2026 AI Educator Summer Institute

This session will engage participants in the development of a cross-disciplinary data science course aimed at students and teachers with no prior coding background. It introduces the fundamentals of Data Science across subject areas with an introduction of Python and  with Jupyter notebooks. This session will challenge participants to imagine an introductory CS course that cuts across all subjects and can be taught by someone from any department with a little motivation. All backgrounds, subject areas, and skill-levels welcome.

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.

This session will engage participants in the development of a cross-disciplinary data science course aimed at students and teachers with no prior coding background. It introduces the fundamentals of Data Science across subject areas with an introduction of Python and  with Jupyter notebooks. This session will challenge participants to imagine an introductory CS course that cuts across all subjects and can be taught by someone from any department with a little motivation. All backgrounds, subject areas, and skill-levels welcome.

In the afternoon session we will focus on 3 modules that can be adapted to create a Student – Led AI School to Community Event for your school. You will leave with a plan for creating an AI community night at your school. 

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

*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

*This is a cohort session. All participants who attend the morning session are invited to attend an accompanying afternoon expansion pack session each afternoon this week to apply your morning learning to the Special Education classroom.*

The 3-hour afternoon workshop gives participants dedicated creation time to apply the morning's framework to real materials from their own classrooms. It is structured as a creation studio rather than a continuation of the lecture.

Hour 1 (1:00–2:00) — Identify and Design: Participants select one real instructional challenge. A lesson that needs differentiation, a text that needs to be adapted for a lower reading level, an assessment that needs scaffolding, or a documentation task they want to streamline. Small groups form around shared challenges. Special educators learn as much from each other as from any presenter. Introduction to the creation workflow: original material → AI prompt → draft → critical review → revision.

Break (2:00–2:15)

Hour 2 (2:15–3:15) — Create: Hands-on creation using Gemini and NotebookLM. Facilitator circulates with targeted coaching. Structured peer feedback in pairs: Does this serve the student? What did the AI miss that a human expert would catch? What needs to be fixed before this goes near a classroom?

Hour 3 (3:15–4:00) — Share, Critique, and Plan: Small group shares. Not just "look what I made" but "here's what the AI got wrong and how I fixed it." Whole-group reflection on AI as a tool for inclusive practice versus a shortcut that bypasses expertise. Individual action planning to identify one specific thing I will try with students in fall 2026.

Participants leave with a real, usable adapted resource and a realistic, critical framework for continuing independently.

Now that participants understand the goal and exciting potential of this course, we will start exploring the design and content of the course. In the first part of the workshop we will do a group data science activity to get used to Jupyter Notebooks and Python. In the second part of the workshop attendees will be given time to explore resources and build their own lesson in a chosen disciplinary area with the aim of building a library of activities across disciplines that could be used by this introductory course for teachers across the state. 

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 the afternoon session we will focus on 3 modules that can be adapted to create a Student – Led AI School to Community Event for your school. You will leave with a plan for creating an AI community night at your school. 

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

*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

*This is a cohort session. All participants who attend the morning session are invited to attend an accompanying afternoon expansion pack session each afternoon this week to apply your morning learning to the Special Education classroom.*

The 3-hour afternoon workshop gives participants dedicated creation time to apply the morning's framework to real materials from their own classrooms. It is structured as a creation studio rather than a continuation of the lecture.

Hour 1 (1:00–2:00) — Identify and Design: Participants select one real instructional challenge. A lesson that needs differentiation, a text that needs to be adapted for a lower reading level, an assessment that needs scaffolding, or a documentation task they want to streamline. Small groups form around shared challenges. Special educators learn as much from each other as from any presenter. Introduction to the creation workflow: original material → AI prompt → draft → critical review → revision.

Break (2:00–2:15)

Hour 2 (2:15–3:15) — Create: Hands-on creation using Gemini and NotebookLM. Facilitator circulates with targeted coaching. Structured peer feedback in pairs: Does this serve the student? What did the AI miss that a human expert would catch? What needs to be fixed before this goes near a classroom?

Hour 3 (3:15–4:00) — Share, Critique, and Plan: Small group shares. Not just "look what I made" but "here's what the AI got wrong and how I fixed it." Whole-group reflection on AI as a tool for inclusive practice versus a shortcut that bypasses expertise. Individual action planning to identify one specific thing I will try with students in fall 2026.

Participants leave with a real, usable adapted resource and a realistic, critical framework for continuing independently.

Now that participants understand the goal and exciting potential of this course, we will start exploring the design and content of the course. In the first part of the workshop we will do a group data science activity to get used to Jupyter Notebooks and Python. In the second part of the workshop attendees will be given time to explore resources and build their own lesson in a chosen disciplinary area with the aim of building a library of activities across disciplines that could be used by this introductory course for teachers across the state. 

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.

Join us for a fun open house at our neighboring brewery (right between the school and the hotel). All participants will be given a drink ticket for one free drink (of any kind). There will be a buffet of appetizers for your enjoyment. The event is designed to give you a chance to continue the great conversations of the Institute so far, to get to know your fellow attendees, and to mingle while enjoying great food and drink. 

Attendees may purchase additional food and drinks at their own expenses. Families are welcome to join (at attendees expense). Tuesday night is Trivia night at the brewery, starting at 7 p.m.  

Reminder, dinner is not provided at GSSM on this evening.