The AI Adoption-Adaptation Framework: A Comprehensive Approach
Greetings, Dear Readers,
Before I begin, I want to thank my readers who have decided to support my Substack via paid subscriptions. I appreciate this vote of confidence. Your contributions allow me to dedicate more time to research, writing, and building Educating AI's network of contributors, resources, and materials.
Dear Amazing Readers of Educating AI,
As the summer sun warms our days, I find myself filled with excitement and anticipation for the upcoming school year. Like many of you, I've spent these months immersed in the world of AI, exploring its potential to transform our classrooms and empower our students. It's been a journey of discovery, challenges, and immense growth.
Throughout this summer, I've been sharing insights and strategies for safe and effective AI implementation. Each article has been a stepping stone, leading us to this moment – the unveiling of a comprehensive framework that I believe will revolutionize how we approach AI in education.
I'm thrilled to present the AI Adoption and Adaptation Framework, a culmination of research, practical experience, and collaborative thinking. But here's the thing: I'm not just theorizing from the sidelines. As I write this, I'm actively working through this process for my own school and classroom. It's challenging, I won't sugarcoat it. However, I've found that approaching AI integration in this logical, step-by-step manner can be incredibly liberating.
This framework isn't just about adopting new technology; it's about opening up a space for true collaboration with our students. As we prepare for the fall, I invite you to join me in this exciting journey. Together, we can create learning environments that harness the power of AI while staying true to our core educational values.
So, let's dive in. Whether you're an AI enthusiast or approaching this with cautious curiosity, I promise you'll find valuable insights in the framework that follows. Remember, we're all learning together in this rapidly evolving landscape.
Here's to a school year filled with innovation, growth, and meaningful connections!
The AI Adoption and Adaptation Framework: A Comprehensive Approach
This framework guides the integration of AI tools in educational settings through a cyclical process, comprising three key phases: Foundational Assessment and Planning, Tool-Driven Curriculum Development, and Educator-Driven Tool Refinement.
Phase 1: Foundational Assessment and Planning
Before diving into AI implementation, stakeholders must engage in thorough discussions about the fundamental aspects of AI integration. This crucial phase sets the stage for everything that follows. It's essential to involve IT departments from the very beginning and facilitate comprehensive discussions with all stakeholders, including teachers, administrators, parents, and students throughout the process.
A. Defining Purpose and Vision
Why integrate AI into student work cycles? This question demands careful consideration. AI's potential to personalize learning experiences, allowing students to progress at their own pace while receiving tailored feedback, is immense. But how does this align with your school's specific goals?
For instance, a high school might envision using AI to provide individualized support for students grappling with advanced math concepts. Picture AI-powered tutoring systems adapting to each student's learning style and pace, offering targeted exercises and explanations. This vision could bridge achievement gaps and boost confidence in STEM subjects, transforming the learning experience.
B. Envisioning Implementation
Now, imagine what AI integration looks like in your specific context. In a high school setting, AI could provide instant feedback on writing assignments, rapidly improving students' skills. Elementary schools might use AI to support differentiated instruction, recommending activities suited to each student's learning level. The possibilities are vast and varied.
During this stage, evaluate the technical aspects of implementation, including system compatibility, infrastructure needs, and potential cybersecurity risks. Consider how AI tools can be effectively integrated into existing curricula and teaching methods.
C. Addressing Logistical and Ethical Concerns
This step involves grappling with thorny issues of cost, safety, and equity. How will you ensure all students have equitable access to AI tools, regardless of their socioeconomic background? What measures will protect student data and privacy? These questions demand thoughtful consideration and robust debate.
Ethical considerations loom large. How can we prevent AI from perpetuating biases? How do we ensure AI assistance doesn't hinder the development of critical thinking skills? Some schools might establish ethics committees to continuously evaluate these issues, ensuring ongoing vigilance.
Address data privacy and security measures, licensing costs, and budgeting for AI tools. Provide platforms for all parties to voice their concerns about privacy, equity, and the impact on learning outcomes.
D. Blocking Out/Sketching Out Policy
Begin drafting policies to guide AI use in the classroom. This initial framework will evolve, but it's crucial to start somewhere. A high school might draft a policy outlining when and how students can use AI writing assistants, specifying that while AI tools are allowed for brainstorming and editing, final submissions must be primarily the student's own work. This policy would set clear expectations and boundaries, fostering responsible AI use.
Policy development should be a collaborative effort involving all relevant parties. Consider creating focus groups to gather feedback on proposed policies. Include sections dealing with data handling, security protocols, and acceptable use guidelines for AI tools.
Throughout this foundational phase, maintain open lines of communication. Regular meetings, workshops, and feedback sessions can help ensure that everyone's perspectives are considered and that the AI integration plan aligns with the school's overall mission and values.
Phase 2: Tool-Driven Curriculum Development
With a solid foundation laid, educators can approach the selection and integration of AI tools with purpose and clarity.
A. Tool Evaluation and Selection
Rigorously assess available AI tools against your established criteria and objectives. This process should involve diverse perspectives from key stakeholders to ensure comprehensive evaluation and buy-in.
Begin by forming an evaluation committee that includes representatives from various groups:
Teachers from relevant subject areas
IT professionals to assess technical requirements and compatibility
Administrators to consider budgetary and policy implications
Students to provide user perspective
Parents to voice concerns and expectations
Special education specialists to ensure accessibility
This collaborative approach ensures that the selected AI tool not only meets educational objectives but also addresses the needs and concerns of all stakeholders. It promotes transparency in the decision-making process and helps build a sense of shared ownership in the AI integration journey.
B. Align Curriculum
Design learning experiences that leverage your chosen AI tools' capabilities without overshadowing core curricular objectives. Begin by clearly outlining your key learning goals and map the AI tools' capabilities to these objectives. Plan specific integration points within the curriculum, create supporting resources like tutorials and best practices, and continuously monitor and adjust based on feedback from students and teachers. Ensure the AI tools foster collaboration and critical thinking, enhancing the learning experience while maintaining the integrity of educational outcomes.
C. Pilot Programs
Small-scale trials are essential. Implement your chosen AI tools in a limited setting, gathering feedback from teachers and students. This real-world testing will reveal potential issues and areas for improvement, informing your broader rollout strategy.
Phase 3: Educator-Driven Tool Refinement
This final phase is crucial for the ongoing improvement and adaptation of AI tools in educational settings. It's where theory meets practice, and real-world insights drive continuous enhancement.
A. Data Collection/Analysis
Conduct a comprehensive feedback process. Gather insights from educators on tool usability, effectiveness, and alignment with educational goals. Student feedback is equally vital – understand their experiences and perceptions. Analyze usage patterns and learning outcomes meticulously.
A high school science department might analyze how an AI-powered virtual lab simulator impacts student learning, comparing test scores, lab report quality, and student confidence levels between AI-using classes and those using traditional methods. This data-driven approach ensures decisions are based on concrete evidence rather than assumptions.
B. Revise Policy, Iterate, and Prompt Improvements
Use your findings to refine policies, improve implementation strategies, and collaborate with AI developers. If students are over-relying on AI for writing assignments, update your policy to require reflection on AI use. If a particular feature of an AI math tutor is causing confusion, work with teachers to develop support materials or modify integration methods.
Don't hesitate to communicate your needs to AI tool developers. Your real-world insights are invaluable for improving these technologies. A confusing interface could be simplified, or a new feature added to address an identified gap.
My Journey with the AI Adoption and Adaptation Framework at My School
At my school, we are looking for a tool that can accommodate scalar integration, accommodating needs at developmental stages. At the same time, we are seeking a product that allows student personalized login and individualized workspace. In addition, we need a system that lets teachers examine and give feedback on chats. At a more local level, I teach a class of seniors who complete a capstone project called the Aries Project. My students need an AI system that is also an amazing research tool.
In my class, writing instruction unfolds along two vectors. On the macro level, we will focus this coming school year on developing and maintaining a purposeful and identifiable voice while making full use of AI tools. On the micro level, students will work methodically through a careful research process: refining questions, conducting a literature review, identifying gaps in research, undertaking a project or fieldwork to fill those gaps, reporting and analyzing findings, and reflecting on the research process. All of this requires a system that allows for careful documentation.
We arrived at this list of requirement by carefully working through the inventory suggested in the AI Adaption-Adoption Framework. At this moment, we are moving forward with a pilot of PowerNotes AI in my senior sections. I will continue to give my readers periodic reports about my experiments as I work through Phases 2 and 3 of the framework over the course of the coming school year. It feels good to have finally committed to a product. Now begins the difficult work of building curriculum that maintains the integrity of the overall purpose of the class and straddles the strengths and weaknesses of the selected product. Wish us luck!!!
If you and your school are in the process of selecting appropriate AI models, check out this recent post. It is the most Select highlightin-depth review of existing educationally-oriented models, if I do say so myself.
The Cyclical Nature of the Framework
Remember, this process is not linear but cyclical. Insights from Phase 3 should inform future planning in Phase 1, creating a continuous loop of improvement and adaptation. Data showing improved engagement among struggling readers might prompt a school to revisit its vision and expand AI integration into other subject areas.
By embracing this flexible, responsive framework, educational institutions can work towards more effective, ethical, and beneficial use of AI in learning environments. It ensures that implementation remains grounded in purpose, aligned with educational objectives, and responsive to real-world experiences. As AI technologies rapidly evolve, this framework allows schools to adapt while maintaining focus on their core educational mission, ultimately enhancing the learning experience for all students.
Thanks for reading!!!
Nick Potkalitsky, Ph.D.
Check out some of my favorite Substacks:
Terry Underwood’s Learning to Read, Reading to Learn: The most penetrating investigation of the intersections between compositional theory, literacy studies, and AI on the internet!!!
Suzi’s When Life Gives You AI: An cutting-edge exploration of the intersection among computer science, neuroscience, and philosophy
Alejandro Piad Morffis’s Mostly Harmless Ideas: Unmatched investigations into coding, machine learning, computational theory, and practical AI applications
Michael Woudenberg’s Polymathic Being: Polymathic wisdom brought to you every Sunday morning with your first cup of coffee
Rob Nelson’s AI Log: Incredibly deep and insightful essay about AI’s impact on higher ed, society, and culture.
Michael Spencer’s AI Supremacy: The most comprehensive and current analysis of AI news and trends, featuring numerous intriguing guest posts
Daniel Bashir’s The Gradient Podcast: The top interviews with leading AI experts, researchers, developers, and linguists.
Daniel Nest’s Why Try AI?: The most amazing updates on AI tools and techniques
Riccardo Vocca’s The Intelligent Friend: An intriguing examination of the diverse ways AI is transforming our lives and the world around us.
Jason Gulya’s The AI Edventure: An important exploration of cutting edge innovations in AI-responsive curriculum and pedagogy.
Tough job, Nick, but someone has to do it:) Your effort to put some shape to a whole school approach is balanced on the fulcrum of risks vs payoff, and you implicitly advise schools to focus their attention on the details, weighing everything from the ethics of use to the mandates of learning and teaching. I think you are getting closer and closer to your goal. One theme that might be emphasized a bit more is your insistence that students are brought into the discussion. I’d say get some student reps on the ethics committee to draft an honor code of use, and emphasize that this is an honor code of learning—not a code of heroism or battle where life and death are at stake. Students might object to your giving teachers the right to surveillance of their chats. Reasonable students will understand that teachers already have the right to monitor students work habits, in fact, the moral obligation. AI is not like talking with peers or texting—it’s more like a library. Student voices are paramount in discussions of ethics that govern their learning.
Speaking of voice, I’d want to open this topic up for students. How do they interpret its meaning? What is the relationship to style and word choice? How do these aspects of writing link to the bot? This discussion circles back to the code of learners, not heroes, not villains. Maybe an orientation to voice task?
I wish you the very best success in this endeavor, for it won't be easy.
Would be great if Principal's and Teachers agree to document the progress of using A.I. in the classroom, just to compare the level of aptitude of students and their progression in education. If A.I. does accelerate student' learning (assuming still in the hypothesis stage), what else can be expanded in the education system with A.I.? (Examples, like manufacturing with 3d printers, Legos, possibly adding more trips to expand student learning.)
Then, I wonder, will schools offer students to craft what matters to them? Let's say creating films, painting or sculpting art, create music and have the students monetize their work to have a step ahead in the "job economy", giving students the advantage to work and add value to the economy based on what matters to them?
Just a thought ...