2026 AI Educator Summer Institute

This session equips educators to address key limitations of AI tools, including bias and hallucination, often caused by reliance on uncontrolled or unreliable sources. It provides participants with the knowledge and hands-on experience needed to guide AI toward generating insights from curated, reliable, and controlled resources.    Participants will begin by comparing open- and closed-source AI tools, developing a clear understanding of their strengths, limitations, and appropriate use cases. They will then explore two project examples created with a closed-source platform such as Google NotebookLM, examining how these models can be adapted for technology and engineering classroom contexts. Next, participants will develop their own project and resource library on a topic of personal or curricular relevance, emphasizing organization, content curation, and instructional design. By the end of the session, educators will leave with a project in progress and a practical framework for selecting and applying AI tools in teaching.

AI is fundamentally changing research. This session will draw directly from active research in plant phenomics, image-based trait analysis, and plant breeding to demonstrate what AI-assisted science looks like in practice. We'll walk through concrete examples: how convolutional neural networks and on-device models are automating the extraction of morphological traits from field images, how large language models are accelerating literature synthesis in crop science, and how AI pipelines are compressing analyses. What do these shifts mean for how we teach science? When a model can segment an image or predict yield, what skills actually matter? We'll dig into how to design data collection exercises and research projects so that students build genuine scientific judgment rather than just prompt-writing habits. You'll leave with a grounded understanding of where plant science and phenomics research is heading, and practical strategies to make your classroom reflect that reality.


Understand how image data can be used with AI for effective research outcomes

Powering Intelligence: How Electric Utilities Enable the AI Revolution

After engaging in this session, participants will be able to explain why AI uses so much electricity and how utilities like Duke Energy are addressing this demand while also using AI tools to run the grid better.

In this fascinating session, educators will learn how utilities like Duke Energy are using AI today to run the grid better. They will look inside a Data Center and discuss why AI uses so much electricity. Finally, attendees will get a glimpse into how utility companies are addressing rapid load growth while protecting customers. Attendees will learn about AI in the real world and how it impacts their lives every day. Educators are encouraged to consider how take this knowledge into their classrooms. 

In this session, I will invite educators to think about AI as more than a classroom tool. Instead of focusing only on prompting or productivity, we will explore AI literacy as a way to help students ask questions about ethics, bias, decision-making, civic life, and real-world systems. Using examples from English and interdisciplinary teaching, I will share practical strategies such as case studies, discussion prompts, short inquiry tasks, and values-based reflection. The goal is to help teachers move from general concern about AI to concrete classroom practice. Participants will leave with a clearer framework for teaching about AI, not just teaching with AI, and they will be ready to extend that work in the afternoon workshop.

In this extended afternoon workshop, participants will build on the morning session through focused, hands-on work. Educators will revisit the two project examples, taking a deeper look at their structure, design, and classroom applications, with an emphasis on adapting them to their own technology and engineering contexts.

Participants will continue developing the projects they began earlier, refining their resource libraries and expanding content using a closed-source platform such as Google NotebookLM. The session emphasizes curating high-quality materials, aligning resources with instructional goals, and designing practical implementation strategies, with opportunities for guided exploration and collaboration.

By the end of the workshop, educators will share their projects, exchange insights, and leave with a more developed, classroom-ready product and greater confidence in applying AI tools to support student learning.

Helping your students understand that data also means images is an important aspect of AI literacy. Using the understanding from the morning session, teachers will develop an activity based on their standards that utilizes image data. 

The afternoon workshop will be hands-on and product-based. After a short recap and model, participants would choose an existing lesson, text, unit, or topic from their own classroom and use a simple planning template to adapt it into an AI literacy resource. I will provide a small menu of sample case studies, guiding questions, and activity structures so teachers can either build from scratch or revise something they already teach. Most of the workshop will be devoted to drafting, conferring, and peer feedback, with time at the end for revision and sharing. The goal is for every participant to leave with at least one usable product—a discussion protocol, mini-lesson, case-study activity, assessment, or short inquiry sequence. If the participants stay for the entire  three hours, participants will expand their product into a fuller mini-unit; if they stay for 90 minutes, the focus will stay on one strong classroom-ready resource.

*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.

In this extended afternoon workshop, participants will build on the morning session through focused, hands-on work. Educators will revisit the two project examples, taking a deeper look at their structure, design, and classroom applications, with an emphasis on adapting them to their own technology and engineering contexts.

Participants will continue developing the projects they began earlier, refining their resource libraries and expanding content using a closed-source platform such as Google NotebookLM. The session emphasizes curating high-quality materials, aligning resources with instructional goals, and designing practical implementation strategies, with opportunities for guided exploration and collaboration.

By the end of the workshop, educators will share their projects, exchange insights, and leave with a more developed, classroom-ready product and greater confidence in applying AI tools to support student learning.

Helping your students understand that data also means images is an important aspect of AI literacy. Using the understanding from the morning session, teachers will develop an activity based on their standards that utilizes image data. 

The afternoon workshop will be hands-on and product-based. After a short recap and model, participants would choose an existing lesson, text, unit, or topic from their own classroom and use a simple planning template to adapt it into an AI literacy resource. I will provide a small menu of sample case studies, guiding questions, and activity structures so teachers can either build from scratch or revise something they already teach. Most of the workshop will be devoted to drafting, conferring, and peer feedback, with time at the end for revision and sharing. The goal is for every participant to leave with at least one usable product—a discussion protocol, mini-lesson, case-study activity, assessment, or short inquiry sequence. If the participants stay for the entire  three hours, participants will expand their product into a fuller mini-unit; if they stay for 90 minutes, the focus will stay on one strong classroom-ready resource.

*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.