How to use this page during the Summit

This page is your team’s shared workspace and final report-out page. It captures your group’s process and thinking throughout the Summit and will be used to share your work with others.
Use this page as your team’s working record during the Summit and your final report-out.
The Summit has several different goals and thus you will use the page differently each day: Day 1 is for alignment, Day 2 is for building one useful thing, and Day 3 is for synthesis and report- out.
Look for the green buttons to indicate what you need to edit.
Megaphones 📣 indicate which items you will be presenting during the end-of-day report-outs.
Only the items with megaphones will be visible when you hit the 'Summit Report Out' button.
If you turn off 'Instructions' then you will only see the page content for public display.

Team 7: AI and Education in Environmental Science

Day 1 directions

AI and Education in Environmental Science

Edit Day 1 setup in Markdown

For ESIIL staff

Group Number: 7

Breakout Room #: S340

ESIIL staff edit in Markdown

Team hero image

How to replace the image above

Upload an image that represents your project and welcome people to your page.

Upload your own image to docs/assets/hero/ and replace the file named hero.png. Use a wide image if you can, then refresh the site preview to check how it looks. Keep the file path docs/assets/hero/hero.png if you want the Markdown above to keep working.

Open image folder for changing image

See a completed example

People

Day 1 task

Get to know your team: share your cards (5-7 mins). Update your team roster (2-3 min).

Use the in-person name cards to guide quick introductions.

Name card prompts	Follow-up notes

Edit People in Markdown

Name	Affiliation	Contact	Github
Jennifer Kovacs	Agnes Scott College	jkovacs@agnesscott.edu	echinodermatamata
James Watling	John Carrol University	jwatling@jcu.edu	jwatling

Team Norms and Decision Making

Day 1 task

Suggested Self-Facilitation Instructions:

Round Robin: Everyone shares 1 norm that they think will be important for their team during the Summit and perhaps following the Summit (2 min).
After everyone has shared, make a list with as many norms as possible in GitHub (5–7 min).
Vote on your top 3 ideas. (Each person gets 3 votes; you can use all your votes on 1 idea or spread them out) (2 min).
In GitHub, move all team norms with votes to the top of the list.

Gradients of agreement

Edit Team Norms in Markdown

Our team norms:

Get all ideas on the table but don't be afraid to express a contradictory opinion
Norms are an ongoing conversation
Group decision making
Respect individual and collective timelines and availability
Discuss & disclose use of AI

Our decision making strategy:

Given our small group size, we expect to make decisions by mutual agreement (to be revisted if/as group evolves)

Our product(s) 📣

Day 2 Tasks

Morning Focus: questions, hypotheses, context; add at least one visual (photo of whiteboard/notes)

Afternoon Focus: try a few datasets and analyses. Keep it visual, keep it simple. Update the site to reflect what you test.

Edit content below here in Markdown

Short term:

Create the framework for guided-inquiry learning modules for the undergraduate environmental science classroom that teach environmental content, data literacy, and AI skills
Build an example module using our framework

Long term:

Recruit instructors to form a working group and build their own modules and case uses at the QUBES BIOME summer meeting in June
Teach an AI module using Colab & Gemini & NEON's flux data in the next academic year
Plan for an ESIIL working group to connect more faculty with content experts to create teaching modules for AI and Env Sci

Our question(s) 📣

Our framework includes these five core elements:

An environmental theme, such as carbon cycling, water quality, biodiversity, land-use change, environmental justice, or ecosystem resilience.
A data component built around authentic datasets, ideally from open or shared sources that can support cross-course or cross-institution use.
An AI-facilitated component in which students use AI to support tasks such as question generation, code drafting, pattern identification, summarization, visualization, or interpretation.
A human-in-the-loop structure in which student and instructor judgment are required at each stage, especially when evaluating AI-generated claims, code, or interpretations.
A synthesis and communication component in which students explain findings to an audience such as classmates, community members, decision-makers, or other researchers.

All modules in the network should be built around the following principles:

1. Question-driven inquiry: Each module begins with an environmental question that is scientifically meaningful and suitable for student investigation using available data.

2. Authentic data use: Students work with real environmental data rather than toy examples so that they experience uncertainty, variation, and limits of interpretation.

3. Scaffolded independence: The module should move from guided work to greater student independence, following the EDDIE logic that early activities provide structure and later activities increase student choice and responsibility.

4. Human oversight of AI: AI use must never stand alone; students should document prompts, inspect outputs, verify claims, and revise work based on disciplinary evidence.

5. Communication as synthesis: The end point of the module is not only analysis, but communication of evidence-based conclusions in an audience-appropriate form.

6. Adaptability: Instructors should be able to teach the full module, shorten it, or substitute a local dataset or tool without changing the core learning goals.

Intentions

Create a collection of modular teaching activites that teach environmental science content, good AI practices, guided inquiry using real data, and effective communication

All modules share four overarching learning goals emphasizing content knowledge, data literacy, AI skills, and communication:

Method or workflow visual

All modules share a common structure, beginning with an introduction to the environmental theme for the module, leading to a guided inquiry component where both data literacy and AI collaboration are emphasized, and ending with synthesis/reflection and communication component:

Method or workflow visual

Click here Module Template

Click here Module Structure

Example Module: Sink or Source? Exploring Forest Carbon Dynamics with NEON Data and AI

This module, titled "Sink or Source?," guides students through investigating ecosystem carbon exchange by transitioning from following a standard scientific tutorial to becoming active "Code Editors" with an AI assistant. Using authentic data from the National Ecological Observatory Network (NEON), students collaborate with an AI assistant to modify code, analyze environmental variables like temperature or light, critically validate the AI's output through a human-in-the-loop workflow, and reflect and report on their observations. This includes Colab code to analyze NEON data, we turn Gemini from a generic chatbot into a grounded scientific collaborator. We connect to the API, then build a context block that packages up everything Gemini needs to give useful answers — the site name, variable descriptions, actual data values, and summary statistics from our NEON dataset. And the student handout explains what we've done and why it is important. From this point on, every student question to Gemini arrives pre-loaded with that context, so it responds like a knowledgeable colleague who has already read the data, not like a search engine returning textbook definitions.

Click here Student handout

Click here Code file

Why this matters (the “upshot”) 📣

This matters because:

It keeps human scientific judgment at the center by starting from trusted datasets and using AI tools to explain and modify code, which foregrounds verification, uncertainty, and reproducibility.

Links AI literacy to core disciplinary outcomes (carbon cycling, data skills, communication) and to the environmental footprint of computation itself via prompt/energy tracking, so students learn to treat AI use as a choice with trade‑offs.

People who could use this:

Environmental science, ecology, and sustainability faculty who want to integrate AI into existing data‑rich labs (NEON, flux, water quality, biodiversity) without rebuilding their courses from scratch.

Want to develop a module to add to our collection-- here's a template

AI-Enhanced Environmental Data Inquiry Module Template

An extension of the Project EDDIE A-B-C framework that layers AI tool use into data-driven environmental inquiry. Modules follow standard EDDIE design — authentic public datasets, quantitative reasoning, scaffolded independence — with an added strand for critical AI literacy.

Module Overview


Scientific question
Dataset
AI tool(s)
Audience
Time
Prerequisites

Learning Objectives

By the end of this module, students will be able to:

[Environmental understanding] Explain...
[Quantitative reasoning] Analyze and interpret...
[AI literacy] Use an AI tool strategically, evaluate its outputs critically, and document how it supported their work.
[Communication] Synthesize findings and communicate them for a defined audience.

Part A — Introductory

Instructor-guided. Students build scientific context, explore the dataset, and complete bounded AI-supported tasks with explicit verification.

Students will: - Explore the scientific question and relevant environmental concepts - Orient to the dataset structure, variables, and known limitations - Complete 1–2 guided interactions with the AI tool using provided scaffolds - Verify AI outputs against data or metadata before accepting them

AI addition: Students compare an AI-generated response to the raw data or metadata and note where it was accurate, incomplete, or wrong.

Part B — Exploratory

Increasing independence. Students investigate a self-directed question, use AI selectively, and check reasoning with peers.

Students will: - Refine or choose a research question appropriate to the dataset - Conduct data exploration, visualization, and analysis - Use the AI tool to support selected steps (e.g., code generation, pattern interpretation, literature context) - Review and critique AI outputs before incorporating them - Discuss findings and AI use with peers

AI addition: Students must record a pre-AI observation before using the tool for any analytical step, then compare it to AI output afterward.

Part C — Advanced

Student-driven. Students make independent choices about data, analysis, and communication; reflect explicitly on AI use.

Students will: - Choose a data subset, analysis approach, or comparison site to extend their question - Produce a communication product for a defined audience - Include an AI transparency statement: what the tool contributed and where human judgment was essential

AI addition: Students reflect on how AI shaped their inquiry — what it accelerated, what it got wrong, and what they would do differently.

Required Module Materials

(Consistent with EDDIE module standards)

[ ] Student handout
[ ] Instructor guide with answer key
[ ] Dataset package with metadata and quality notes
[ ] AI use guide — defines permitted uses and prompt logging requirements
[ ] Verification checklist — students confirm AI outputs against data before using them
[ ] Assessment rubric aligned to learning objectives

Notes for Developers

This template is designed to work with any AI tool — large language models, image classifiers, species identification tools, anomaly detectors, or others. The AI literacy strand is tool-agnostic: the core skills (provide context, evaluate output, verify against source, document use) apply regardless of the specific technology.

Modules should meet the EDDIE module rubric before the AI layer is added. If your module doesn't yet work as a standard EDDIE module, start there first.

Next Steps

Short term: We have applied to present a Work In Progress Poster at the upcoming QUBES BIOME Summer Meeting and will recruit for a QUBES Fall Working Group Long term: Apply to an ESIIL Working Group around developing AI-assisted Data-Inquiry Teaching Modules

Day 3 Tasks

Sythesis: highlight 2-3 visuals that tell the story; keep text crisp. Practice a 6-minute walkthrough of the homepage. Why -> Questions -> Data/Methods -> Findings -> Next

Edit content below here in Markdown

Findings at a glance 📣

Headline 1 — Training the next generation of environmental data scientists requires teaching AI as a scientific skill, not a shortcut Students working with authentic environmental datasets — flux towers, water quality sensors, biodiversity surveys — quickly discover that AI outputs are only as good as the context they are given. The same critical habits that make a good data scientist make a good AI user.

Headline 2 — Faculty don't need to rebuild their courses — they need a retrofit A working framework, a shared template, and one worked example show that AI literacy can be layered into existing data inquiry modules without displacing the environmental science. The scaffold stays the same; the AI strand runs through it.

Headline 3 — The next cohort of environmental scientists will use AI whether we teach it or not — the question is whether they'll use it well Building human-in-the-loop habits now — verify outputs, document prompts, trace claims back to data — is the difference between students who are empowered by AI tools and students who are misled by them.

Visuals that tell a story 📣

Four learning goals, one module: environmental understanding, data literacy, AI literacy, and communication develop together — not as separate add-ons.

Module structure The module arc: environmental theme → guided data and AI inquiry → synthesis and communication. The human-in-the-loop requirement runs through every stage.

What’s next? 📣

Short term: - Present a Work-in-Progress poster at the QUBES BIOME Summer Meeting and recruit for a fall working group - Pilot the Source or Sink? module in a course this academic year and document the student experience

Long term: - Build a working group of PUI environmental science faculty developing and sharing modules across data types and AI tools — not LLM-only - Apply for an ESIIL Working Group to connect faculty with data infrastructure experts and scale module development across institutions

Who should see this next: - Do you have a data-set that you think would be awesome for this kind of teaching module? Let us know! - Environmental and ecology researchers who supervise undergraduates or early-career scientists working with large observational datasets - NEON, LTER, and other open data network education staff

Cite & Reuse

If you use these materials, please cite:

Summit Team. (2026). Summit Group 2026 Team 7 — Innovation Summit 2026. https://github.com/CU-ESIIL/Summit_group_2026_7

License: CC-BY-4.0 unless noted.