2026 AI Educator Summer Institute

Explore five ways to introduce and expand integrated A.I. instruction in your K-5 classroom. We will explore how to help students understand important themes about A.I., including ethics, policy, and how A.I. works (it is not your friend!). Then we will explore tools and resources that will aid you in bringing A.I. into your existing lessons in engaging ways without disrupting your flow or time constraints. You will leave with at least one (hopefully more!) idea to use this fall in their classrooms.

In this hands-on session, educators will explore IBM SkillsBuild through Discovery Education and learn how to navigate courses that build foundational AI and digital skills with confidence. Participants will gain a clearer understanding of AI concepts, digital literacy, responsible use, and workforce-aligned skills that can support both teacher learning and student readiness.

This session will also lay the foundation for the afternoon Expansion Pack Workshop, where educators will apply what they learned to classroom instruction. Educators will use their new AI knowledge to determine when AI is the right tool, how AI can increase rigor, and how AI can provide additional supports or accommodations for students as they design lessons for their own subject areas and grade levels.

As teachers, we get why our lessons about data are important, but do our students? Let's explore ideas that help students become more engaged in lessons about data and help them find relevance in what they are studying.

In this interactive afternoon workshop, educators will move from awareness to application by exploring research-based Discovery Education SOS Instructional Strategies that can be used across grade levels and content areas. Participants will work collaboratively to develop, curate, adapt, and practice strategies they can immediately implement in their classrooms.

Educators will first explore high-impact instructional strategies that work with or without AI, then examine how AI can be used intentionally to increase rigor, support accommodations, extend student thinking, and create more responsive learning experiences. Using knowledge from the morning session, participants will design or adapt classroom-ready lessons for their own subject areas while determining when AI adds value and when strong instructional strategy alone is enough.

Educators will leave with:

5–7 practical instructional strategies they can use for engagement, discussion, collaboration, and formative assessmentA stronger understanding of when AI can meaningfully support instructionA classroom-ready plan for using AI to increase rigor, support student needs, and strengthen learning

Workshop time will be structured by providing teachers with options for how to use the workshop time productively. The three main options teachers will be directed towards will be reworking existing problems in their own materials, creating their own progression of lessons following the same sequence from their curriculum, or developing a student driven/student centered data unit. Teachers will be able to determine their own starting ppoint and move flexibly among these outcomes. Collaboration with grade level peers and school based teams will be encouraged, but not required.

Among the additional resources teachers will be encouraged to use are CODAP, PHet, Pudding.cool, and https://www.datascience4everyone.org/datasets

Referencing what we explored in the morning session #14, attendees will dig into the tools and resources to curate, create, or adapt at least one (hopefully more!) complete plan that is aligned to their standards and instructional materials that they can use this fall in their classrooms.

*This is a cohort session. Following one morning session on Monday, July 13, this group will meet every afternoon to continue its exploration and learning.*

In the afternoon sessions participants will continue to examine various problems in applied mathematics and will explore how AI connects disparate mathematical concepts.  By attempting challenging problems from various resources (e.g., the Mathematical Association of America, The Art of Problem Solving, and The American Mathematical Society), teachers will again apply a blend of traditional techniques and numerical tools to solve advanced problems; however, unlike the morning session these problems will include examples that current AI models often fail to thoroughly grasp. By discovering more of AI’s limitations, they will be reminded of the value of primary source materials, such as time-tested, multi-edition textbooks.  These examples will reinforce the necessity of human oversight and boost professional confidence in identifying "hallucinations" or errors.  Additionally, teachers will also be encouraged to research and solve relevant problems of interest to them and their particular course w

The afternoon workshop will allow teachers to investigate some of these tools and develop their own models, simulations, or data interpretation ideas. I’d like to introduce my collaboration with Taylor to create a simulation for my passion project, as well as some other data analysis tools (CODAP, Data Story Bytes, and websites to collect large data sets from including Kaggle).

The workshop would be divided into two mini sessions. The first will focus on simulation creation. The teachers will be able to work on creating their own simulations in pairs, then share with the group. The second session will focus on data analysis tools and will be conducted in a similar manner.

In this interactive afternoon workshop, educators will move from awareness to application by exploring research-based Discovery Education SOS Instructional Strategies that can be used across grade levels and content areas. Participants will work collaboratively to develop, curate, adapt, and practice strategies they can immediately implement in their classrooms.

Educators will first explore high-impact instructional strategies that work with or without AI, then examine how AI can be used intentionally to increase rigor, support accommodations, extend student thinking, and create more responsive learning experiences. Using knowledge from the morning session, participants will design or adapt classroom-ready lessons for their own subject areas while determining when AI adds value and when strong instructional strategy alone is enough.

Educators will leave with:

5–7 practical instructional strategies they can use for engagement, discussion, collaboration, and formative assessmentA stronger understanding of when AI can meaningfully support instructionA classroom-ready plan for using AI to increase rigor, support student needs, and strengthen learning

Workshop time will be structured by providing teachers with options for how to use the workshop time productively. The three main options teachers will be directed towards will be reworking existing problems in their own materials, creating their own progression of lessons following the same sequence from their curriculum, or developing a student driven/student centered data unit. Teachers will be able to determine their own starting ppoint and move flexibly among these outcomes. Collaboration with grade level peers and school based teams will be encouraged, but not required.

Among the additional resources teachers will be encouraged to use are CODAP, PHet, Pudding.cool, and https://www.datascience4everyone.org/datasets

Referencing what we explored in the morning session #14, attendees will dig into the tools and resources to curate, create, or adapt at least one (hopefully more!) complete plan that is aligned to their standards and instructional materials that they can use this fall in their classrooms.

*This is a cohort session. Following one morning session on Monday, July 13, this group will meet every afternoon to continue its exploration and learning.*

In the afternoon sessions participants will continue to examine various problems in applied mathematics and will explore how AI connects disparate mathematical concepts.  By attempting challenging problems from various resources (e.g., the Mathematical Association of America, The Art of Problem Solving, and The American Mathematical Society), teachers will again apply a blend of traditional techniques and numerical tools to solve advanced problems; however, unlike the morning session these problems will include examples that current AI models often fail to thoroughly grasp. By discovering more of AI’s limitations, they will be reminded of the value of primary source materials, such as time-tested, multi-edition textbooks.  These examples will reinforce the necessity of human oversight and boost professional confidence in identifying "hallucinations" or errors.  Additionally, teachers will also be encouraged to research and solve relevant problems of interest to them and their particular course w

The afternoon workshop will allow teachers to investigate some of these tools and develop their own models, simulations, or data interpretation ideas. I’d like to introduce my collaboration with Taylor to create a simulation for my passion project, as well as some other data analysis tools (CODAP, Data Story Bytes, and websites to collect large data sets from including Kaggle).

The workshop would be divided into two mini sessions. The first will focus on simulation creation. The teachers will be able to work on creating their own simulations in pairs, then share with the group. The second session will focus on data analysis tools and will be conducted in a similar manner.