The Science of Learning and Implementation

September 16, 2026September 16, 2026Dr. Rachelle Dené Poth Leave a comment

Guest post by Cherry-Anne Gildharry

Change Agent I Designer I Coach I Educator of 33 Years I

“In all my years as a professor of this Postgraduate Teaching Program, I have never had someone use knowledge gained from this course and the related professional development (PD) sessions offered at the university level in such meaningful, innovative, and excellent ways to create a teaching portfolio and lesson plans with ongoing reflective implementation structures. Cherry-Anne, I am extremely impressed by your excellent learning portfolio and the meaningful lessons that you implemented throughout this course” – this is a summary of my professor’s message to me in 2001 when I pursued my Post Graduate Diploma.

Whether I am designing learning for students, teachers, and leaders or engaging in professional learning, my creations and implementations have always been aligned with the four powerful elements of the science of learning and implementation: neurodesign, plasticity, connectivity, and continuity. These elements have guided my effectiveness and success as a teacher, department chair, teacher mentor, school-based and district- level coach, a virtual instructional coach, and a professional learning designer, developer, facilitator, and change agent.

I received an A in Teaching Practice in 2001 for my designs and implementation; a grade that numerous colleagues said I would never get because my professor had not given an A in many years to Post Graduate Diploma in Education (DIPED) students. My professor was extremely impressed by my work and encouraged me to attend professional learning workshops in the U.S. to gain additional strategies because of my passion for learning and implementing at extremely high levels.

My first visit to the U.S. was in 1988, when I went to New York on a one-month vacation, but even at that time, the exchange rate was high, which increased in 2001, making professional learning workshops in the U.S. extremely expensive for me to attend. However, I sent my dream out into the universe and willed it into reality, as advised in the book, The Secret, which I was introduced to in 2006.

With 15 years of teaching experience in Trinidad and Tobago and a track record of success, I applied to teach in the U.S. in April 2007. After meeting many criteria to become a teacher in the U.S.and attending an interview scheduled in the Bahamas, where I taught a full lesson that incorporated strategies gained from my postgraduate professional learning opportunities, I was offered a teaching job the next day. So, bet your bottom dollar, I was ready to gain ideas and excel at implementing strategies attained from professional learning.

My very first professional development session in the U.S. was in August 2007, when I attended a district math workshop that shared how to connect the use of Texas Instruments calculators to math concepts. A birthday candle lab was referenced for collecting data when teaching the topic of Scatter Plot. Of course, I took extensive notes and documented my implementation action plans, which materialized into a final for now learning products using assessment techniques that I am accustomed to from my country of origin. I have attended numerous professional learning sessions since then, and I have had numerous opportunities to individually facilitate professional learning, with an ongoing focus on growth in each design I develop.

I am immensely thankful for the opportunities to attend and facilitate workshops in the U.S. So, to pay it forward, the following parts of this article provide a deeper dive into the Science of Learning and Implementation strategies I learned and used, specifically connected to the four powerful elements: neurodesign, plasticity, connectivity, and continuity.

Infographic titled 'Applying the Science of Learning to Professional Learning' with four sections: Neurodesigns, Plasticity, Connectivity, and Continuity, each outlining principles for enhancing professional learning based on neuroscience.

Neurodesign- Minimize Cognitive Load and Maximize the Working Memory

It is important to focus on the elements of neurodesign to minimize cognitive load and ensure that learners are not overwhelmed or burned out when designing and implementing professional learning.

Create designs that focus on content specificity to maximize human brain processing and long-term working memory.
Minimize cognitive load and information overload to prevent overwhelmingness and ensure that information is sent to long-term memory.
Incorporate designs and principles such as visual hierarchy and others that support the neuroscience of how the brain processes information and what it is naturally drawn to.

When content is overwhelming, the amygdala, the system responsible for routing information based on your emotional state, takes over and sends information to the reactive brain, thus blocking it from entering the prefrontal cortex. The reactive brain responds to information instinctively instead of through thinking.

Professional learning designs and implementations must ensure that information is channeled to the prefrontal cortex (PFC) instead of the reactive brain, because the PFC is responsible for cellular changes that produce long-term neural networks. Unfortunately, “the prefrontal cortex is actually only 17 percent of your brain; the rest makes up the reactive brain” (Willis, 2009).

Designs that tightly pack content onto slides and have too many slides make learners overwhelmed and burned out. We must use profound neurodesign principles that focus on:

Organizing and delivering content to provide specificity, clarity, precision, connectivity, simplicity of design, consistency, and calmness for learning.
Including elements that the brain is naturally drawn to, such as:
- Varying sizes of shapes and text.
- Consistent fonts, themes, colors, and style.
- Distinct guiding titles and guiding arrows.
- Contrasting colors and icons.
- Chunked and well-spaced text.
- Clutter-free slides with white space that improve readability.
Adding relevant pictures and images and allowing them to profoundly communicate a thousand words.
Using visual hierarchy or infographics in which content is prioritized, and the importance is shown to guide users through a seamless and aesthetically appealing flow that hooks and deepens learning.

Is your professional learning design using neurodesign principles to ensure that information is sent to the prefrontal cortex, which is only 17 percent of the brain?

Plasticity- Heighten Neurogenesis and Neuroplasticity to Promote Enduring Learning

The Science of Learning and Implementation places significant emphasis on plasticity, more specifically, neurogenesis and neuroplasticity, when designing professional learning. To grow new neurons, strengthen neural connections, and develop high-level executive functioning skills, we must implement the following essential practices and ideas:

Create professional learning that promotes neurogenesis and neuroplasticity. If not, information is shared, but enduring learning does not take place.
Design rigorous learning opportunities that fire and wire neurons together, provide repeated activation, and allow for frequency and recency of ideas to strengthen neural synapses.
Include tasks that require participants to engage in writing and other science of learning styles to grow new neurons and strengthen neural networks.

It was once believed that the brain did not have the ability to grow new neurons, but this belief was disproven in 1960 by Joseph Altman and replaced with the belief of neurogenesis, the birth of new neurons. Neurogenesis is understood to be a lifelong process; adults can grow new neurons. The concept of neuroplasticity, or brain plasticity is the brain’s ability to develop stronger structures and networks; it dates back to 1890.

Once information enters the prefrontal cortex, it is transmitted to short-term memory by synapses and neurons. If the information is deemed important, then and only then is it sent to the hippocampus and afterwards to the neocortex for long-term storage. It is not at all automatic that information is transferred to long-term storage.

So, how do we develop new neurons and fire and wire neurons together to strengthen neural networks for long-term storage when we design professional learning?

Neurodesign components explored in the neurodesign section must be a number one priority to ensure content is sent to the pre-frontal cortex and the process of neuroplasticity is heightened.
Use it or lose it: Repetition is key in strengthening neural networks. Repetition here does not relate to rote memory and step-by-step memorization, but to repetition of in-depth and profound ideas that help learners generate long-term connections for retrieval and application. Active engagement is critical!
Create larger and stronger networks of neurons by seamlessly incorporating and blending multiple research-based strategies that connect ideas from slide to slide and within slides.
Ensure that learners are not passive recipients but instead are given numerous and extensive opportunities to be active participants. Learners must be given time to work on high-level tasks and create high-level learning products.

Is your professional learning design firing and wiring neurons together and developing strong neural networks so that information is deemed important and sent to the neocortex?

Connectivity – Connect to Different Regions of the Brain, Pedagogy, and Andragogy

A focus on the power of connectivity when creating professional learning designs is extremely crucial if we want learners to generate excellent learning artifacts and continuously apply learning at a high level. Connectivity strengthens neural connections and heightens learning.

Cognitive content and tasks must be designed to ensure multiple regions of the brain connect and communicate in order to strengthen neural synapses and complex neural networks.
Learning must connect to adult learning principles to increase the opportunities for new neurons to be generated and stronger neural networks to be formed.
Knowledge gained must be interconnectedly applied and implemented to deepen learning and strengthen neural connections.

To significantly improve connectivity, it is essential to ensure the implementation of the following science of learning practices.

Personalize learning by focusing on goals. Your learners have prioritized goals and learning outcomes, so learning designs must provide opportunities for learners to focus on their goals during the learning session and create learning artifacts that align with them.
Include brain-related learning styles and multiple sensory designs that the brain is naturally drawn to (not the myth of fixed learning styles). Some of these are visual, auditory, reading/writing, and kinesthetic. Ensure that both the left and right hemispheres are involved in learning to strengthen neural connections.
Provide learners with choice to meet their needs and have relevance for learning. Both adults and young learners need to see the relevance for learning and choose tasks that are relevant to their needs, goals, and experiences.
Incorporate writing into learning sessions. Writing deepens connectivity; “the process of creative formulation and physical writing lights up a whole lot of the human brain. Language, cognition, memory, visual processing, planning and control, and the ability to make associations between unrelated concepts all come into play” (This Is Your Brain on Writing, n.d.).
Learning designs must incorporate the connectedness of all pedagogical strategies to show how these can be used concurrently. Use flowcharts or infographics to communicate the big picture. Too often, strategies are shared in isolation, but this goes against the science of learning and implementation theories.

Do your learning designs use pedagogical and andragogical principles that connect both regions of the brain to develop long-lasting neural connections?

Continuity – Optimize Brain Pruning and Mature Complex Processes

Excellence in designing professional learning is not a one-time situation. Instead, it is a dedicated journey of ongoing creations, metacognition, and revisions. Learning does not stop at a PL session; it has only just begun. Continuity is key in the science of learning and implementation.

Continuity is an active process of recursion that impacts brain convolution and results in mature complex processes, integration of ideas, and high cognitive creations and implementations.

The science of learning and implementation stresses the importance and need for continuity for brain functions such as pruning, recursion, and brain convolution to take place to impact excellent and enduring learning. How can we promote continuity of learning after the professional learning session?

Time must be carved into the learning design for participants to reflect on their goals and learning artifacts and develop an implementation timeline with steps for ongoing revision.
Young and adult learners need to understand the principles behind having a growth mindset. Develop a simple framework for learners to complete and focus on ways to deal with setbacks during implementation. Here are two examples of growth mindset initiatives I developed from scratch and spearheaded: SAGE Growth Conversations and the GROW Growth Mindset Framework. Teachers were asked to collaborate with me after I created a framework, timeline, and artifact.
Incorporate self-learning activities that develop self-directed and autonomous learners.
It is said that emotions are closely related to the brain and learning. How can you tap into your participants’ emotions to make a lasting impact for high-level continuity? I have used images, questions, videos, stories, and more to promote connectivity.

Knowledge gained from professional learning can easily be forgotten or lost if we do not incorporate strategies that ensure continuity.

How are you designing professional learning to guarantee continuity?

As a unique autodidactic learner from the age of three and a half years old, I have always been riveted in my purpose, goals, and my innate passion for learning and continuously applying knowledge gained. My wife, Melanie Gildharry, an educator turned business analyst, is blazing trails and creating excellent products at extremely high levels in the business world. Like me, she is also an autodidactic learner who follows a heutagogy learning model and has a deep-seated passion for learning and creating.

When I analyze the common principles, traits, and mindsets we share from attending and presenting professional development, it narrows down to the four science of learning principles of neurodesign, plasticity, connectivity, and continuity. For us, even when these elements were not incorporated into PL sessions we attended, we ensured that our learning products included them. No professional learning session is ever wasted for us!

Professional learning attendees, the onus is also on you. Your learning artifacts and implementation steps must focus on neurodesign, plasticity, connectivity, and continuity, even if these are not present in your professional learning sessions.

It is important to remember that “Knowledge is not power until it is applied” ~ Dale Carnegie. However, know that there will be failures in the journey of applying the science of learning and implementation to design excellent professional learning and create learning artifacts, but always remember the profound and inspiring words of Albert Einstein:

“Failure is success in progress”
“You never fail until you stop trying”
“The only sure way to avoid making mistakes is to have no new ideas”
“We cannot solve our problems with the same thinking we used to create them”

Keep in mind, professional learning designs in the U.S. are used as exemplars for learners around the world. Therefore, it is extremely important to ensure that we continue to incorporate the science of learning and implementation principles into professional learning designs to generate exceptional learning sessions.

Let’s lead professional learning designs that focus on The Science of Learning and Implementation elements.

References

Ford, D. J. (2011, July 20). How the Brain Learns – Training Industry. Training Industry. https://trainingindustry.com/articles/content-development/how-the-brain-learns/

Neurogenesis and Neuroplasticity: Similarities and Differences. (n.d.). http://Www.re-Origin.com. https://www.re-origin.com/articles/neurogenesis-and-neuroplasticity

Understanding Brain Connectivity: How Our Minds Develop and Adapt. (2020). Brainbalancecenters.com. https://www.brainbalancecenters.com/blog/understanding-brain-connectivity-how-our-minds-develop-and-adapt

What Is Neurodesign? | Built In. (2024). Built In. https://builtin.com/articles/neurodesign

About the Author Cherry-Anne Gildharry

I have a Bachelor of Science in Mathematics, a Master of Science in Education, and a Graduate Certificate in Instructional Coaching. I have 33 years of experience in education, and counting, and I have taught math in Trinidad and Tobago, North Carolina, Iowa, and Texas.

Throughout my career, I have held numerous roles, such as a High School and Middle School Math Teacher, Department Chair, and Teacher Leader. I have also served as an Algebra 1 and Geometry Lead Teacher, Workshop Creator and Facilitator, and Marzano’s Demonstration Teacher. Additionally, I served as a School-Based and District Coach, Leadership Coach and Collaborator, Learning Design Strategist, Virtual Instructional Coach, Professional Development Auditor, and Professional Development Content Creator.

I have a track record of success both as a teacher, curriculum designer, school-based and district-level coach, and virtual instructional coach. In North Carolina, 100% of my Algebra 1 and Geometry students achieved passing scores for consecutive years using tasks I designed from scratch. As an instructional coach, consultant, and change agent, I have coached teachers in North Carolina, Iowa, Texas, and Teach for America teachers to attain similar track records. I currently help develop courses for teachers and coach teachers in various states across the United States.

I am a lifelong and self-driven learner and educator with a growth mindset and an undeniable passion for education. I am beyond blessed to be married to the woman of my dreams; a former Chemistry teacher, technology coach, E-Learning Designer, and Curriculum Manager turned Business Analyst, and a lifelong learner and educator. Together, our energies drive our success, philosophies, dedication, career, and family goals even more. We are work-life balance advocates. We believe in working extremely hard but intentionally setting time aside to recharge and rejuvenate!

About Rachelle

Dr. Rachelle Dené Poth is a Spanish and STEAM: What’s Next in Emerging Technology Teacher. Dr. Rachelle Dené Poth is an edtech consultant, presenter, attorney, author, and teacher of Spanish and STEAM: Emerging Technology. Rachelle has a Juris Doctor degree from Duquesne University School of Law and a Doctorate in Instructional Technology. Rachelle’s focus was on the need for professional development for educators, specific to Artificial Intelligence. Rachelle specializes in Artificial Intelligence, AI and the Law, AI and Healthcare, Cybersecurity, and STEM. She has more than 8 years of experience teaching and presenting on AI in her classroom, as well as working with educators worldwide.

Rachelle is currently serving as the Grant Coach for an initiative through ISTE+ASCD and Pinterest. Rachelle works with a Task Force from 12 districts in the United States and assists with policy revision, professional development, and the design of digital wellness resources for students, educators, and families.

Rachelle provides professional development related to AI policy and implementation to school districts, universities, and organizations. She also presents and provides keynotes on AI at state, national, and international events and in schools. Rachelle also provides AI training for other industries, including business, healthcare, and legal fields.
Rachelle is an ISTE-certified educator and recipient of the ISTE Making IT Happen Award and several Presidential Awards for volunteer service to education. Rachelle received the EdTech Trendsetter Award from EdTech Digest in 2024 and 2026.

She is the author of ten books, including “What the Tech? An Educator’s Guide to AI, AR/VR, the Metaverse and More! and “How to Teach AI: Weaving Strategies and Activities Into Any Content Area.” She has written curricula and courses on AI for all levels.

She is also a blogger for Defined Learning, EdTech K12, Edutopia, Getting Smart, and Tech & Learning. She is the host of the ThriveinEDU podcast, ISTE’s Learning Unleashed podcast, and The Lift by Amazon on BAM Radio Network. Contact Rachelle for your event!

Image featuring Dr. Rachelle Dené Poth with her name, social media handle, and a selection of her book covers displayed along with a brief description of her roles as an educator, author, attorney, and consultant.

AI in Education vs. AI Education: Why Policy and Practice Must Move Together (Part I)

July 21, 2026July 18, 2026Dr. Rachelle Dené Poth Leave a comment

Posted via my LinkedIn page and ThriveinEDU newsletter.

Over the past year, I’ve had the tremendous opportunity of working with district leaders, educators, students, families, and even policymakers across the country. Throughout this work, one question has come up repeatedly:

Is there a difference between AI in education and AI education?

I will pause…to let you consider.

For me….

My answer is always yes.

Although the terms are often used interchangeably, they represent two distinct priorities. AI in education focuses on how artificial intelligence is being implemented in teaching, learning, and school and district operations.

AI education focuses on helping students understand artificial intelligence itself. They know how it works, how it generates information, how it influences decisions, and understand that it will shape their futures. These two phrases are not the same, and not having a clear distinction creates gaps in policy, instruction, and student preparedness.

When people hear “AI in education,” they often think about tools and how they are being used. One example I have shared is a student using AI to generate a first draft of an essay. AI in education asks: What classroom expectations and policies should guide this use?

AI education asks: Does the student understand how the AI generated that response, what biases may exist, and how to evaluate its accuracy?

In my work, I’ve come up with a series of questions as starting points for conversations:

How are schools using AI platforms?
What tools are allowed, and who is vetting them?
How are educators integrating AI into instruction?
What policies are in place or are needed to guide its use?

These are just a few of the important questions that need to be considered and answered specifically for AI implementation. However, there is another conversation that is equally important to have in our schools, and the focus is on AI education.

AI education is more than tools. It is about preparing students to understand artificial intelligence, how it works, how it processes and generates information, how it influences decisions, and how it will impact their future.

Both are important, and from the many opportunities I’ve had to work with educators and speak with students, it seems there is confusion. We need to align on the meaning of these terms so we can fully provide the learning support our students and schools need.

What I’ve Seen in Schools

Across the districts I have supported, interest in artificial intelligence has increased. During the past year, I’ve seen the mindset shift from one of hesitation to exploration.

Educators are curious. Students are experimenting. Leaders are asking thoughtful questions and also seeking guidance for their schools.

But when it comes to AI implementation, whether school- or district-wide, it is not always consistent, and consistency matters.

Some schools are exploring AI tools without clear guidance, while others are restricting its use out of caution. And some are writing policies without fully understanding and addressing classroom realities. And this is why I recommend that administrators involve educators, students, and families in the conversations or create a task force specific to AI. By doing this, schools can better understand how AI is being used, the support students receive at home and at school, and what their needs are. Because in many places, students are using AI outside of school without learning how to use it responsibly, which creates yet another gap.

It creates a gap between access and understanding and between use and responsibility. However, by establishing a policy and ensuring consistent implementation, we can work toward closing the gap and making meaningful progress in an increasingly AI-surrounded world.

This Also Matters for Legislators

Artificial intelligence is not a future issue. It is a present reality shaping:

How students learn
How educators teach
How information is created and consumed
How future work will be defined

Legislators throughout the country are increasingly being asked to make decisions about data privacy, student protection, AI tool access, digital equity, and workforce readiness. These are important decisions that carry real implications for classrooms.

But having an effective policy requires more than regulation. It requires understanding how AI is actually being used and how it should be used in educational environments.

I’ve reached out to and spoken with legislators from different states over the past six months, and some of the common thoughts around policies are:

-Policies that focus only on restrictions will fall short.

-Policies that ignore implementation will create confusion.

And policies that fail to address AI education will leave students unprepared.

Why Does This Matter for Educators

Educators are at the center of the shift in classroom instruction, especially now with AI. Educators are adjusting to technological changes, navigating new tools, fielding student questions, balancing innovation with responsibility, and working to support student learning in real time.

My own research showed that many educators are being asked to adapt without the necessary guidance or support. I’ve been asked many times:

“Do I need to teach about AI?”
“What should I allow in my classroom?”
“How do I know if students are using AI appropriately?”
“How do I design lessons where students are still doing the thinking and not having AI do the work for them?”

These are not simple questions with simple answers. They are instructional, ethical, and professional questions. And these questions require communication and preparation, and not just policy. It is a process and an iterative one.

The Risk: Where Do We Focus?

If we focus only on AI in education, we risk:

over-reliance on tools
inconsistent expectations
unclear boundaries for educator and/or student use
missed opportunities for deeper learning

If we focus only on AI education, we risk:

teaching theory without application
disconnecting learning from real-world tools
failing to prepare students for how AI is actually used now and the future possibilities

The solution? It’s not about choosing one over the other, but rather finding a way to align both in our schools, because that is how we guide students toward success.

What Alignment Looks Like in Practice

Across the districts I support, I see the most progress when schools intentionally connect AI in education with AI education. Schools making progress have:

Clear Expectations for Use: Students and educators understand when and how AI can be used, and what responsible use looks like.

Instruction That Builds Understanding: Students are not just using AI. They are learning how it works, the importance of questioning it, and how to evaluate its outputs.

Professional Learning for Educators: Teachers have the time, support, and guidance to meaningfully integrate AI into instruction. And this is consistent and ongoing throughout the year.

Policy That Reflects Practice: Policies are designed with classroom realities in mind and focus on guidance rather than restriction. Schools have committees or task forces to be better informed and able to adjust as needed.

Ongoing Conversations: Schools engage students, educators, and families in discussions about AI, digital wellness, and responsible technology use.

AI Literacy Is Workforce Literacy

The students graduating now will enter a workforce shaped by automation, intelligent systems, and rapidly evolving expectations regarding their skill sets.

According to global workforce trends and the World Economic Forum’s Top 10 Skills for 2030, technological literacy, AI understanding, and cybersecurity awareness are among the most in-demand skills for the coming decade.

And when it comes to AI literacy, we must remember that it is not just about technical knowledge. It is about:

critical thinking
ethical decision-making
information evaluation
adaptability
communication

These are the essential, in-demand skills that must be intentionally taught in our classrooms through a variety of learning opportunities and supports.

We Share Responsibility

Preparing students for an AI-shaped future requires collaboration between:

Educators
School leaders
Policymakers
Families
Communities

Each group plays a role in preparing students and guiding them toward future success.

Legislators can create frameworks that support responsible implementation and that focus on protecting students.

Educators can design learning experiences that build understanding and confidence and model appropriate and safe use of AI for students in their classrooms.

School leaders can align systems, expectations, and communication.

When everyone is involved in navigating the AI space together, it creates coherence.

Artificial intelligence is changing education. It is moving fast, however, when it comes to our work, the most successful systems are not those that move the fastest. Success comes with clarity. Clarity about expectations. Clarity about purpose. Clarity about how technology supports learning rather than replacing it.

Across the country, I have seen schools and districts working to build that clarity and consistency. They are asking questions and working together to find answers as a start. They are not seeking perfection, just ongoing progress.

Preparing students for the future

We cannot prepare students for the future by focusing only on the tools they use today. We must also prepare them to understand the systems shaping their world.

And the decisions we make now, at both the classroom and policy level, will determine whether students are ready not just to use technology, but to think critically, act responsibly, and lead in a world where artificial intelligence is part of everyday life.

That is the work ahead, and it is definitely the work worth doing.

AI in education matters, but AI education matters just as much. Think about your school and district. Are these distinctions clear? Stay tuned for part II in the series, with actionable strategies, and contact me to provide training for your school.

Subscribe to my ThriveinEDU newsletter to stay informed.

If Your Organization Is Beginning This Work

I help schools and other organizations (law firms, healthcare professionals, business owners) implement AI responsibly through policy guidance, professional learning, and classroom-ready strategies grounded in both instructional practice and legal insight.

My sessions focus on helping teams:

• understand what AI can and cannot do

• recognize responsible-use considerations

• build confidence using emerging tools

•align implementation with organizational priorities

If your school, district, or organization is beginning conversations or looking to dive in and learn more about AI policy, professional learning, or responsible implementation, I’d welcome the opportunity to support your next steps through leadership workshops, keynote sessions, or strategic planning partnerships.

Preparing people is what makes AI implementation successful. Contact me via bit.ly/thrivineduPD for my training and speaking services.

Image generated by Canva AI that shows neural network and AI in the center.

Leading Forward in AI, Part VII: Building Professional Learning Systems That Last

July 7, 2026July 3, 2026Dr. Rachelle Dené Poth Leave a comment

How do we build sustainable professional learning systems for educators?

In the first six parts of this series, I shared what I’ve been learning from working with district leadership teams across the country as they navigate artificial intelligence, digital wellness, and purposeful technology use. Over the course of my Leading Forward Series, I’ve shared what I’ve been learning from working with district leadership teams across the country as they navigate artificial intelligence, digital wellness, and purposeful technology use.

We’ve explored:

curiosity over fear
educator readiness
leadership and systems
measuring what matters
trust as the foundation
designing for thinking
true partnerships

These conversations led to a bigger question, which is how do we build systems that last? Because the goal is not to have one answer and end the conversations, it is to prepare schools to adapt continuously to be ready to meet the changing educational landscape.

After reviewing my notes, reflecting on conversations, and gathering insights from surveys, I noticed some key takeaways.

One-time Training Is Not a Strategy

One of the most common concerns expressed was that professional development is often seen as a one-time event. Sometimes it is a workshop, maybe a keynote, or a specific training session focused on a new platform or a mandate. These are all valuable learning experiences, but they are not enough, especially with the rapidly changing technologies we are seeing in education and the world.

Artificial intelligence is evolving so fast, new tools are emerging constantly, and expectations are frequently changing or, at times, may be inconsistent. A single session is not enough to prepare educators to adapt to ongoing changes. Schools need sustainable systems for learning.

What Sustainable Professional Learning Looks Like

In the districts I support, I have noticed several common characteristics that have positively impacted the educators and ultimately students and families.

1. Learning is ongoing, not just an event.

It is embedded in the school year. Educators have regular opportunities to explore new tools, reflect on practice, share strategies, and learn from one another. It should also be a time for educators to explore new ideas in the classroom and partner with students to gather additional feedback and to learn together.

2. Learning Is Collaborative

Powerful learning does not happen in isolation. It happens when educators work in teams, have opportunities to observe one another, take time to share successes and challenges, and build collective understanding. Collaboration like this leads to system-wide progress and better outcomes for educators and students.

Continue reading via my newsletter on LinkedIn.

If Your Organization Is Beginning This Work and is Seeking Consistent Support, Contact me:

My sessions focus on helping teams:

• understand what AI can and cannot do

• recognize responsible-use considerations

• build confidence using emerging tools

•align implementation with organizational priorities

Preparing people is what makes AI implementation successful. Contact me via bit.ly/thrivineduPD for my training and speaking services or reach out via email, Rdene915@gmail.com.

Leading Forward, Part VI: Designing for Thinking in an AI-Driven Classroom

June 25, 2026June 23, 2026Dr. Rachelle Dené Poth Leave a comment

In the first five parts of this series, I shared what I’ve been learning from working with district leadership teams across the country as they navigate artificial intelligence, digital wellness, and purposeful technology use. There have been many conversations, brainstorming sessions, questions answered, and then more questions posed.

In each of these conversations, we have considered, and I have asked a lot of questions. But one of the most common questions that I’ve heard and considered is:

How do we know, and how do we make sure, that students are still doing the thinking? Why is this a popular question? Because in an AI-driven world, having access to answers is no longer the challenge. Answers are available everywhere and instantly. Thinking is now the challenge.

Making A Shift

When it comes to education and creating learning experiences, a large part of schooling is focused on helping students find the right answer. Educators consider the support needed to guide students in working through challenges and developing their skills as they build content knowledge. Now, that landscape has changed. And, it has changed fast.

Students can generate responses, explanations, and even write entire essays within seconds using the AI tools available to them. But this new reality leads to understandable concerns. Questions such as:

Are students relying too much on AI?
Do they know how to evaluate the information?
Is their own thinking being replaced?
What does learning look like now?
What should learning look like now?

These are some of the important questions we need to be asking. And the answers will shift our focus. Education still plays a critical role in developing essential skills, but it is evolving and requires more intentional planning and heightened awareness of available technologies, along with mindfulness about how to leverage them.

Will AI Replace Our Thinking? Not Unless We Let It

We know that artificial intelligence can support learning in powerful ways. It can:

provide feedback
offer explanations
generate ideas
support revision
help students explore concepts
serve as a thought partner for educators

But without clear expectations and a consistent message, it can also shortcut learning opportunities.

So what makes the difference?

Continue reading via my newsletter on LinkedIn.

If Your Organization Is Beginning This Work

My sessions focus on helping teams:

• understand what AI can and cannot do

• recognize responsible-use considerations

• build confidence using emerging tools

•align implementation with organizational priorities

Preparing people is what makes AI implementation successful. Contact me via bit.ly/thrivineduPD for my training and speaking services.

Prompting the Physics Mind: The Role of AI Tools and Prompt Engineering in Addressing Metacognitive Learning Resource Gaps Among Undergraduate Physics Students

June 16, 2026June 2, 2026Dr. Rachelle Dené Poth Leave a comment

Guest post by Patricio Bastida Nava, undergraduate researcher at the University of Massachusetts Amherst.

“Give me six hours to chop down a tree, and I will spend the first four sharpening the axe.”

— Abraham Lincoln

The first time I realized how badly AI could fail a student was during my first semester at UMass Amherst. I was studying for my second Physics 181 midterm. I just couldn’t understand projectile motion and struggled with kinematics. None of it was clicking. So I did what felt productive: I asked ChatGPT to build me an interactive visualization, a map of how the problems fit together, something I could study from. The artifact it produced was beautiful. I felt prepared. There I was looking at the exam when I knew instantly that understanding the concept and actually solving a problem were two entirely different things. The exam did not ask me to recall relationships. It asked me to set up equations, choose coordinate systems, and grind through algebra with variables. I had outsourced the thinking and memorized the output. I felt deeply frustrated. I just didn’t know what was wrong with me or how to fix it. The gap between what I thought I knew and what I could do had never been so big.

That failure changed how I used AI. I stopped asking it to explain and started asking it to coach: generate problems, demand my reasoning before giving feedback, and adapt difficulty to my mistakes. My learning improved. As usual, this experience led to a very important question — if I was going through this situation, what was happening to everyone else?

Weeks later, Dr. Torrey Trust ran an exercise in her AI and Education seminar that gave me part of the answer. She asked students — biology, computer science, engineering, economics — what tool they turned to when a concept was not clicking. Nearly every hand pointed in the same direction: ChatGPT. Not because anyone had tested it against alternatives. Not because it produced the best learning outcomes. Because it was fast, and speed feels like understanding. Researchers call this the learning illusion: the subjective sense that you have learned something when you have only been exposed to it. In education research, metacognition (the practice of thinking about how you are learning and whether it is actually working) is the primary defense against this illusion. But metacognition is effortful, and ChatGPT is effortless. That is the trap.

I am a first-year physics student, but I am also a researcher. I never attended a traditional school. I earned my high school diploma in Mexico by examination alone. Everything I know, I taught myself, and for much of that process, AI was one of the only resources I had. That experience gives me no patience for the argument that AI is simply a shortcut. For students like me, it was the classroom. But it also gave me no illusions about its dangers, because I have lived both sides: the version of AI that builds understanding and the version that quietly destroys it. This past March, I co-presented original research at the SITE International Conference in Philadelphia with Dr. Trust, evaluating how well large language models actually support learning when measured against established instructional theory. What we found should matter to every STEM educator. Faculty need to stop relying on blanket AI bans, update their syllabus policies, and start teaching students how to use AI for metacognitive reflection and cognitive collaboration — because whether faculty act or not, students are already using these tools every day.

The Learning Illusion

Akgun and Toker published a 2025 empirical study comparing students using ChatGPT against students using traditional textbooks. The AI group showed short-term gains on simpler tasks, but their long-term retention was significantly worse. The AI was doing the thinking. The student was watching. In learning science, this is called cognitive offloading, and in physics, it compounds every week. A student who does not genuinely work through Newton’s Second Law in week three will be lost when momentum, energy, and wave mechanics arrive later.

The struggle is not the enemy of learning in physics. The struggle frequently is the learning.

Hon’s 2026 systematic review of studies from 2018 to 2024 confirms that AI tools consistently increased engagement but also produced over-reliance and inconsistent outcomes, with the biggest gaps in disciplines that require deep conceptual reasoning. Physics is exactly that kind of discipline. Yet every day, physics students everywhere open ChatGPT, paste in a problem, and read the solution. It feels productive. It is not.

When AI Actually Works

The picture is not uniformly negative. AI can sometimes teach better than a traditional classroom, but only when it’s designed very carefully. In 2025, Harvard researchers ran an experiment and found that students learned more physics and learned it faster when they used a custom-built AI tutor instead of sitting in a typical active-learning class. What made it work wasn’t the AI itself so much as the guardrails built into it: students had to walk through their thinking before getting any help, mistakes became useful signals rather than dead ends, and the system adjusted based on where each student was actually getting tripped up. Even then, the researchers noted it could have been even better with tighter controls on how quickly answers were revealed. When I tested the model myself, I found it still occasionally provided solutions faster than a student could meaningfully process them.

Kotsis frames this through cognitive load theory: AI must scaffold inquiry rather than replace it. When a student pastes a problem and copies the answer, they eliminate all cognitive load. When they prompt an AI to coach them step by step and require them to show their work first, they engage exactly the cognitive processes physics instruction is designed to build. Younis found measurable improvements in conceptual mastery among undergraduate physics students when AI was integrated this way.

The AI is the same either way. The learning is completely different.

What the Data Actually Shows

At SITE 2026, Dr. Trust and I set out to answer a specific question: do the study and learning modes that major AI companies have built — features these companies developed, by their own account, in partnership with educators and learning scientists — actually deliver a sound learning experience? We tested four platforms: ChatGPT, Gemini, Claude, and Perplexity. Our framework was Gagné’s Nine Events of Instruction, a model from the 1960s that defines the foundational conditions for effective learning, from gaining the learner’s attention and stating objectives through eliciting performance, providing feedback, and supporting transfer to real-world application.

Across all four platforms, two of Gagné’s events were nearly absent: Gain Attention and Inform Objectives. In practice, this meant that no tool consistently explained what the student should know or be able to do after the lesson, and no tool took meaningful steps to engage the student’s curiosity before presenting the content. Without a stated learning objective, a student cannot track their own progress, cannot reflect on whether they actually understood something, and cannot connect the current concept to the next one. In a discipline as cumulative as physics, that is not a minor gap. It is a structural failure.

The findings went deeper than missing events. Learning guidance was the most consistent behavior across all four tools, but the other behaviors followed a repetitive, formulaic pattern rather than adapting as the interaction progressed. Feedback was constantly present but shallow — short and generic, lacking the depth needed to actually support learning. Every tool works with enthusiasm and encouragement regardless of the quality of the student’s responses, making it dangerously easy to fall into a learning illusion: you feel like you understand because the AI keeps telling you that you are doing great. ChatGPT in particular overwhelmed users with multiple questions simultaneously, creating a mismatch between what it asked the learner to do and what its own interface allowed. Of the four tools, Claude was the only one that consistently pushed students toward critical thinking — and, perhaps tellingly, it is often perceived as the most frustrating to use.

There is something else important to say. The presence of a pedagogical behavior in an AI interaction does not guarantee its quality. A tool can ask questions without asking useful questions. Our research required classifying each interaction against Gagné’s events regardless of quality, then reexamining the qualitative texture of those interactions to understand what the numbers alone could not capture. What the data showed, across hundreds of interactions, is that the most sophisticated AI study modes available right now cannot consistently meet what a first-year education textbook from 1965 would call basic instructional standards — and these are the tools students are relying on every night.

The Missing Skill: Metacognitive Prompting

If the tools themselves are not pedagogically reliable, then the burden falls on how students use them. This is where metacognitive prompting becomes essential — and where the gap in instruction is most glaring. Consider two students preparing for the same Physics 181 midterm on the work-energy theorem. The first opens ChatGPT and types: “Teach me about the work-energy theorem for my exam.” The AI produces a tidy summary. The student reads it, feels reassured, and moves on. Cognitive offloading is complete.

The second student writes a different kind of prompt. They instruct the AI to act as a physics professor who will first provide a short conceptual explanation, then present a symbolic problem using only variables — no numbers. The prompt explicitly requires the student to show their full step-by-step reasoning, including a free-body diagram and force decomposition, before the AI reveals any solution. It instructs the AI to analyze the student’s reasoning, identify specific misconceptions, explain why each mistake matters conceptually, and provide metacognitive strategies — reflection prompts like “Which assumption did I make unconsciously?” or checklists for common errors. Only after this exchange does the AI present a worked solution, and it follows up with a new problem adapted to the student’s demonstrated weaknesses.

The AI is identical in both cases. The learning is not. The first student consumed information. The second student built understanding. The difference is not intelligence or motivation. It is whether anyone ever taught the second student that prompting is a skill, that the quality of what you ask determines the quality of what you learn, and that the goal is not to get the answer but to find out where your reasoning breaks. Nobody is teaching this. Not in physics courses, not in orientation, not in any syllabus I have seen.

What Needs to Change

A professor during my first semester dismissed AI with an analogy: “Do you send your computer to do workouts for you?” The analogy is not wrong about personal responsibility. But it assumes students have a proper gym, a qualified trainer, and enough time to use both. Most of us do not. Office hours last an hour. Textbooks do not ask you how you are thinking. AI is available at two in the morning when the exam is tomorrow, and the concept still will not click. For many of us, it is the only resource available long enough to actually help. That does not make it safe. It makes it necessary — and necessity without guidance is how students get hurt.

Three concrete changes could begin to address this, and none of them cost money. First, update syllabus policies. The University of Texas at Austin has published sample AI guidelines that move past blanket bans toward transparent policies treating AI as a citable tool with clear attribution requirements. Any university can adopt and adapt the same framework. Second, name the risk. Tell students explicitly what cognitive offloading is and why speed is not learning. Chen documents practical strategies for avoiding AI-driven learning illusions that could be incorporated into any course’s first-week materials. Third — and this is the intervention that does not exist yet — teach students how to prompt. Not as a computer science skill, but as a metacognitive one. A single module in the first week of a physics course, showing the difference between a prompt that offloads thinking and a prompt that forces reflection, would do more for student learning than any AI ban ever has. Resources for this already exist. EdTech Books publishes open-access materials — many peer-reviewed, others designed by scholars and educators — addressing how to design AI-integrated assignments and teach prompting for critical thinking rather than answer retrieval. One example is AI-Ready Educators and Students: Using the AUGMENT Framework to Teach and Learn with Generative AI, which offers a free, classroom-ready framework for exactly this kind of teaching. These resources exist right now, and most faculty have not seen them.

I want to be honest about the limits of this argument. Prompting is a patch. It is a patch for what is, at its core, a real and serious wound: AI tools built for speed rather than learning, that consume millions of liters of water annually, that encode biases, and that will not on their own produce the physicists this world needs. But we do not have time to wait for better tools, and the wound is already open. We do not have those tools yet. I am not sure we will have them in five years. Students are using these tools today with no guidance on how to use them well.

The question has never been whether students will use AI. The question is whether anyone will teach them the difference between a prompt that replaces their thinking and a prompt that sharpens it. That is a teaching problem, and it has a teaching solution. The goal is not to ban these tools or to endorse them. The goal is to give students the knowledge, the research, and the critical awareness they need to make an informed decision about how they learn — and then the freedom to make it. Right now, students are making that decision every day. They are just making it in the dark. The least any university can do is turn on the lights.

About the author

Patricio Bastida Nava is a Mexican undergraduate student at the University of Massachusetts Amherst, where he is pursuing a double major in Physics and Astronomy/Astrophysics alongside interdisciplinary studies in artificial intelligence and STEM education. His work sits at the intersection of AI research, instructional design, and applied technology. He has co-authored research on how generative AI platforms support teaching and learning, and designs corporate AI training programs grounded in prompt engineering and educational theory. He is also a member of UMass’s iCons program in the AI & Future of Work track. Beyond his academic work, Patricio serves in student technical leadership and is passionate about the role of AI, physics, and pedagogy in shaping the future of work and learning.

About Rachelle

If Your Organization Is Beginning This Work

My sessions focus on helping teams:

• understand what AI can and cannot do

• recognize responsible-use considerations

• build confidence using emerging tools

•align implementation with organizational priorities

Preparing people is what makes AI implementation successful. Contact me via bit.ly/thrivineduPD for my training and speaking services.

Dr. Rachelle Dené Poth is a Spanish and STEAM: What’s Next in Emerging Technology Teacher. Rachelle is also an attorney with a Juris Doctor degree from Duquesne University School of Law and a Master’s in Instructional Technology. Rachelle received her Doctorate in Instructional Technology, with a research focus on AI and Professional Development. In addition to teaching, she is a full-time consultant and works with companies and organizations to provide PD, speaking, and consulting services. Contact Rachelle for your event!

Rachelle is an ISTE-certified educator and community leader who served as president of the ISTE Teacher Education Network. By EdTech Digest, she was named the EdTech Trendsetter of 2024, one of 30 K-12 IT Influencers to follow in 2021, and one of 150 Women Global EdTech Thought Leaders in 2022.

She is the author of ten books, including ‘What The Tech? An Educator’s Guide to AI, AR/VR, the Metaverse and More” and ‘How To Teach AI’. In addition, other books include, “In Other Words: Quotes That Push Our Thinking,” “Unconventional Ways to Thrive in EDU,” “The Future is Now: Looking Back to Move Ahead,” “Chart A New Course: A Guide to Teaching Essential Skills for Tomorrow’s World, “True Story: Lessons That One Kid Taught Us,” “Things I Wish […] Knew” and her newest “How To Teach AI” is available from ISTE or on Amazon.

Leading Forward, Part V: How Do We Know It’s Working? Measuring What Matters in an AI-Driven World

June 2, 2026June 1, 2026Dr. Rachelle Dené Poth 2 Comments

Throughout this series, I’ve shared what I’ve learned from working alongside district leadership teams across the country as they navigate artificial intelligence, digital wellness, and purposeful technology use.

We’ve explored:

why curiosity is replacing fear
why educator readiness is the foundation
why leadership and systems matter

But there is another critical question schools must answer:

How do we know if it’s working? Because implementation is not the goal. Impact is.

The Problem With Measuring the Wrong Things

Screen time and effective use of technology are hot topics in conversations happening in schools across the country. In many districts, success with technology has typically been measured by:

number of devices available, so all students can participate in learning
tool adoption rates
platform usage
logins and activity

These metrics are easy to track. But they don’t tell the full story. I’ve said it many times in various ways, but a classroom full of students using devices does not automatically mean that impactful, meaningful learning is happening. Nor does it show true student engagement just by the use of devices.

More technology use does not automatically lead to deeper learning.

More screen time does not equal greater engagement or better outcomes

So we have to really think about what we are measuring. If we continue measuring what is easy, we risk missing what matters most. And we might miss providing the best learning experiences for students.

What Should We Be Measuring Instead?

Across the districts I work with, the leadership teams are beginning to shift their focus.

The districts making the most progress are beginning to ask different kinds of questions:

Are students thinking more deeply?
Are students asking better questions?
Are students able to evaluate information more critically?
Do students understand when and how to use AI responsibly?
Are students being guided in how to use technology and why they are using it?
Do educators feel confident in their instructional decisions?
Are they supported as technology changes?

These are harder to measure, but they are far more meaningful and provide greater insight that schools can act upon.

Indicators Schools Are Moving in the Right Direction

There are some clear indicators I have seen and read about that show schools are moving in the right direction.

1. Student Thinking Is Visible

Students are not simply submitting AI-generated responses. They are explaining their thinking, reflecting on their process, questioning outputs, and making revisions. They are being guided and understand how to use AI as support, not a replacement.

2. Educators Are Making Intentional Decisions

Teachers are not asking whether they can use a certain tool or platform. Instead, they are questioning when they should and what the impact will be. This shift shows greater confidence in the purposeful use of technology and in intentional lesson design. Quality over quantity.

Continue reading the rest and subscribe to my newsletter on LinkedIn.

Subscribe to my ThriveinEDU newsletter to stay informed.

If Your Organization Is Beginning This Work

I help schools and other organizations (law firms, healthcare professionals, business owners, psychologists) implement AI responsibly through policy guidance, professional learning, and classroom-ready strategies grounded in both instructional practice and legal insight.

My sessions focus on helping teams:

• understand what AI can and cannot do

• recognize responsible-use considerations

• build confidence using emerging tools

•align implementation with organizational priorities

Preparing people is what makes AI implementation successful.

Contact me to work with you or speak at your event. bit.ly/thriveineduPD See testimonials about my work via my website.

Leading Forward in AI: From AI Conversations to Sustainable Systems (Part IV)

May 28, 2026May 25, 2026Dr. Rachelle Dené Poth Leave a comment

In my last article, I shared what I’ve been learning from working with district leadership teams across the country as they navigate questions about artificial intelligence, digital wellness, and purposeful technology use. That work has provided me with insightful information and meaningful opportunities to learn from educators, students, and families.

Throughout these conversations, one message continues to stand out:

We cannot begin, and we cannot stay focused only on the tools and the tech.

We must move forward.

The Shift Schools Must Make Now

In Part II, I emphasized that educator readiness is the foundation of successful AI implementation. Schools that prioritize supporting educators are the ones seeing the most progress. And it starts with leadership and consistency. But readiness alone is not enough.

What I have learned from working with school Task Forces across the country is that they have had many conversations around AI, screen time, and tech use. They have explored the possibilities and understand the urgency with these topics, but they also have a similar question.

What do we do now? And this is where I believe that leadership matters most.

Moving From Conversations to Systems

Across the districts I continue to work with, I see a clear difference between schools that are talking about AI and schools that are leading with AI. I also see a difference between AI in education and AI Education. I recently met with a State Representative in Pennsylvania, and we had this conversation as well. The difference I’ve noticed and that we discussed is not just access to tools. It is about the presence of a system. Schools making meaningful progress are not relying on isolated efforts when they find time. Instead, they are building structures with a lens on consistency, clarity, and sustainability.

The system they are developing is focused on having:

clear expectations for the responsible use of all technology
consistent messaging across classrooms and grade levels
ongoing professional learning opportunities with follow-up support
shared language for students, staff, and families
opportunities for student voice and feedback

When these are part of the conversation, AI implementation becomes less about individual decisions, which leads to inconsistency, and becomes more about a goal for collaborative and collective progress.

Consistency Builds Confidence

One of the most common challenges I have been hearing from both educators and students is inconsistency. I’ve met with student groups, interviewed educators, spoken with parents, and heard similar comments from educators and parents across the country.

In one classroom, the use of technology, and specifically AI, is encouraged. In another, it is restricted. In one classroom or school, the expectations are clear and known to all. In another, they are undefined or inconsistent.

Continue reading on LinkedIn and subscribe to my newsletter there as well.

Preparing people is what makes AI implementation successful.

Contact me to work with you or speak at your event. bit.ly/thriveineduPD See testimonials about my work via my website.

About Rachelle

Leading Forward Part II

May 12, 2026May 2, 2026Dr. Rachelle Dené Poth Leave a comment

Preparing Educators for an AI Future Means Preparing Leaders First

In my last article, I shared my thoughts about what I’ve been learning from working with district leadership teams across the country as they navigate questions about artificial intelligence, digital wellness, and purposeful technology use. My work has provided me with tremendous opportunities to learn from educators, students, and families.

Conversations about screen time, purposeful technology use, and digital balance are happening everywhere. What I’ve found most insightful is when students and educators have the chance to sit down and engage in open, honest conversations about these topics and learn from one another. I’ve noticed a common theme in most of these conversations. We have to focus on more than just the technology, especially when talking about AI use in schools. Frequently, the focus is first on specific tools. When talking about artificial intelligence happening in schools, the questions have been:

Which platform should we allow? What should students be permitted to use? What policies do we need?

These are important questions. But they are not the first questions schools should be asking.

The first question schools should be asking is:

How prepared are our educators to lead in an AI-shaped learning environment?

Successful implementation is not about technology adoption.

Introducing AI into classrooms is easy. Supporting educators to understand how to use it meaningfully is the real work. And with support comes confidence.

Educator readiness is the real implementation strategy

Across the districts I have worked with, I’ve noticed that the biggest predictor of successful AI integration is not the access to tools, but whether or not educators feel supported as they navigate the changes happening.

I believe that schools will see more progress and success when there are goals set. Educators must have time to explore. Expectations need to be communicated clearly and with a consistent message. Policies must be in place, and they should emphasize guidance rather than restriction. AI implementation and any technology integration succeed when educators understand not only how to use tools, but why they should use them, and what the impact is on student learning. This is what I am hearing from students around the country.

Across classrooms nationwide, students are using an increasing number of digital tools in their classes. However, I am hearing from them that they are not always consistently guided on how to use them safely, ethically, and responsibly. Students wanting clarity is a powerful insight. Students wanting more purposeful use of technology is an even more powerful insight. How can this happen?

By supporting educators, because it helps to then support students.

Leadership sets the tone

One of the most powerful influences on AI adoption, technology use, and the establishment of standards for communication and screen time in a school system is leadership modeling.

When administrators ask for feedback, communicate transparently, dive in to explore tools with teachers, and acknowledge uncertainty while providing direction, they create a safe environment for innovation. Leadership like this builds trust, and trust makes responsible implementation possible.

Preparing students means preparing adults first

Students will graduate and enter workplaces shaped by automation, intelligent systems, and evolving expectations around collaboration with technology. According to the World Economic Forum, technological literacy is #3 for 2030. #1 is AI and #2 is cybersecurity. Students are not the only ones preparing for that future. Educators need to be prepared so that our students are too.

Professional learning on AI is no longer an option. It is an essential part of instructional readiness. The schools making the most progress right now are engaging in conversations to build systems that help educators adapt confidently as change continues. And that may be the most important preparation strategy of all.

Supporting educators means strengthening entire school systems. This is one of the most important investments districts can make as they prepare students for an AI-shaped, AI-driven future.

Stay tuned for part 3 of this Leading Forward Series.

Subscribe to my ThriveinEDU newsletter to stay informed.

If Your Organization Is Beginning This Work

My sessions focus on helping teams:

• understand what AI can and cannot do

• recognize responsible-use considerations

• build confidence using emerging tools

•align implementation with organizational priorities

Preparing people is what makes AI implementation successful.

About Rachelle

Contact Rachelle to schedule sessions about Artificial Intelligence, AI and the Law, Coding, AR/VR, and more for your school or event! Submit the Contact Form.

Follow Rachelle on Bluesky, Instagram, and X at @Rdene915

Leading Forward in AI: What I’ve Been Learning from Schools Across the Country (Part I)

May 5, 2026May 19, 2026Dr. Rachelle Dené Poth Leave a comment

Subscribe to my ThriveinEDU newsletter to stay informed. (If you receive my newsletter, you may have read this, but just in case…here is part I)

Over the past eight months, I’ve had the opportunity to work with educators, school leaders, and district teams from twelve districts across the country as they navigate one of the biggest shifts education has experienced in decades: the arrival of artificial intelligence in everyday teaching and learning. This work is part of a national digital wellness and innovation initiative supporting districts as they develop responsible approaches to emerging technologies.

I work with a Task Force from each district to evaluate policies, create resources for families, and decide when and how to begin teaching students about AI, as well as how best to support educators. And some of these Task Forces include students and parents. We have had many conversations about digital wellness, digital citizenship, screentime, and, of course, AI.

The conversations about AI included shared concerns, questions, and challenges. However, what has stood out the most in these conversations with these schools is not fear. It’s curiosity.

In classrooms, teachers are asking thoughtful questions about how AI can support student thinking rather than replace it. Administrators are working to align emerging tools with existing priorities such as digital citizenship, academic integrity, and student wellness. District teams are exploring how policy can move beyond restriction toward responsible guidance. Some are even completely rewriting their policies to align with these changes and make sure that a common language is used.

Recently, my work has included:

• Supporting district digital wellness and AI implementation planning

• Leading professional learning sessions on responsible AI use

• Presenting on AI and the law for educators

• Visiting classrooms to observe how students are already interacting with AI tools

• Collaborating with leadership teams and developing next-step strategies for staff support

• Designing activities for administrators and educators to evaluate policies and effective AI use

One consistent theme continues to emerge:

Districts, educators, and students are ready to lead.

Educators are not waiting for perfect answers to the big AI questions. They are considering the best pedagogical practices for using AI that protect students while expanding opportunities.

The most successful districts I’m working with right now are focusing on three priorities:

Supporting educator confidence: They need clarity, examples, and time to explore.
Creating shared expectations for responsible use across classrooms and grade levels
Preparing students to think critically about AI-generated information.

Artificial intelligence isn’t just a technology conversation.

It’s a leadership conversation.

And I’m excited to continue working with and learning alongside school districts as they move forward with clarity, purpose, and a strong commitment to keeping human relationships at the center of innovation.

Providing the training

Artificial intelligence is changing expectations across nearly every profession. Schools are not the only organizations preparing for this shift.

In my work as an educator, attorney, and national presenter on responsible AI implementation, I support organizations as they explore how AI connects to decision-making, ethics, communication, and everyday professional practice.

My sessions focus on helping teams:

• understand what AI can and cannot do

• recognize responsible-use considerations

• build confidence using emerging tools

•align implementation with organizational priorities

Preparing people is what makes AI implementation successful.

About Rachelle

Contact Rachelle to schedule sessions about Artificial Intelligence, AI and the Law, Coding, AR/VR, and more for your school or event! Submit the Contact Form.

Follow Rachelle on Bluesky, Instagram, and X at @Rdene915

Educator explaining AI literacy concepts on a smartboard to students with tablets and laptops

AI Literacy is Not Tool Mastery: How to Build Sustained Educator Capacity

April 28, 2026May 2, 2026Dr. Rachelle Dené Poth Leave a comment

Previous post on Getting Smart

Not long ago, artificial intelligence in education felt novel. It was something shiny, experimental, and, for many educators, possibly unsettling at times. When ChatGPT arrived in November 2022, the initial conversations and concerns were more focused on fear. I recall receiving emails, text messages, phone calls, and visits from educators who were concerned about cheating, plagiarism, lost skills, and what instantly felt like an overwhelming pace of change. It was something else to adjust to, not long after the overwhelming feeling that many felt in March of 2020.

But since that initial adjustment to the increased use of AI in our world at the end of 2022 and through 2023, I’ve seen a shift happening. At first, there was skepticism, uncertainty, and hesitation, and not just in the world of education. However, as we’ve continued to adjust to new tools and new ways of working, I’ve noticed a shift from considering AI as a “what if” to the acceptance that AI is here and its use is increasing. It’s embedded in tools educators already use, and if it hasn’t already, then it will potentially slowly but surely become part of the daily routine and workflow of teaching and learning.

I’ve spoken about this shift from novelty to normalcy and how it brings a new challenge: educator upskilling.

A few years ago, I started researching the training available to educators and other professionals in AI. At the end of 2023, 87% of the educators in the United States had not received any training. In my workshops, some attendees are having their first training experience, more than 3 years after ChatGPT made its debut. So I think that we need to focus on an important question, whether in education or not. The question is no longer whether educators need professional learning around AI. Most people agree that they do. The bigger issue is whether we are approaching AI professional development in ways that are deep, sustained, and human-centered, or whether we’re still experiencing the one-and-done sessions that barely scratch the surface. With AI and the pace of change in education and the world, we need to do better and be prepared.

Shifting to Ongoing Capacity Building

When I completed my doctorate nearly two years ago, my research focused heavily on professional learning in emerging technologies, with a strong emphasis on AI. Even then, the message was clear. A single PD session, or even a series of short, tool-based trainings, was not enough, especially if completed early in the year or during a limited time span.

Yet, that is what I am learning about how AI PD is structured today. Through surveys in my sessions and conversations with other educators, there is a common experience happening, which is:

A 30-minute overview.
A 15-minute “certified educator” badge.
A walkthrough of one tool done well.

While these experiences can be helpful, especially for getting started and when time is limited, in the long term, they don’t build AI literacy. They build familiarity, whether with AI concepts or an AI tool. But familiarity is not AI literacy. Not for us as educators, nor for the students we are preparing for a future surrounded by AI and a world of work that seeks employees skilled in AI.

Continue reading the original post on Getting Smart.

About Rachelle

Contact Rachelle to schedule sessions about Artificial Intelligence, AI and the Law, Coding, AR/VR, and more for your school or event! Submit the Contact Form.

Follow Rachelle on Bluesky, Instagram, and X at @Rdene915