Zero-Cost AI and Data Science Resources Featured in September 16 Education Session

College educators looking for a lower-cost way to teach artificial intelligence, programming and data science can explore a free September 16 information session highlighting NSF-supported CloudBank Classroom, a cloud-hosted JupyterHub environment that lets students run Python, R and C++ code directly in a browser without requiring institutions or learners to purchase their own specialised computing infrastructure. The session, hosted by NAAIC with NSF CloudBank Classroom, is scheduled for 2 PM Eastern Time and will feature educators with experience using the platform in data-science instruction.

The event focuses on one of the biggest practical problems in AI education: access.

Teaching data science can require:

• Software installation
• Compatible computers
• Cloud infrastructure
• Technical support

CloudBank Classroom is designed to reduce those barriers by placing the development environment in a web browser.

September 16 Zero-Cost AI & Data Science Session: Key Details

Event	Zero-Cost AI & Data Science Resources With NSF CloudBank Classroom
Date	September 16, 2026
Time	2 PM ET
Format	Online
Host	NAAIC
Platform Featured	CloudBank Classroom
Main Audience	College Educators
Focus	Zero-cost open-source AI and data science instruction

What Is CloudBank Classroom?

CloudBank Classroom is a lightweight, cloud-hosted JupyterHub built for teaching.

Students can log in through a browser and receive access to a configured programming environment.

Supported Technologies Include

• Python
• R
• C++
• Jupyter Notebook
• JupyterLab
• RStudio
• VS Code

Why Browser-Based Computing Matters

In a traditional programming course, students may spend substantial time dealing with:

• Installation errors
• Operating-system differences
• Package versions
• Missing libraries

Cloud Environments Can Standardise the Classroom

If every student uses the same hosted environment, the instructor can reduce technical differences between devices.

Students Can Run Code Without Local Setup

CloudBank says its environment comes preloaded with common data-science tools.

Students can:

• Write code
• Analyse datasets
• Complete assignments
• Create visualisations

through the browser.

Persistent Storage Is Included

Each learner receives storage so work does not disappear every time the browser session ends.

Students Can Use Existing University Credentials

The platform supports institutional authentication.

That can reduce the need for students to create additional accounts.

Why Is It Described as Zero Cost?

CloudBank says U.S.-based educators can request classroom access through programs supported by the National Science Foundation, including ACCESS and the NAIRR Pilot.

NSF Supports CloudBank

The National Science Foundation has invested in expanding CloudBank to improve access to advanced cloud computing for research and education.

In 2025, NSF announced a $20 million award to expand CloudBank.

CloudBank Works With Major Cloud Providers

The expanded CloudBank ecosystem has included providers such as:

• Amazon Web Services
• Google Cloud
• IBM Cloud
• Microsoft Azure
• NVIDIA DGX Cloud

Students Do Not Need to Manage Those Commercial Accounts Directly

The goal is to simplify access for education and research users.

The September 16 Session Features UC Berkeley Experience

NAAIC says faculty with UC Berkeley experience will discuss how similar infrastructure has been incorporated into AI and data-science teaching.

Berkeley Has a Long History With Jupyter-Based Data Science Education

UC Berkeley's Data 8 ecosystem helped popularise notebook-based introductory data-science education.

What Is the Berkeley Data Stack?

The Berkeley Data Stack includes open-source tools such as:

• Jupyter notebooks
• Online textbooks
• One-click notebook links
• Autograding tools

CloudBank Extends This Model Beyond Berkeley

The goal is to make similar infrastructure available to educators at other institutions.

Community Colleges Can Benefit Especially

A community college may not have:

• A dedicated cloud-engineering team
• Large computing clusters
• High software budgets

Cloud Access Can Reduce Institutional Inequality

If students at a community college can work with the same type of notebook environment used at a major research university, infrastructure becomes less of a barrier.

Hardware Differences Can Create Hidden Inequality

In a data-science course, one student may own:

• A high-end laptop

while another student relies on:

• An older low-cost device

Browser-Based Computing Can Reduce That Gap

The heavy computation occurs remotely rather than depending entirely on the student's laptop.

But Students Still Need Internet Access

Cloud-based education does not eliminate every access problem.

Students still need:

• Reliable internet
• A browser-capable device

What Is JupyterHub?

JupyterHub provides multi-user access to Jupyter notebook environments.

Jupyter notebooks combine:

• Code
• Text
• Equations
• Charts
• Results

in one interactive document.

Why Jupyter Is Popular in Data Science

Students can see the relationship between:

• Question
• Code
• Data
• Output
• Interpretation

Notebooks Can Make Coding Less Intimidating

A beginner does not need to build an entire software application.

They can run individual blocks of code and immediately see what happens.

Python Is Central to AI and Data Science Education

Python is commonly used for:

• Data analysis
• Machine learning
• Visualisation
• Scientific computing

R Remains Important for Statistics

R is widely used in:

• Statistics
• Social science
• Public health
• Research

C++ Can Support More Technical Computing Courses

Including multiple programming languages increases the range of courses that can use the platform.

CloudBank Classroom Also Supports VS Code and RStudio

This gives students experience with tools commonly used beyond introductory notebooks.

Assignments Can Be Distributed Through nbgitpuller

Instructors can distribute notebook materials directly into student environments.

This can reduce time spent asking students to download and configure files manually.

Grading Extensions Can Support Classroom Workflow

Notebook-based courses can incorporate grading tools that help instructors distribute and evaluate assignments.

Reproducibility Is Another Benefit

Data-science results should ideally be reproducible.

A shared cloud environment reduces the risk that one student's code only works because of a unique software configuration.

AI Education Needs More Than Chatbots

When people hear “AI education,” they often think only of generative AI tools.

But serious AI instruction can include:

• Programming
• Data preparation
• Statistics
• Machine learning
• Model evaluation

Students Need to Understand Data

A model is only as useful as the:

• Data
• Assumptions
• Evaluation

behind it.

Hands-On Coding Builds Deeper AI Literacy

A student who trains a small model can better understand:

• Why outputs change
• What bias means
• Why accuracy is imperfect

Data Science Can Be Taught Across Disciplines

Notebook-based instruction can support:

• Business
• Biology
• Economics
• Political science
• Public health
• Engineering

AI Is Becoming a General Academic Skill

Students do not necessarily need a Computer Science major to benefit from:

• Data literacy
• Basic coding
• AI evaluation

Open-Source Tools Reduce Vendor Lock-In

Using open tools can make it easier to:

• Reuse course materials
• Move between environments
• Share notebooks

Open Source Does Not Mean No Support Is Needed

Institutions still need:

• Instructor training
• Technical assistance
• Course design

CloudBank Provides Consulting and Helpdesk Support

The program offers support for:

• Onboarding
• Cloud setup
• Troubleshooting

Educators Should Start With One Course

Before deploying across an entire university, institutions can pilot the environment in:

• Introductory data science
• Programming
• Statistics

Measure Whether It Actually Removes Barriers

A pilot should examine:

• Student login success
• Technical-support requests
• Assignment completion
• Student feedback

What Educators Can Learn From the September 16 Session

NAAIC lists takeaways including:

• How the zero-cost JupyterHub works
• How educators integrate it into courses
• How open-source tools can support AI and data-science modules

Who Should Attend?

The session is especially relevant for:

• College faculty
• Data-science instructors
• Computer Science faculty
• Instructional technology teams
• Academic leaders

What Students Can Do Independently

Even students without access to this particular institutional environment can build skills through open resources involving:

• Python
• Jupyter
• Public datasets
• Open-source textbooks

Start With Projects, Not Only Videos

Watching a data-science lecture is different from analysing real data.

A Good Beginner Project Could Involve

• Cleaning a small dataset
• Creating charts
• Calculating summary statistics
• Explaining findings

Then Progress to Machine Learning

Students can later explore:

• Classification
• Regression
• Model evaluation

Final Takeaway

NAAIC and NSF CloudBank Classroom are holding a September 16 information session at 2 PM Eastern Time to show college educators how they can use a zero-cost, cloud-hosted JupyterHub for AI and data-science teaching.

The platform supports Python, R, C++, Jupyter, RStudio and VS Code while giving students persistent storage and browser-based computing without requiring local software setup.

The larger significance is access. Data-science education can be difficult for colleges with limited technical infrastructure and for students using lower-powered computers.

By using NSF-supported cloud resources and open-source tools, educators can reduce some of those barriers and focus more classroom time on coding, data analysis and AI concepts rather than software installation and computer configuration.