and staff to practice using approved tools in authentic professional contexts rather than focusing only on technical features.
Develops shared language, expectations, protocols, and examples so that AI implementation is coherent across schools, departments, and leadership teams.
Encourages educators and leaders to share effective practices, unsuccessful experiments, emerging concerns, and evidence about the impact of AI on professional practice.
Monitors whether AI implementation is improving instructional quality, organizational effectiveness, staff capacity, equity, and student outcomes rather than measuring success primarily through rates of technology adoption.
Continues learning about emerging AI capabilities, limitations, evidence-informed applications, risks, regulations, and implications for educational leadership.

An AI-Competent Special Service Provider: Examples in Practice

Purpose

This resource provides examples of how the competencies in the Colorado AI-Competent Special Service Provider Profile may appear in professional practice.

The examples are illustrative, not exhaustive or prescriptive. They should be adapted based on students’ ages, abilities, communication and access needs, cultures and languages, professional context, access to technology, and local expectations for AI use. Because special service providers work across many professional disciplines, not every example will apply to every role. AI use should remain consistent with the provider’s professional standards, training, credentialing, scope of practice, and responsibilities.

These examples do not suggest that SSPs should use AI for every task. In some situations, choosing not to use AI is the most competent, ethical, or effective decision.

Human-Centered Practice

Prioritizes direct interaction, relationship-building, observation, and knowledge of individual students when trust, communication, health, functioning, access, or social and emotional support are essential.
Uses AI to adapt explanations, practice activities, communication or accessibility supports, or resources while preserving meaningful interaction with students and families.
Reviews AI-supported materials for cultural and linguistic responsiveness, accessibility, developmental appropriateness, and possible stereotypes or deficit perspectives.
Uses student and family input to individualize supports rather than relying on AI-generated assumptions about a student’s strengths, needs, preferences, or experiences.
Considers students’ communication, sensory, physical, health, language, and accessibility needs when selecting or adapting AI-supported resources.
Recognizes when direct service, skilled observation, assessment, counseling, consultation, specialized equipment, or another professional approach is better suited to the student’s needs.

Data-Informed Insight

Begins with a clear professional question and identifies the information needed before asking AI to summarize, compare, organize, or visualize data.
Uses AI to organize or summarize appropriate data, then checks the output against the original information for omissions, errors, or unsupported conclusions.
Uses multiple sources of evidence, such as observation, interviews, student and family input, assessment results, progress-monitoring data, health or service information, functional performance, and educator input, when interpreting student needs.
Uses aggregated, de-identified, or synthetic information when possible and selects platforms approved for the type of information being used.
Examines AI-identified patterns for alternative explanations, missing information, and cultural, linguistic, socioeconomic, health, or disability-related bias.
Uses AI-generated summaries or patterns as a tool for analysis rather than treating them as new evidence or replacing professional interpretation.

Consultation and Teaming

Uses AI to generate possible consultation questions, meeting agendas, discussion prompts, or planning tools, then adapts them to the specific student and team.
Uses AI to translate technical or discipline-specific information into family-friendly or educator-friendly language and reviews it for accuracy before sharing.
Uses AI to draft resources, follow-up communication, or action steps after consultation while ensuring they accurately reflect the team’s discussion and decisions.
Brings AI-generated strategies to the team as possibilities to consider rather than presenting them as recommendations that should automatically be implemented.
Uses input from students, families, educators, and other professionals to determine whether an AI-generated idea is realistic and appropriate in the student’s actual environment.
Consults with another qualified professional when AI raises a question or recommendation outside the SSP’s own training, role, or scope of practice.

Systems and Intervention Design

Identifies the student, service, team, or system need before using AI to generate possible interventions, accommodations, care or support plans, accessibility strategies, practice activities, or resources.
Provides AI with relevant criteria, intended outcomes, setting, age or developmental level, and implementation constraints when generating possible strategies.
Reviews AI-generated intervention or support ideas against evidence-informed practices, professional standards, student data, and the intended goal before using them.
Adapts AI-generated ideas based on factors such as frequency, duration, service delivery format, environment, staffing, equipment, communication needs, accessibility, and student response.
Uses progress-monitoring data, observation, and student response to determine whether an AI-supported strategy is working and modifies or discontinues it when needed.
Uses validated protocols, specialized tools, assistive technology, professional resources, colleagues, or nondigital approaches when they are better suited to the task than AI.

Professional Judgment

Identifies a clear professional purpose before selecting an AI tool and uses platforms approved for the intended task and type of information involved.
Provides relevant context, criteria, examples, audience information, and constraints when prompting AI while minimizing identifiable or confidential student information and using approved platforms when protected information is involved.
Treats AI-generated reports, summaries, recommendations, goals, care or support plans, communications, and resources as drafts that require professional review and revision.
Verifies claims, citations, calculations, translations, assessment information, intervention recommendations, and other important content using reliable sources and relevant professional expertise.
Interprets AI-generated recommendations through knowledge of the student, available evidence, professional standards, family and student input, educational context, and applicable requirements.
Maintains professional responsibility for decisions about evaluation, eligibility, health and safety, intervention, services, accommodations, placement, crisis response, and other consequential student decisions rather than delegating those decisions to AI.

Additional Information & Guidance

Events Coming Soon

Colorado Virtual AI Summit - March 2025 Highlights

The Colorado Department of Education and Colorado Education Initiative co-hosted a FREE, virtual Colorado AI Summit to celebrate National AI Literacy Day on Friday, March 28th, 2025. The schedule was filled with panels and learning sessions throughout the day. The event focused to the journey of AI in K12 in Colorado, with guest appearances from Colorado Governor Jared Polis and Colorado Commissioner of Education Susana Córdova. View summit session recordings and information here

Additional Events and Opportunities

iLC AI Professional Development Course Pilot
- Educators are invited to take part in a free pilot AI Professional Development course offered by iLearn Collaborative (iLC). This course provides practical insights into using AI in the classroom, covering topics such as effective AI integration, ethical considerations, and addressing common misconceptions. Participants will have the flexibility to engage with the course content at their own pace while earning clock hours. This is a great chance to explore AI's potential in education and contribute feedback to help shape future offerings. Seats are limited, so early registration is recommended. More information and Registration here: iLC Fall 2024 Artificial Intelligence Course Pilot
Free AI AI for Literacy Achievement Workshop Series from Innovate US
- Hosted by InnovateUS in partnership with the Burnes Center for Social Change and led by The Learning Agency, these 60-minute virtual sessions are tailored for parents and caregivers, educators, literacy specialists, program directors, public sector officials and anyone passionate about advancing reading outcomes Click here to learn more and register for these workshops.

AI Guidance for Schools and Districts

The Colorado Education Initiative (CEI) invites educators, school leaders, and stakeholders to explore the Colorado Roadmap For AI In K-12 Education by visiting this link: Colorado Roadmap For AI In K-12 Education. The Colorado Roadmap For AI In K-12 Education provides practical strategies and resources for integrating AI into teaching and learning in Colorado's schools.

As knowledge and understanding of AI continue to evolve, the Colorado Roadmap for AI in K-12 Education will be updated to reflect the latest advancements, insights, and resources.

AI Instructional Resources for Educators

Integrating Artificial Intelligence into K-12 education will involve a wide array of resources, tools and curriculum. This webpage will highlight just a few of these resources and will be updated regularly. Please note, the Colorado Department of Education does not endorse any resource or opportunity, but lists them strictly as an informational service.

Standards

Colorado Academic Standards for Computer Science, Colorado Department of Education

Instructional and Curriculum Resources

AI Resource Hub, Open Educational Resources Colorado
AI Resources for Districts and Schools, Colorado Digital Learning Solutions
Computer Science Resource Bank, Colorado Department of Education
AI Guidance for Schools Toolkit, Teach AI
AI Literacy Curriculum Hub, AI for Equity
AI for Education Resource Hub, AI for Education
GenAI Chatbot Prompt Library for Educators, AI for Education
MagicSchool AI Resource Library, MagicSchool

AI Professional Development for Educators

AI Professional Development For Educators

A variety of trainings and courses related to artificial intelligence are available for educators. This webpage will highlight just a few of these opportunities and will be updated regularly. Please note, the Colorado Department of Education does not endorse any resource or opportunity, but lists them strictly as an informational service.

Training Courses

Title	Organization	Modality	Details	Time Commitment	Cost	Certificate
ChatGPT Foundations for K–12 Educators	Common Sense Media	Virtual	Self Paced and Asynchronous	1 Hour	Free	Yes
AI Basics For K-12 Teachers	Common Sense Media	Virtual	Self Paced and Asynchronous	1 Hour	Free	Yes
AI for Educators Training Course	Google	Virtual	Self Paced and Asynchronous	2 hours	Free	Yes
AI for Educators Training Course	Microsoft	Virtual	Self Paced and Asynchronous	3 hours	Free	Yes
AI 101 For Teachers	Code.org	Virtual	Self Paced and Asynchronous	3 Hours	Free	No
Academic Integration of AI	ILC	Multiple Modalities Available	Instructor-Led	10 Hours	$25	Yes
Ethical and Societal Implications of Artificial Intelligence	ILC	Multiple Modalities Available	Instructor-Led	10 Hours	$25	Yes
Introduction to AI (Artificial Intelligence)	ILC	Multiple Modalities Available	Instructor-Led	10 Hours	$25	Yes
Artificial Explorations for Educators	ISTE	Virtual	Instructor-Led	15 Hours	Member: $186 Non-Member: $249	Yes
Next Level AI Skills for Educators	ISTE	Virtual	Instructor-Led	15 Hours	Member: $186 Non-Member: $249	Yes
AI Foundations for Everyone	IBM	Virtual	Self Paced and Asynchronous	33 Hours	$39 Per Month	Yes
An Essential Guide to AI for Educators	AI for Education	Virtual	Self Paced and Asynchronous	2 hours	Free	No

AI Information and General Resources

A range of information and resources related to artificial intelligence is available. This webpage will feature just a few of these materials and will be updated regularly. Please note, the Colorado Department of Education does not endorse any specific resource or publication, but provides them solely as an informational service.

Information

AI 101, aiEDU.org
Understanding the Impact of AI on Our Kids, Common Sense Media
What is AI?, ISTE
Information for Parents and Caregivers: All About Artificial Intelligence, BritishColumbia.gov

General Resources

Artificial Intelligence in Education, Digital Promise
SAFE Framework, EDSAFE AI Alliance
Artificial Intelligence in Education, ISTE
Human-Centered Artificial Intelligence Definitions, Stanford University
Teachai.org
AI Initiative, Colorado River BOCES

Glossary of Terms

Foundational Concepts

Artificial Intelligence (AI): Technologies designed to perform tasks commonly associated with human intelligence, such as generating language, recognizing patterns, making predictions, or producing recommendations.
Model: A computational system developed to identify patterns and produce outputs, such as predictions, classifications, recommendations, or generated content.
Training Data: The information used to develop an AI model and help it identify patterns. The quality, representation, and limitations of training data influence the system’s outputs.
Generative AI: A type of AI that creates new content, such as text, images, audio, video, code, or data summaries, based on patterns learned from existing data.
Large Language Model (LLM): A type of generative AI trained on large amounts of text to recognize language patterns and generate responses to prompts.
AI-Generated Content: Text, images, audio, video, data summaries, code, or other material produced or substantially modified by an AI system.
Automation: The use of technology to complete or support tasks with reduced manual effort.
Predictive Analytics: The use of data, statistical methods, or AI systems to estimate likely future outcomes, patterns, or needs.

AI Knowledge and Use

AI Literacy: The ability to understand how AI systems generally work, what they can and cannot do, and how to use and evaluate them responsibly.
AI-Competent: Having the knowledge, judgment, and skills needed to understand, evaluate, and use AI thoughtfully, safely, ethically, and effectively.
Prompt: An instruction, question, example, or set of information provided to an AI system to guide its response.
Prompt Fluency: The ability to communicate a clear purpose, relevant context, criteria, examples, and constraints when interacting with an AI system and to revise the prompt when needed.
Human-Centered: An approach that prioritizes human needs, relationships, dignity, agency, expertise, and well-being when designing or using technology.

Evaluation and Oversight

Bias: A tendency, assumption, or pattern that may favor or disadvantage particular perspectives, individuals, or groups. Bias may arise from data, system design, human decisions, or the context in which a tool is used.
Algorithmic Bias: Systematic patterns in an AI system that may produce unfair, inaccurate, or unequal outcomes for particular individuals or groups.
Hallucination: An inaccurate, fabricated, or unsupported AI-generated response that may appear credible or confident.
Deepfake: AI-generated or manipulated audio, video, or imagery designed to make it appear that a person said or did something that did not occur.
Verification: The process of checking AI-generated information against reliable evidence, authoritative sources, disciplinary methods, calculations, or professional expertise.
Human-in-the-Loop: A practice in which a person actively reviews, interprets, approves, revises, or overrides an AI-generated output or recommendation.
Professional Judgment: Decisions informed by professional knowledge, experience, evidence, ethical standards, policy, and understanding of the individuals and context involved.
Transparency: Clear communication about when and how AI is used, what role it plays in a process, and who remains responsible for the resulting work or decision.
Consequential Decision: A decision that may significantly affect a student or staff member, such as placement, grading, intervention, discipline, evaluation, access to services, or allocation of resources.

Data Privacy and Protection

Personally Identifiable Information (PII): Information that directly identifies an individual or could reasonably be combined with other information to identify them.
Sensitive Information: Information that requires heightened protection because its exposure could create risk or harm, such as health, disability, disciplinary, financial, family, legal, or confidential personnel information.
De-Identified Data: Data that has been modified to remove or obscure information that could reasonably identify an individual.
Data Minimization: The practice of collecting, using, and sharing only the information necessary for a specific educational or operational purpose.

Legal Requirements

FERPA: The Family Educational Rights and Privacy Act, a federal law that protects the privacy of student education records and gives eligible students and parents certain rights regarding those records.
COPPA: The Children’s Online Privacy Protection Act, a federal law that regulates the online collection of personal information from children under age 13.

Disclaimer

In an effort to assist the education community and general public, the Colorado Department of Education has gathered the list of resources below, including links to websites. CDE is not responsible for the accuracy, legality, or content of the resources above.

CDE does not endorse any particular resource, product, service, institution or opinion expressed in the resources, nor does CDE necessarily endorse views expressed or facts presented by the providers or institutions.

CDE and its employees do not make any warranty, express or implied, nor assume any liability for the services provided by any entity, program or resource identified on this site or through links from this site. Further, CDE does not provide any funding to the, organizations, programs and resources identified herein and/or through links. CDE does not provide dedicated funding for these resources.