Murder, Mystery, and Machine Minds: AI-Powered “Whodunits” for the Classroom

September 8, 2026August 25, 2026Dr. Rachelle Dené Poth Leave a comment

Guest post by: Renee Dawson | Middle School Special Education Teacher | Atlanta, GA @dawsonedtech | #AIinEDU #whodunit #middleschoolteachers

What if your next standards-based lesson felt more like a crime scene investigation than a worksheet, and your students were actually excited to complete the activity for the lesson? With AI chatbots and generative image tools, you can transform traditional content and learning activities into immersive “whodunit” experiences that hook middle and high school students while reinforcing critical skills.

At its core, a classroom whodunit is a structured problem-solving activity. Students analyze clues, apply content knowledge, and eliminate possibilities to determine the culprit, location, and weapon. The twist? AI can now help you generate the entire experience, completely aligned to the current state standard you’re teaching, in minutes.

Start with your standard, not the story. Choose a specific learning objective, such as: identifying types of symbiotic relationships, solving integer operations, or analyzing textual evidence. Then prompt your favorite AI chatbot to build a mystery scenario that embeds those concepts into clues. For example, if your standard focuses on integer operations, each clue might require students to correctly solve a problem to eliminate a suspect. A correct answer could reveal: “The suspect was seen at a temperature of -5°C, but your solution shows the crime occurred at +3°C. This suspect is not the murderer.”

The power of this approach lies in how clues are structured. Each question should act as a gatekeeper, allowing students to eliminate one of three categories: suspect, location, or weapon.

AI chatbots are great for generating these layered clue systems. You can ask for 8–12 clues that vary in difficulty, include misconceptions, or scaffold learning. For differentiation, you might prompt the AI to create leveled versions of the same mystery.

For example, in a science classroom studying ecosystems, a clue might read: “The crime scene shows evidence of a parasitic relationship. Which suspect studies organisms where one benefits and the other is harmed?” Students must apply vocabulary knowledge to eliminate suspects who specialize in mutualism or commensalism. Each correct interpretation moves them closer to solving the case.

Once your narrative and clues are generated, generative AI tools can bring the mystery to life visually. You can create realistic or stylized images of:

Suspects (for example, “a nervous-looking botanist in a greenhouse”)
Locations (for example, “a dimly lit school laboratory with overturned beakers”)
Weapons or objects (for example, “a cracked test tube labeled with a chemical formula”)

These visuals dramatically increase engagement, especially for visual learners and students who benefit from contextual cues. They also make your activity feel more like a game or digital escape room rather than a traditional assignment.

To streamline your workflow, you can pair tools strategically. Use a chatbot to generate the storyline, character descriptions, and clue questions. Then copy those descriptions into an image generator to create matching visuals. Finally, organize everything into a platform you already use to create a slide deck, digital form, or even a digital escape room, where students can interact with clues, track eliminations, and submit their final answers. I enjoy creating an introduction video that students either watch in their group or I play on my interactive board to help set the scene and get them excited for the activity. To get them moving and release some of their energy, consider hiding the clues around your classroom or throughout a hallway to add a scavenger hunt aspect to the activity.

Another advantage of AI-generated whodunits is how easily they support iteration. You can quickly revise a clue for clarity, swap out a standard, or regenerate an entire mystery with a different theme (school, space station, historical setting) while keeping the same academic focus. This flexibility is especially valuable for reteaching, enrichment, or standardized test preparation.

Of course, the teacher’s role remains essential. AI can generate content, but you ensure alignment, accuracy, and appropriateness for your students. Review each clue for misconceptions, adjust language for readability, and consider adding discussion or reflection components. For instance, after solving the mystery, students might explain how specific evidence helped them eliminate each suspect, reinforcing both content knowledge and reasoning skills. Another component I often add at the end of my science whodunits is to have the students complete a CER (Claim-Evidence-Reasoning) activity on the learning objective or standard using information from the clues in the activity.

When you use AI tools to your advantage, you can create whodunits that turn learning into an experience. Students aren’t just answering questions, they’re investigating, debating, and thinking critically. Plus, as they work to crack the case, they’re also mastering the standard you set out to teach.

AI-Powered Whodunit Teacher Quick Guide

Step 1: Start with Your Standard. Choose a specific learning target. One skill per mystery works best.

Step 2: Prompt the AI for a Mystery Scenario. Ask the chatbot to generate:

A short backstory (crime + setting)
3–5 suspects, locations, and/or tools
8–10 clues tied directly to your standard

Step 3: Design Elimination-Based Questions Ensure each clue leads students to eliminate:

One suspect, OR
One location, OR
One key object

Step 4: Generate Visuals (Optional but Powerful) Use an image generator to create:

Suspects (character portraits)
Locations (crime scenes)
Key objects (weapons/tools)

Step 5: Build the Activity Organize in:

Google Slides (interactive clues)
Google Forms (self-checking)
Escape room format (locks + codes)

Step 6: Add a Reflection Have students explain:

How they eliminated options
Which clues were most important

Sample AI Prompts:

Science: “Create a whodunit mystery for 7th-grade students where they must identify types of symbiotic relationships (mutualism, commensalism, parasitism) to solve the case. Include 4 suspects, each with a scientific specialty, and 8 clues. Each clue should require students to correctly identify a relationship type in order to eliminate one suspect, location, or object. Include clear answers and explanations.”

Social Studies: “Create a whodunit mystery for 8th-grade students set in a historical context where students must use geographic clues (landforms, climate, or region characteristics) to determine where an event took place. Include 4 locations and 8 clues. Each clue should require students to apply geographic reasoning to eliminate one incorrect location or suspect. Align to the following 8th-grade social studies standard listed below.”

Math: “Create a classroom whodunit mystery for 6th-grade students where solving integer problems helps students identify the culprit. Include 4 suspects and 8 clues. Each clue should involve integer operations (addition, subtraction, multiplication, or absolute value). A correct solution should allow students to eliminate one suspect, location, or weapon. Include an answer key and step-by-step solutions.”

Language Arts: “Create a whodunit mystery for 6th-grade students where students must analyze short passages and identify themes or use textual evidence to solve the case. Include 4 suspects and 8 clues. Each clue should include a short paragraph on a 3rd-4th grade reading level and a question requiring students to infer themes or cite evidence, allowing them to eliminate one suspect, location, or object. Include sample answers and reasoning.”

Sample Introduction Video to one of my whodunit activities for 6th grade math:

AI-generated images from some of my classroom whodunits:

Images generated using Microsoft Image Generator

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!

The Elective Course Effect: How Non-Major Requirements Can Shape STEM Identity

August 31, 2026August 25, 2026Dr. Rachelle Dené Poth Leave a comment

Guest post by Tessa Dodson. Opinions expressed are those of the writer.

Walk through almost any school, and you’ll hear similar suggestions that students interested in STEM should take as many math, science, and technology courses as possible. While this advice is well-intentioned, it overlooks an important truth.

Students don’t develop a strong STEM identity solely by taking more STEM classes. They develop it by becoming confident thinkers, effective communicators, creative problem-solvers, and lifelong learners. Many of those qualities are cultivated through nonmajor requirements, especially courses in languages, social sciences, and humanities.

Why Electives Matter More Than You Think

When students enroll in electives, they often encounter learning experiences that differ from their core STEM coursework. Language classes strengthen communication and cultural understanding, and arts courses encourage creativity, observation, and design. History and philosophy ask students to evaluate evidence, construct arguments, and understand context.

Meaningful elective experiences often carry over into STEM learning. Instead of treating the arts and STEM as competing priorities, schools that use interdisciplinary learning can help students develop broader and more holistic skills.

Think of a student working on an engineering design project. Technical calculations are important, but so are presenting ideas to classmates, working through disagreements within a team and adapting solutions based on feedback. These are precisely the types of skills students develop through thoughtfully designed courses.

1. Building Better Problem Solvers

Many STEM challenges have no single correct answer. Scientists evaluate competing evidence, and engineers balance trade-offs. Medical researchers consider ethical implications alongside scientific data. Modern workplaces require individuals who can analyze information and make informed decisions, and 88% of employers look for candidates with evidence of problem-solving on their resume.

Students become stronger problem solvers when they learn to approach complex questions from multiple perspectives. Humanities courses frequently ask students to interpret ambiguous information, defend positions with evidence, and recognize nuance. Arts courses encourage experimentation, revision, and innovation. Language learning develops adaptability and pattern recognition while requiring students to navigate unfamiliar situations. Many colleges prefer that incoming students have at least two years of study in a world language.

Problem-solving habits and skills transfer naturally into STEM disciplines. When schools intentionally preserve nonmajor requirements, they create more opportunities for students to practice flexible thinking before they encounter technical challenges.

2. Improving Communication Skills

One misconception about STEM careers is that success depends primarily on technical expertise. In reality, today’s scientists, engineers, and technology professionals spend considerable time communicating. They explain findings to clients, present proposals to leadership teams, collaborate across departments, mentor colleagues, and translate technical concepts for nontechnical audiences.

However, students rarely develop these skills through technical coursework alone. Humanities courses can strengthen organization and evidence-based reasoning. Foreign language courses improve listening, perspective-taking, and communication. These professional capabilities complement technical knowledge and make it more impactful. According to 37 executives from the chemical and pharmaceutical industries, collaboration and communication are among the most valuable nontechnical skills for scientists.

3. Boosting Creativity That Drives Innovation

Innovation rarely comes from repeating established or old practices. Instead, breakthrough ideas emerge when individuals connect concepts across different disciplines. Some of history’s most influential innovators drew inspiration from art, literature, philosophy, or music alongside scientific inquiry.

Today’s technology companies value employees who combine analytical thinking with creativity. Arts education plays an important role in promoting this mindset. Whether students are composing music, designing visual media, or performing theater, they learn to receive feedback, experiment with new ideas, and embrace revision as part of the creative process. Those same habits support engineering design cycles, software development, scientific experimentation, and product innovation.

Personal Growth Extends Beyond Career Preparation

Education serves a purpose that is beyond preparing students for a particular profession. It also helps young people develop into curious, empathetic, and resilient individuals who can adapt to changing circumstances throughout their lives.

Literature courses invite students to consider perspectives different from their own. Philosophy asks students to consider ethical questions that rarely have simple answers. The arts foster self-expression, perseverance, and confidence through creative exploration.

By providing these experiences, you can contribute to students’ personal growth, helping them become more reflective, emotionally aware, and open to new ideas. They also strengthen qualities like resilience, adaptability, and intellectual curiosity, which are just as valuable in a STEM career as technical expertise.

By providing opportunities for students to explore interests beyond their primary field of study, you reinforce the idea that education is about growing into capable, well-rounded people, not simply preparing to become future employees.

More Students Will See Themselves in STEM

Some students discover their interest in STEM only after connecting technical concepts with their personal passions. For example, a student who enjoys storytelling may become interested in data visualization. A musician may discover acoustical engineering, or a visual artist may pursue user experience design.

When you encourage students to explore subjects beyond their intended major, you create opportunities for students to combine their passions rather than choose between them. As students begin to see how different subjects intersect, they are more likely to view STEM as a field where their unique talents and perspectives are valued.

This broader, more inclusive approach is especially important for students who may not identify with traditional STEM careers. By demonstrating that there are many ways to contribute to STEM, you can help more students develop the capabilities and confidence that are essential to building a strong STEM identity.

What Educational Leaders Can Do

Consider how your school’s policies support interdisciplinary learning. Start by examining whether scheduling practices unintentionally discourage students from taking electives outside their intended majors. When every period is devoted to technical coursework, students may lose valuable opportunities for broader, well-rounded development.

Next, you can encourage collaboration across departments. Invite STEM and humanities teachers to co-design interdisciplinary projects that address authentic problems. Students might combine environmental science with persuasive writing, computer science with graphic design, or engineering with ethics.

You can also celebrate diverse pathways into STEM. Highlight alums and professionals whose careers show the importance of interdisciplinary experiences. Share examples of computer scientists who value philosophy or healthcare professionals whose language skills improve patient care. These stories can help students understand that success in STEM does not require limiting themselves to technical coursework.

Rethinking Success

The pressure to optimize courses for college admissions or career readiness can unintentionally limit students’ educational experiences. Valuable learning also takes place in classes outside a student’s major.

When schools value nonmajor requirements, they focus on developing adaptable thinkers rather than just technically skilled graduates. This broad approach helps students communicate effectively, think creatively, collaborate well, and become resilient learners. They gain confidence in academic mastery and in their ability to address real-world problems.

About Tessa

Tessa Dodson is the Senior Writer of Classrooms.com, where she researches and covers educational policy, professional development, teacher support systems, and integration challenges that K-12 and higher education institutions may face. She aims to research and provide actionable insights for students, educators, school administrators, and other leaders.

About Rachelle’s 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.

About Rachelle

AI in Education vs. AI Education, Part V

August 26, 2026August 25, 2026Dr. Rachelle Dené Poth Leave a comment

Rethinking Learning and Assessment in the AI Era

Throughout this series, I have focused on the distinction between AI in education and AI education, the importance of moving from policy to practice, and the steps schools can take to build more intentional and responsible systems around AI.

But another question continues to come up in conversations with educators:

What happens to teaching and assessment when students have access to AI?

AI can brainstorm, summarize, explain, revise writing, solve problems, generate examples, create images, and produce polished responses in seconds. If it can complete part of an assignment, or sometimes most of it, we need to ask a more important question:

What are we actually assessing?

The focus should not simply be on whether students can produce a finished product. It should be on whether they understand, can explain, apply, evaluate, and think independently. Throughout this series, I have focused on the distinction between AI in education and AI education, but as I started winding down the short series, I recognized another question that continues to come up in conversations with educators:

What happens to teaching and assessment when students have access to AI?

I have heard it frequently, and it is a question that deserves attention because AI is powerful. It can brainstorm, summarize, explain, generate examples, revise writing, solve problems, create images, and produce responses within seconds.

So my thoughts are that if AI is able to complete part of an assignment, or potentially most of it, as educators, we need to really consider what it is that we are assessing. The answer should not be whether a student can produce a finished product (focus on process over product), but whether they understand, can explain, can apply, can evaluate, and can think on their own.

Start With the Learning Goal

Before deciding whether students should use AI, educators need to identify the instructional purpose of the learning experience.

What should students know?
What should they be able to do?
What thinking should they demonstrate?
What skills are we trying to develop?

Once those questions are clear, it is easier to decide what role, if any, AI should play in our instruction.

If the purpose of an assignment is to assess a student’s ability to organize and communicate their own ideas, then using AI to generate the response may interfere with the learning goal. If the purpose is to evaluate sources, revise weak arguments, compare perspectives, or improve a draft, AI may have an appropriate supporting role.

Instead of asking only, Can students use AI? I think a better question is:

What role should AI play in this particular learning experience?

Assess the Thinking, Not Just the Product

For many years, I was focused on the tools and final products to evidence learning. Whether it was an essay, a presentation, a project, or a worksheet. But now that we are in the AI era, we have to reconsider those final products. A final product does not always provide enough insight into what a student actually understands. We know that students may create great representations of their learning via final products, which they did without the use of AI, however, we may want more evidence of their learning along the way. Those products still matter, but in an AI era, we may need more evidence of the thinking behind them.

Some examples may be to ask students to provide:

drafts
annotations
reflection questions
process journals
checkpoints
revisions
explanations of decisions
demonstrations of how they reached their conclusions

These are not new strategies, but they are increasingly becoming more important, especially with AI. If the purpose of an assignment is student thinking, then the learning experience needs to make that thinking visible.

Decide What Role AI Should Play

One of the most important instructional decisions educators can make is determining the appropriate role of AI in an assignment.

In Part III, I shared an AI-use continuum that schools and educators can use as a starting point.

AI use may be:

not permitted
permitted with educator approval
permitted with disclosure
encouraged for a defined purpose
intentionally embedded in the assignment

I believe that the same continuum becomes especially useful when we think about assessment.

For example, AI might not be appropriate during an assessment designed to measure independent writing skills. We want students to develop student agency and be independent thinkers.

But AI could be intentionally included in an assignment that tasks students with critiquing an AI-generated response, identifying errors, comparing perspectives, or improving a weak argument. Even taking five minutes to have ChatGPT or any other LLM generate inaccuracies around a concept and then tasking students with critically evaluating it will make an impact on their learning.

Students might use AI to brainstorm ideas, but are still expected to create and defend the final product themselves. Being able to demonstrate learning, make thinking visible, and grasp the concepts and content they need to be successful, are essential.

Another idea is for students to partner with AI to receive feedback, rather than answers, and then explain which suggestions they accepted, which they rejected, and why. An activity like this really helps students to build those critical thinking and digital discernment skills. We always want to connect back to the learning goal and align with our instructional purpose.

Build in Evidence of Student Thinking

One of the best ways to preserve student ownership is to create opportunities for students to explain their thinking. It does not need to take a lot of time. It is possible through simple questions and activities. Some examples are to ask students:

Why did you make this choice?
How did you arrive at this conclusion?
What evidence supports your answer?
What did you change during the process?
What did AI contribute? What did you contribute?
What did you disagree with?
What did you verify? How?
Are you able to explain this without the use of AI?

These are just some of the guiding questions that can promote visible thinking and help students become more aware of their own learning process. Metacognition comes into play here.

For example, instead of asking students only to submit a final essay, educators might ask them to submit an outline, a short draft, be transparent about where they used AI, explain a revision they made, and briefly defend their final argument.

Here are some content area examples:

Science: Students might compare an AI-generated explanation with a verified source and break down and discuss where the response is incomplete or inaccurate.

Math: Students could analyze an AI-generated solution to a problem and explain whether the reasoning is correct.

Social Studies: Students could compare how AI explains the same historical issue from varying perspectives.

World Languages: Students might evaluate an AI translation and explain where meaning, tone, or cultural context changes and where there might be misunderstandings. I have done this with my own students.

In learning, the final product still matters, but so does the process that produced it.

Productive Struggle

AI can make many tasks faster, but faster does not always mean better for learning. An additional concern is that because AI can produce answers so quickly, students will not experience productive struggle. Students need opportunities to think through difficult problems, experience uncertainty, make mistakes, revise, and discover that their first idea is not always their best one.

If AI removes every challenge, it may also remove part of the learning.

I have explained to my own students about the importance of productive struggle. When they question me, I tell them that they need time to think through a difficult problem, to experience uncertainty, to make mistakes, to revise, and sometimes realize that the first idea is not always the best one.

If we rely on AI too much, it takes away the challenge and may also take away some of the opportunities for learning. And for this reason, completing things quickly and efficiently should not always be the goal in education. I’ve tried to convey that sometimes the process matters more than the speed. Sometimes finding the answer when working alone builds confidence. Sometimes struggling with a sentence or solving a math problem helps students develop the skills of resilience, collaboration, and student agency.

I consider this in my work and ask educators to consider which struggles are unnecessary barriers for students and which are essential parts of learning. We know that AI can help remove barriers, but pushing it further, it should not remove every challenge for students.

From AI Efficiency to AI Dependency

One of the biggest concerns I see developing is not simply whether students are using AI, but whether they are becoming overly reliant on it.

There is a difference. Think about the difference between using AI efficiently and relying on AI to do the thinking. Efficiency means selecting and using a tool or even a strategy to support a learning task while still maintaining ownership, judgment, and understanding. But on the other side, overreliance or dependency happens when students become unable or unwilling to complete the task without the tool.

Students need our guidance. In my work this past year with schools across the country, this is what I have heard from them. They want us to help them develop awareness and develop a checklist that they can work through to understand when they should and shouldn’t use AI. Some questions they can consider are:

Could I do this without AI?
Did AI help me think, or did it do the thinking for me?
Am I able to explain this in my own words?
Did I verify the information? How can I?
What decisions did I make? (not the AI)
What did I actually learn, and can I explain it to another person?
Would I still understand this if the tool were unavailable?

Questions like this will help students build more than AI literacy skills. They will build self-awareness, which is important because we know that responsible AI use is not just about knowing how to use a tool, but it is also about knowing when to use it, how much to rely on it, and when to step away from it.

Assessment is Evolving

Concerns came right away. “The kids are going to cheat.” “It’s the end of assessments as we know them.” But AI does not mean we need to throw assessments aside. I believe that it means we have to be very clear about what we are assessing. Sometimes students ask simple questions. For example, as a Spanish teacher, can I give them the English word and have them match it with the Spanish word? But with AI, I have been thinking through this more. Here are my thoughts:

If we are assessing recall, there may be times when AI should not be available.

If we are assessing reasoning, students may need to explain their process.

If we are assessing creativity, students may need to show how their ideas developed.

For research skills, students may need to verify sources and defend why they trust them.

There are a lot of things to consider in our work as educators when it comes to AI. If AI is intentionally part of the learning experience, then evaluating how students use AI may itself have to become part of the assessment. I present these questions in sessions that I do and when talking with student focus groups. I ask them:

Did you question the output?
Do you recognize limitations?
Can you identify bias?
Are you able to verify claims?
Can you make decisions about what to use and what to reject?

Students need to develop these skills because they are important, especially when considering their preparation for a successful future.

Where AI in Education and AI Education Meet

Throughout this series, I have focused on AI in Education and AI Education. Now, in this fifth and final part (I think), is where the distinction between them becomes especially important.

AI in education asks what role AI should play in the learning experience: whether students should use it, when, for what purpose, and under what expectations.

AI education asks whether students understand what AI is doing: whether they can evaluate its output, recognize limitations and bias, verify information, and make responsible decisions about its use.

Assessment brings both together because educators must evaluate not only what students produce, but also the thinking, judgment, and learning that led to it.

Students should not only demonstrate that they can use AI. They should demonstrate that they can think with it, think beyond it, and think without it when needed.

My Final Thoughts

Now that we have so many possibilities available because of AI, it does not make the learning process less important. It hopefully makes it more important and leads us to be more intentional in our instructional planning.

Educators have always been responsible for designing learning experiences that help students develop knowledge, skills, confidence, and independence, all of which will lead to future success.

AI challenges us to be more intentional and design learning experiences that make students’ thinking visible. We need to guide students to recognize the difference between using AI for enhancement versus replacement. And something that I’ve heard more frequently is not trading the efficiency that AI can promote for dependency on AI.

I think we are past whether or not AI belongs in education. It is here. Research and predictions for future work show that it will continue to be in demand. So as educators, we must prepare our students and ourselves to use it ethically and responsibly. We have to stay focused on how we can use it so it does not result in the loss of essential skills like critical thinking, productive struggle, reflection, creativity, and human judgment that make learning truly authentic and meaningful.

How we do this is an important question to consider as we continue moving forward in an AI-shaped world.

Thanks for reading this series and for the feedback.

Please subscribe to my ThriveinEDU newsletter to stay informed. And contact me for more information.

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 or email Rdene915@gmail.com, for my training and speaking services.

AI in Education vs. AI Education, Part III

August 11, 2026August 6, 2026Dr. Rachelle Dené Poth Leave a comment

The First 5 Steps on the Roadmap for Moving Forward

In Part I, I explored the difference between AI in education and AI education and why both are essential if we want to prepare students for a rapidly changing world.

In Part II, I focused on the gap between policy and practice and introduced six areas that schools and districts should address as they move from conversation to implementation:

Establish clear, shared expectations
Invest in educator readiness
Teach AI literacy intentionally
Align AI use with digital wellness
Engage stakeholders
Review, measure, and adjust

Now, we have to consider how schools actually begin this work. In this part of the series, I will share the first five steps of the roadmap moving forward.

There is no single plan that will fit every school or district. Community needs, resources, policies, student populations, and levels of readiness will vary across school systems within states and across the country.

Given the pace at which AI is advancing, schools should not wait until they have every answer to every question asked. They just need a starting point, a shared process, and a willingness to learn and adjust along the way. And to be successful, they need to be involved in thoughtful and consistent conversations.

Having the Right People at the Table

AI implementation should not be the responsibility of one person, department, or leadership team. I have said this many times when presenting at conferences and working with school districts. AI implementation requires multiple perspectives.

In the past year, schools that have formed an AI Task Force and included members from different backgrounds and roles in education have had the most success.

School and district task forces should include:

classroom educators from varying grade levels and content areas
students
families
school and district leaders
curriculum and instructional technology staff
special education and student-support representatives
data privacy, cybersecurity, or legal personnel
community or workforce partners

Having members from each group leads to a more thorough evaluation and a deeper understanding of AI from different perspectives.

Educators understand classroom realities. Students understand how AI is being used. Families can ask questions, express concerns, and support needs.

On the tech side, technology and legal professionals can help address privacy, security, contracts, age requirements, and compliance with relevant laws. School leaders can connect the work to curriculum, professional learning, communication, and long-term planning.

Connecting these voices creates a stronger foundation for decisions and helps prevent policies from being created in isolation. And it does not require a large task force to be impactful. The goal is to make sure the people affected by the decisions have a meaningful role in shaping them.

Here are the steps that I recommend for schools. During my workshops, speaking events, and writing, I try to help build out policies and systems that will have an impact. I also expand on each of these and create interactive experiences to help educators and students, develop skills and become more knowledgeable about AI and responsible use of it.

Step 1: Understand What Is Already Happening

Before schools write new policies or purchase new tools, they need to understand their current status.

Students and educators may already be using AI in a variety of ways, including:

brainstorming
lesson planning
generating questions
summarizing information
creating drafts
translating text
producing images
providing feedback
researching ideas
completing assignments

Some uses may be appropriate and productive, while others may raise concerns about privacy, accuracy, bias, academic integrity, or overreliance. My work with AI and the law addresses these concerns.

Schools cannot create effective expectations without first understanding what is happening. Over the past year, sitting with student focus groups, moderating panels, and visiting classrooms have all helped me to gain additional insights into what schools look like in an AI era.

Schools need to know where confusion exists, what educators need, what students already understand, and where gaps in guidance may be creating risk. The information gathered should guide the next steps.

Step 2: Define Shared Principles

Before writing detailed rules, schools should identify the principles that will guide AI use. These principles provide a common foundation across classrooms and grade levels, even when specific expectations vary. Common and shared language around AI is essential.

Possible shared principles might include:

Human judgment remains essential.
Learning remains the priority.
AI should support thinking rather than replace it.
AI use should be acknowledged when appropriate.
AI-generated content should be evaluated for accuracy, bias, and relevance.
Technology use should align with student well-being and instructional purpose.
Access and expectations should be equitable.

These principles can help educators make decisions when a new tool, classroom scenario, or student question arises. And they are especially important because schools cannot anticipate every possible use of AI. A policy can provide boundaries and shared principles that help people make responsible decisions within those boundaries.

Step 3: Create Clear Expectations for Use

One of the most common challenges educators face is determining when AI use should be allowed. The appropriate role of AI depends on the learning goal, the age of the student, the type of assignment, the information being shared, and the amount of independent thinking required. Schools may benefit from creating an AI-use continuum. For example:

AI Use Is Not Permitted

Students are expected to complete the task independently because the goal is to assess their individual knowledge, thinking, or skill.

AI Use Requires Educator Approval

Students may use AI only after discussing the intended purpose with the educator.

AI Use Is Permitted With Disclosure

Students may use AI for specific parts of the process but must explain how it was used.

AI Use Is Encouraged for a Defined Purpose

AI is intentionally used for brainstorming, comparison, feedback, revision, simulation, or another identified learning goal.

AI Use Is Embedded in the Assignment

Students are expected to interact with AI, evaluate its output, identify limitations, and reflect on its role in their work.

This type of continuum gives educators more flexibility and helps students understand that responsible use depends on context. The expectation should always connect to the purpose of the learning experience.

Step 4: Prepare Educators Through Practical Professional Learning

Educator readiness is one of the most important parts of implementation and it was the focus of my doctoral research two years ago.

Teachers should not be handed a policy and expected to figure out the rest on their own. They need time to explore, ask questions, test tools, and express concerns. Educators need opportunities to examine questions such as:

What is the learning goal?
How might AI support the learning?
How might it interfere with the learning?
What should students be expected to do independently?
What privacy or safety concerns exist?
Would another approach be more effective?

Having conversations and addressing questions like these will help educators build confidence and consistency.

Step 5: Teach AI Literacy Across the Curriculum

AI education should not only be addressed in a computer science class. AI literacy should be developed across subjects and grade levels. Some examples are:

In language arts, students might compare a human-written passage with an AI-generated one and analyze voice, evidence, and credibility.
In social studies, they might examine how bias appears in responses about historical events.
In science, students might verify an AI-generated explanation against trusted scientific sources.
In math classes, students might evaluate whether an AI-generated solution is correct and explain where the reasoning succeeds or fails.
In world language classes, students might compare AI translations and identify where meaning, tone, or cultural context is lost.

With activities like these, we can teach students how to question, evaluate, and make decisions about the use of AI and its accuracy.

These first five steps provide a practical starting point for schools and districts working to move from uncertainty to intentional action. By bringing the right people together, understanding current AI use, defining shared principles, creating clear expectations and common language, preparing educators, and building AI literacy across the curriculum, schools will establish a stronger foundation for responsible implementation. But the work does not end here.

In Part IV, I will continue the roadmap by exploring how schools can connect AI use with digital wellness, communicate clearly with students and families, and create an ongoing process for reviewing, measuring, and adjusting their approach.

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. Contact me via bit.ly/thrivineduPD or email Rdene915@gmail.com, for my training and speaking services.

Learning Together in Immersive Worlds: Exploring Multiplayer in Delightex Edu

August 4, 2026July 24, 2026Dr. Rachelle Dené Poth Leave a comment

In collaboration with Delightex Edu.

I started using Delightex (formerly CoSpaces Edu) many years ago in my STEAM and Spanish classes. I dove into creating projects and finding options and opportunities for students to explore and have fun learning. For more than 9 years, I have used Delightex Edu with my students and have been continually awed by the platform’s enhancements.

Each year, a variety of new features are added to the already robust platform. What I love about it is that the new features spark curiosity, inspire creativity, and, most importantly, give students more ownership of their learning. The loss of these skills is a concern in the rapidly advancing technological landscape in education. Delightex continues to open up more opportunities for educators and students to leverage technology in meaningful ways. Earlier, they added the AI features and AI buddies, and now there is Multiplayer!

With the new Multiplayer activity, students and teachers can enter the same virtual Project and explore, communicate, and solve problems together in real time. Multiplayer has been a game-changer for my students and for educators who explore it! It is a great opportunity to engage students and promote digital citizenship and the appropriate, responsible use of technology.

For years, I’ve spoken about the need to move students from consumers to creators to innovators. With Delightex Edu, students do more than view and interact with stagnant digital content. Students create and collaborate to design interactive environments, dive into coding experiences, narrate and bring their stories to life, and create immersive worlds for themselves and others to explore. Multiplayer is a game-changer because it adds a whole new dimension, transforming students’ individual creations into shared learning experiences others can enjoy! And when that happens, it is so much more meaningful for learning.

Individual Exploration to Shared Experience

In a Multiplayer Project, teachers launch a live session, and then they become the host. Delightex Edu generates a join link and a session code that can be shared with the class. Students can choose a personalized avatar and enter the same virtual environment as their classmates. When they do this, they can explore, interact, complete challenges, and learn together.

Does it take a lot of time? No. It is easy to get started. Teachers can set up a Multiplayer Project directly or select Multiplayer when creating an Assignment. The technology itself is exciting, but the true value lies in the learning design. Multiplayer is not simply about interacting as avatars in the same digital space. Rather, it is an innovative option that creates opportunities for discussion, negotiation, shared decision-making, peer teaching, problem-solving, and reflection. It fosters the development of essential skills for students.

Why Multiplayer Learning Matters

Research has consistently demonstrated the value of active learning. Studies have shown that active-learning approaches improve student performance. Collaborative learning also creates academic, social, psychological, and assessment benefits because learners work together toward a shared goal or outcome.

Immersive learning environments also increase engagement by giving learners a sense of presence within the experience. In my own classroom, I’ve seen greater engagement, motivation, time on task, and more positive learning experiences. It is important to always focus on the learning target itself and then design the experience leveraging the technology in purposeful ways. This distinction is important. Multiplayer should be connected to clear learning goals. Perhaps students need to gather evidence, explain concepts, solve a challenge, evaluate competing ideas, or create something collaboratively. The virtual environment becomes a space for meaningful interactions rather than feeling like a digital field trip that lacks a clear learning purpose.

Ideas for Early Elementary Classrooms

For younger learners, Multiplayer Projects can emphasize exploration, communication, vocabulary development, and simple problem-solving.

Students might enter a virtual community and work together to identify important places such as a school, library, hospital, fire station, or park. They can discuss what happens in each place and how community members help one another.

To build literacy skills, students could explore the setting of a familiar story, identify characters and objects, and retell events in sequence. A teacher-created AI Buddy could serve as a story character, narrator, or guide, asking age-appropriate questions, and this is a great way to help students understand how to use AI safely to enhance learning.

For math, students could complete a shape hunt, sort virtual objects, compare quantities, or follow directional clues through a maze. In science, they might visit a habitat and identify what different plants and animals need to survive.

At this level, teachers can model digital citizenship by establishing expectations for movement, turn-taking, communication, and helping classmates.

Ideas for Middle School

As students become more independent, Multiplayer can support increasingly complex collaboration. In science, groups might explore a solar-system exhibition, identify planetary characteristics, and work together to solve a sequence of clues. They might investigate an ecosystem or enter a model of a cell and explain the function of each structure.

Social studies students could tour a virtual museum, analyze artifacts, compare civilizations, or participate in a simulation where they assume the role of a historical figure or community member. Rather than just reading about an event, students can explore different perspectives and discuss how decisions affected various groups.

Language arts students might enter the setting of a novel, examine visual clues, interview AI-powered characters, or solve an escape room based on plot, theme, vocabulary, and textual evidence.

World language students can practice asking for directions in a virtual city, ordering food in a restaurant, describing their surroundings, or completing a collaborative cultural scavenger hunt.

Ideas for High School

Students can use Multiplayer Projects for simulations, debates, design challenges, and role-based learning.

A government or civics class could simulate a town council meeting, a legislative hearing, a courtroom proceeding, or an international summit. Students could enter the environment with assigned roles, examine evidence, question one another, and negotiate a solution.

Science and career-technical education students could complete safety inspections, analyze environmental conditions, investigate virtual crime scenes, or troubleshoot a simulated workplace problem. Health science students might explore an anatomical model or practice communicating with an AI Buddy designed to represent a patient.

In literature and history, students could build and lead virtual exhibitions. Rather than submitting a traditional presentation, teams could design an environment, curate primary sources, create interactive characters, and guide visitors through their interpretation.

Creating the Future of Learning Together

The future of education is not simply about giving every student access to more technology. It is about creating opportunities for students to think, communicate, design, question, and solve problems together. Focusing on the learning goals and then adding in various methods and tools to reach those goals.

Delightex Edu’s Multiplayer feature brings these goals into a shared, immersive environment. Whether students are navigating a maze, exploring the solar system, touring a virtual museum, practicing another language, or solving a real-world challenge, they are actively participating in the learning process.

They are not just watching an experience unfold. They are inside it, learning together and building skills that will lead them to a successful future.

About Rachelle

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.

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

**Interested in writing a guest blog for my site? Would love to share your ideas! Submit your post here. Looking for a new book to read? Find these available at bit.ly/Pothbooks

************ Also, check out my THRIVEinEDU Podcast Here!

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AI in Education vs. AI Education, Part II: From Policy to Practice

July 29, 2026July 24, 2026Dr. Rachelle Dené Poth Leave a comment

What Must Happen Next

In Part I of this series, I shared the importance of distinguishing between “AI in Education” and “AI Education”, and why both must move forward together if we want to prepare students for a rapidly changing world.

That distinction is critical.

But it is not enough.

Across the conversations I continue to have with legislators, district leaders, and educators, the next question is becoming clearer:

What does this actually look like in practice?

Because the challenge right now is not awareness.

It is implementation.

Guidelines are being drafted. Policies are being written. Conversations are happening at leadership levels.

But in classrooms, the reality is often very different.

Teachers are still asking:

What should I allow? How do I design lessons so that students are still thinking and not reliant on AI? What should I do if students use AI in ways I did not anticipate? How do I explain responsible use to students and families?

This gap between policy and practice is one of the most important challenges we face. Policies can set direction, but practice determines the impact.

A policy may explain what is permitted, restricted, or expected. However, educators still need support to translate those expectations into lesson design, assessment, classroom conversations, and decisions about student use. I’ve mentioned this before in my Leading Forward in AI series, and I share it again. We need consistency, a shared language, and a classroom connection. Without that connection, even well-written policies may have little effect on classroom practice.

What I’m Seeing in Classrooms

In the schools I work with, students are already using AI tools—often more than adults realize. Students are:

brainstorming ideas
asking questions
exploring concepts
testing tools independently

And it is happening now, so this is not a future issue. And it is no longer a question of whether students will use AI, but rather whether they will be guided in how to use it responsibly.

Without guidance, students experiment in isolation and rely on AI too much, and possibly for the wrong reasons. With guidance, they learn to question outputs, verify information, protect their data, acknowledge their use of AI, and recognize when a tool is supporting their thinking rather than replacing it.

That difference matters.

Moving From Restriction to Responsibility

One of the most common responses to emerging technology has been restriction. Conversations about blocking access, limiting use, and controlling the learning environment of students have been happening even more.

In some situations, restrictions are necessary. Schools must consider student privacy, age requirements, academic integrity, instructional purpose, accessibility, and whether a tool has been properly vetted. But restriction cannot be the entire strategy.

We know that students will use AI outside of the classroom. While approaches that focus on restriction may be in the best interest of protecting students, they do not prepare students for long-term success.

In the future, students will use AI in higher education, the workplace, and in everyday decision-making, which is why they need guidance. By focusing only on restrictions, we miss out on the opportunity to teach responsibility. And responsibility prepares students for real-world environments.

With opportunities in our classrooms, we can support students as they learn about AI. Students need to know how to evaluate AI-generated information, disclose when they have used a tool, protect personally identifiable information (PII), recognize bias, and understand when AI use is appropriate.

What Effective Implementation Requires

From my experience working with districts across the country, successful implementation does not come from a single policy or a single training. It comes from strategic alignment.

Continue reading 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.

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

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.

What Teaching Faculty Want From Professional Development (That’s Not Just Workshops)

July 14, 2026July 7, 2026Dr. Rachelle Dené Poth Leave a comment

Guest post by Tessa Dodson. Opinions expressed are those of the writer.

Educators are all too familiar with “workshop fatigue,” the feeling of sitting through another mandated training session that feels disconnected from the realities of their classroom. To move beyond a compliance-based model of professional development (PD) for teachers, district leaders must explore what educators need to grow. The shift to an empowering, continuous learning model is a crucial strategy for boosting teacher motivation, efficacy, and retention in an increasingly demanding field.

The Disconnect Between Intentions and Training Needs

The default model of professional development has been the one-off workshop or conference. While often well-intentioned, this approach frequently fails to produce lasting change in instructional practice or improve teacher motivation. The core issue lies in a fundamental gap between what research identifies as effective and what is most commonly practiced in schools.

This discrepancy creates a significant challenge for leaders trying to support their faculty. While sustained job-embedded learning is more effective, many teachers still attend short-term workshops, as these are often the most accessible and available options provided by their districts. The result is a cycle of ineffective professional learning that fails to translate into meaningful classroom improvements.

“One-Size-Fits-All” Agendas

A common pitfall of traditional PD is selecting a single topic for an entire district or school. This approach rarely meets the nuanced needs of individual teachers. The challenges faced by a kindergarten teacher are vastly different from those of a high school physics instructor. A seasoned educator requires a different type of support than a novice. When PD is not tailored, it feels irrelevant and fails to honor each teacher’s unique classroom context.

One-Off Workshops

Learning that is not sustained over time is rarely integrated into practice. One-off workshops lack the essential follow-up, coaching, and collaborative feedback loops required for true skill development. Without ongoing support to implement new strategies, troubleshoot challenges, and refine their approach, teachers are likely to revert to their established routines, and the growth potential is lost.

A Lack of Choice

Giving teachers choices goes beyond simply letting them select from a preapproved menu of workshops. True empowerment means inviting them to be active participants in the entire process. Teachers can work together to identify problems in their own classrooms and the school, and then they can co-design learning experiences to address those specific challenges.

Limited Time and Trust

Two of the most valuable resources leaders can provide are time and trust. Effective professional learning cannot be an add-on. Instead, it requires dedicated time within contract hours for teachers to collaborate, observe peers, conduct research, and reflect on their practice. This must be paired with a culture of trust, where leaders have confidence that their faculty will use this time professionally and effectively to improve their instruction and, ultimately, student outcomes.

What Teachers Really Want From Professional Development

The most effective frameworks for teachers’ professional development are built on a foundation of empowerment. This involves a profound shift in mindset, from viewing teachers as recipients of training to recognizing them as professionals who can and should guide their own learning. When school structures are designed to foster teacher autonomy, the impact on professional growth is significant.

A 2023 study found that teachers’ autonomous behavior predicted professional development at work. The study identified key structural factors, such as “empowering teachers” and the “decentralization of responsibilities,” as crucial to creating an environment in which this autonomy could flourish. By trusting teachers to take ownership of their professional growth, leaders can unlock their intrinsic teacher motivation and capacity for innovation.

A landmark report from the Learning Policy Institute outlines seven widely shared features of effective PD, focusing on content, active learning, collaboration, coaching, expert support, and sustained duration. From the teacher’s perspective, this framework translates into a few key training necessities.

Ongoing, Job-Embedded Coaching

Teachers value the opportunity to work with instructional coaches who can provide personalized support. Unlike a formal evaluation, coaching is a partnership focused on growth. A coach can co-plan a lesson, model a new strategy, observe, and provide feedback. Alternatively, a coach may serve as a sounding board in the teacher’s classroom.

Opportunities for Meaningful Collaboration

Educators’ most valuable resource is each other. Many teachers crave structured time to engage in meaningful collaboration with their peers. This can take many forms, including professional learning communities, study groups where teachers collectively plan and refine lessons, and peer mentorship programs. A well-designed teacher-to-teacher mentorship program can be a powerful driver of both individual and collective growth.

Direct Connections to Their Classroom Practice

Teachers want professional learning that is directly relevant and immediately applicable to their work. They are eager for strategies, tools, and knowledge that help them solve the real, everyday challenges they face with their students. When PD is grounded in classroom practice, it feels more like an essential tool for success than a requirement to simply check off.

How Leadership Can Build a Culture of Continuous Learning

School and district leaders are the primary architects of the environment in which teachers receive professional development. By shifting their approach, they can build a culture where teacher-led, continuous learning becomes the norm.

For example, Princeton Public Schools has found success with “Flex PD” programs, which provide staff with release time and resources to pursue professional learning of their choice. By treating teachers as professionals, the district fosters a sense of ownership and relevance that traditional models often lack.

Moving From Compliance to Collective Capacity

Instead of focusing on enforcing PD mandates, leaders should see their role as building their staff’s collective expertise. This requires trusting teachers to take ownership of school-wide goals and empowering them to find the best path to achieving them. When leaders shift their focus from compliance to cultivating collective capacity, they invest more effectively in the long-term growth of their entire school community.

Measuring Success Beyond Attendance Sheets

In a culture of continuous learning, success is measured differently. Instead of tracking seat time or completion certificates, leaders should look for authentic evidence of growth. This can include observing changes in instructional practice, noting increases in staff collaborative conversations, and measuring the impact on student engagement and outcomes.

Taking the First Step Toward Meaningful Growth

The journey toward a more meaningful, empowering, and effective model of professional development for teachers begins with dialogue. By asking educators what they need to grow and trusting their answers, school leaders can take the first crucial step toward building a culture where both teachers and students can thrive.

About Tessa

About Rachelle’s 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.

About Rachelle

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.

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My sessions focus on helping teams:

• understand what AI can and cannot do

• recognize responsible-use considerations

• build confidence using emerging tools

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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.

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