“The AICE Framework affirms a simple truth: AI is only as transformative as the educators’ ability to use it with purpose and precision.”
By Alex Liu, Dr. Min Sun, {alexliux, misun}@uw.edu, Colleague AI, University of Washington
As artificial intelligence (AI) technologies become more embedded in the tools educators use every day, a new set of professional responsibilities has emerged for educators. AI now supports a wide range of instructional activities—from lesson planning and assessment design to feedback, content curation, and teacher-student interaction. While these tools can improve instructional quality, their impact depends not on the tool itself but on the educator’s ability to use AI in intentional, ethical, and pedagogically grounded ways.
What Does It Mean to Be AI-Competent as an Educator? To answer this question, the Colleague AI research team at the University of Washington introduces the AICE Framework: Advancing Instructional Capacity with Educators through AI. AICE is a research-based, growth-oriented model that defines and supports educator AI competency, with an emphasis on actionability, sustainability, and instructional integration.
Why Do We Need AICE Framework?
AICE is developed to be an observable and actionable framework that scaffolds the growth of educators’ AI competencies, ensuring that the integration of AI is not just technical but instructional. The AICE Framework empowers educators to unlock the full pedagogical value of AI tools and maximizes the impact of school and district investments in AI by turning educational technology into measurable gains in teaching and learning.
While much of the recent attention has focused on AI literacy for students or on broad ethical considerations, the role of educators in this shift is under-examined. Educators are increasingly expected to interpret, adapt, and implement AI-generated outputs, often without sufficient structured and targeted support.
Several existing frameworks emphasize conceptual understanding and ethical awareness. While these are crucial, they often remain at a declarative level (what educators should know), rather than guiding how educators operate AI-powered tools in professional contexts.
AICE fills this gap by defining AI competency through four interrelated dimensions of professional practice, offering a structured and practice-based framework for educators. Each dimension is grounded in practice, informed by research, and designed to scale across educational settings.
- Educator Growth-Oriented: Each dimension is translated into observable and teachable competencies, enabling actionable feedback and ongoing professional learning.
- Instructionally Grounded: The framework prioritizes enhancing teaching and learning, rather than focusing solely on technical proficiency.
- Measurable and Scalable: Competencies can be tracked through platform usage logs and aligned with strategically designed training programs. Insights from implementation inform professional development, tool design, and broader system-level adopting strategies.
- Practice-Driven: The framework emphasizes demonstrated instructional practices that reflect AI competency, beyond mindset or attitudes, anchoring growth in what educators actually do.
The Four Dimensions of AICE
The AICE Framework outlines four dimensions that guide educators in using AI tools intentionally, ethically, and in ways grounded in sound pedagogy. Together, these dimensions make AI integration both measurable and scalable, affirming a simple truth: AI is only as transformative as the educator’s ability to use it with purpose and precision.
1. Functional: Operational Proficiency
Definition: Educators demonstrate fluency in navigating and applying AI tools for instructional and professional tasks.
- F1. Tool Fluency: Educators can confidently operate AI-enabled tools (Colleague AI or others) and tools relevant to their instructional context.
- F2. Purposeful Application: Educators can identify and match appropriate AI tools/features to specific instructional or professional tasks.
This foundational layer ensures educators can consistently and skillfully engage with AI as part of their professional toolkit. This dimension establishes the technical foundation required for more pedagogically and ethically sophisticated uses of AI.
2. Content: Contextual Adaptation
Definition: Educators use AI to access and transform content in ways that are relevant, coherent, and aligned with instructional needs.
- C1. Content Retrieval: Educators can obtain instructional materials, insights, and data from AI systems aligned with curriculum goals.
- C2. Instructional Adaptation: Educators can reshape AI-generated content into formats, modalities, and tones suitable for their specific classroom, student needs, and institutional goals.
- C3. Critical Inspection: Educators can critically evaluate the accuracy, usefulness, and relevance of AI-generated content and operate AI tools to revise and refine the content.
AI-competent educators are not passive content consumers; they critically reframe and customize AI outputs for their own educational context.
3. Pedagogical: Instructional Enhancement
Definition: Educators integrate AI to improve instructional design and delivery, student engagement, and professional efficacy.
- P1. Pedagogical Design: Educators use AI to inform and refine instructional sequencing, modality selection, and alignment with learning objectives, improving lesson quality and classroom delivery.
- P2. Instructional Integration: Educators enhance student learning and engagement by embedding AI tools into lesson planning, delivery, and assessment.
- P3. Professional Optimization: Educators use AI to advance on-job professional learning outcomes, improve evaluation performance, and fulfill a more balanced set of professional obligations across disciplinary, individual, institutional, and societal responsibilities.
This dimension positions AI not just as a standalone tool, but as a collaborator in instructional and professional improvement.
4. Ethical: Responsible Use and Modeling
Definition: Educators ensure the responsible use of AI and model ethical digital practices for students.
- E1. Responsible Operation: Educators manage AI use in compliance with ethical norms and institutional code of conduct, such as data privacy, transparency, and inclusivity.
- E2. Ethical Modeling: Educators reflect on their own AI use to guide students in developing ethical reasoning and responsible digital behaviors in AI-supported environments.
- E3. Critical Inspection: Educators can critically evaluate AI responses and identify and correct biases and misinformation in the generated information.
Educators are not only AI operators. They are ethical stewards, modeling what thoughtful, human-centered AI use looks like for their students.
(Note. Interactive poster generated on Colleague AI. Click to try Generate Interactive.)
Framework Foundations
The AICE Framework integrates and builds upon three foundational bodies of work:
- AI Literacy Framework (Stanford Teaching Commons): Emphasizes understanding of AI’s functions, ethical concerns, and instructional applications. This framework has been inspired by Miao and Cukurova’s AI competency framework for teachers (AI CFT), which is intended to support the development of AI competencies among teachers to empower them to use these technological tools in their teaching practices in a safe, effective and ethical manner.
- Professional Obligation in Enacting Curriculum (Herbst & Chazan, 2020): identify educators’ responsibility to discipline, students, institutions, and society. This theoretical foundation helps us position AI use within the set of professional obligations.
- Instructional Core (City et al., 2009): Highlights the dynamic relationship between teacher, student, and content as the engine of learning outcomes.
These anchors ensure the AICE Framework is both aspirational and actionable, linking new technologies to enduring principles of instructional quality and teaching effectiveness.
Implications for Practice
For Teachers and Coaches: AICE can inform self-assessment tools, professional learning pathways, and micro-credentials focused on AI integration.
For Professional Development Designers: Each AICE competency provides a unit of learning that can be scaffolded across beginning, intermediate, and advanced stages of AI use.
For Tool Designers: The framework offers design principles for building educational AI tools that are transparent, adaptable, and instructionally and professionally relevant.
For System Leaders and Policymakers: AICE can guide certification criteria, district AI-readiness strategies, and equity-focused implementation roadmaps.
Contact and Resources
Want to learn more, collaborate on research, or implement the AICE Framework in your school or district? Connect with us:
- Share your thoughts on the AICE Framework with us on social media using #AICEframework.
- Planning for back-to-school professional learning? Book a meeting [link] with our team to co-design a training that aligns with the framework.
- Questions or partnership inquiries? Reach out to us at info@colleague.ai
[The AICE competency rubric will be available soon…]
