Spring 2026 Working Connections

February 13, 2026 - April 17, 2026

Registration is Closed

Program Policies

The goal of the National IT Innovation Center’s (NITIC) Working Connections professional development is to equip IT faculty at two-year institutions of higher education with the expertise needed to teach their track content in a subsequent semester. This ensures that the most current information reaches their classrooms, either as a stand-alone course or as supplemental material to an existing course.

Cost:

Tuition is FREE; there is no fee to attend.

Eligibility:

Working Connections is for faculty and administrators currently teaching IT credit courses (full-time or adjunct) at a regionally accredited U.S. two-year community college or technical college.
To ensure equitable access to new learning opportunities, participants may not enroll in the same track more than once. Tracks that repeat previously offered content will be clearly noted, and individuals who have already completed the course are not eligible to retake it.
Attendees are expected to use what they learn in their track to teach or supervise a class in the next 12 months.
High school teachers may only attend if they also teach as a community college adjunct.
Seats will be limited to 2 per institution. Additional faculty will be placed on a waitlist and will receive a seat if space becomes available after registration closes.

Registration:

Completing the registration form requests your seat. Your seat is not confirmed until you receive a registration confirmation email from NITIC.
Participants may only attend ONE session for Spring 2026 Working Connections.
Each individual may only submit one application for registration. Only the first submission will be considered, and any subsequent registrations will be disregarded without further notice.
IT Innovation Network (ITIN) member institutions will have a priority window to register and will be notified of the dates via the NITIC mailing list.

Attendance Requirements:

This is a synchronous online workshop.
Instructors have been instructed to track attendance and participation. Participants are expected to attend and actively engage in all scheduled sessions. Attendance means contributing to discussions, completing in-class activities, and being present for live instructions – not just logging in.
Missing more than 25% of the total class time will disqualify you from earning the Credly badge. Participants must attend at least 75% of the total instructional time to adhere to the standards of the program.
If you anticipate any absence, notify your instructor and NITIC in advance. If your absence is unexpected, please notify your instructor and NITIC as soon as you are able.
Instructors are not required to provide make-up work or spend time outside of scheduled sessions helping participants catch up if time is missed. Any make-up work is at the instructor’s discretion, and completion of the work does not override the 25% limit.

Cancellation/Track Changes:

If you must cancel your registration or request a track change, please notify Mark Dempsey at mdempsey@collin.edu immediately before the deadline.
To be good stewards of our NSF ATE grant funding, we must fill all available seats. Attendees who register but then fail to show up without providing advance notice may be ineligible for future Working Connections workshops. Please inform us right away if you’re not able to attend.

Tracks:

Tracks run for the entire duration of Working Connections session; attendees may only select one track.
Some tracks have specific pre-requisites or requirements. Be sure to read the track details before requesting to register.
Tracks may be repeated throughout the year. See the track details to ensure you’re not registering for a track you’ve already completed.
Seating capacity varies by lab, track, and instructor, but typically capped at 20 attendees.
Webcam and dual monitors are highly recommended. Tracks often require being able to read instructions and perform the project.
Recordings and use of AI notetaking assistants during online tracks are left up to the sole discretion of the instructor. NITIC is not facilitating, storing, or managing recordings or AI transcriptions.
Be sure to check for time zone differences. You are responsible for ensuring you do not miss your track.

Completion Credential:

NITIC has teamed up with Credly to provide digital badges to showcase verified Working Connection credentials.
Only those who attend 75% or more of the course AND pass the required track assessment with a grade of 80% or better will receive their badge.
Badges will be issued within 30 days of completion and can be showcased on LinkedIn, email signatures, or printed as a certificate. Hard copies can be printed from Credly’s website and will reflect CEUs earned.

Survey:

All attendees will complete a survey before the end of the event.
Longitudinal surveys will continue to be sent after the event to measure lasting impact.

WAITLIST ONLY

SESSION I: AI Fundamentals to Classroom Transformation: Tools, Assessment, and Curriculum Reimagined (INTRO)

Fridays, February 13 – March 13, 1 PM – 5 PM ET

Description

This 20-hour professional development track supports educators in reimagining teaching, assessment, and curriculum design in response to the rapid integration of artificial intelligence across education and the workforce. The experience is intentionally hands-on, with participants actively using AI tools throughout each session rather than observing demonstrations alone.

Participants develop a clear understanding of foundational AI concepts and explore how large language models function as instructional support tools rather than content shortcuts. Through guided, interactive work with tools such as ChatGPT, Claude, Gemini, NotebookLM, Scribe, and Guidde, educators practice streamlining course design, creating clearer instructional materials, and improving student support in real time.

A central focus of the track is the redesign of assessment practices for an AI-enabled classroom. Participants engage directly in reworking existing assignments into authentic, AI-aware assessments that emphasize critical thinking, process, reflection, and real-world application. By the end of the track, each participant leaves with practical workflows, instructional artifacts, and assessment strategies they can immediately implement in their own courses using primarily free or institutionally accessible AI tools.

Certification Prep

No formal certification
Supports institutional AI literacy goals, faculty professional development hours, and workforce-aligned AI skill development

Objectives

By the end of this track, participants will be able to:

Explain how generative AI is transforming teaching, learning, and curriculum across disciplines.
Apply LLMs and AI tools to course design, content creation, and student engagement.
Design authentic, AI-aware assessments that emphasize critical thinking and process over product.
Evaluate AI tools and workflows to determine appropriate, ethical classroom use.

Pre-requisites

None, curiosity and willingness to explore new instructional approaches recommended.

Required Textbook

None.

Suggested/optional Textbook

The instructor may provide digital resources, examples, and templates. The instructor may provide optional open-access articles and tool documentation.

At-Home Computer Requirements

Laptop or desktop computer (Windows or macOS)
Reliable internet connection
Ability to create free accounts for AI tools (Google, OpenAI, Anthropic)

Please note that content is subject to change or modification based on the unique needs of the track participants in attendance.  

Agenda

Feb. 13 — AI Fundamentals & the Changing Classroom:

What AI is (and isn’t)
Ethical and responsible classroom use
Overview of LLMs and AI ecosystems

Feb. 20 — LLMs as Teaching Assistants:

ChatGPT, Claude, Gemini, Grok
Projects, Gems, Games, and lightweight models (NanoBanana)
Prompting for thinking, not answers

Feb. 27 — AI Tools That Save Time & Increase Clarity:

NotebookLM for study guides and podcasts
Scribe & Guidde for walkthroughs and microlearning
Reducing student confusion and faculty workload

Mar. 6 — Authentic Assessment in an AI World:

Redesigning assessments AI can’t replace
Video, teach-backs, reflections, and AI critique assignments
Rubrics and mastery-based evaluation

Mar. 13 — Curriculum Reimagined & Capstone Build:

Embedding AI literacy into curriculum
Faculty sustainability workflows
Capstone showcase: redesigned module or course artifact

Instructor

Nancy Miller Nancy Miller is an award-winning IT professor, AI-powered teaching leader, and Cisco Networking expert with over 25 years of experience in network management, cybersecurity, and instructional design. A national presenter and certified AI educator, she specializes in helping colleges integrate modern tools like Microsoft PowerApps to streamline workflows, enhance student engagement, and build career-ready skills. Nancy’s work spans AI literacy, automation, and hands-on IT curriculum design, empowering faculty and students across North Carolina and beyond. Her workshops blend real-world practice with practical innovation, giving participants the confidence to design smarter, more efficient learning and administrative solutions.

SESSION I: Data Analytics using Python, External APIs & AI (INTERMEDIATE)

Fridays, February 13 – March 13, 1 PM – 5 PM ET

Description

This track is a five-day, hands-on series for community college instructors who want to integrate modern Python, API-driven data, and AI tools into their existing courses. Participants will work through instructor-provided labs in Google Colab, Excel (via Python add-ins), Streamlit, and Power BI that they can take back and reuse or lightly adapt in their own classes. The emphasis is on practical, low-friction activities that expose students to real-world data, basic AI capabilities, and interactive analytics, while minimizing prep time and infrastructure needs for both faculty and students.

NOTE: This track closely mirrors the content covered in the Summer III 2025 Working Connections offering, “Advanced Data Analytics – Machine Learning with Python, Power BI & Excel.” Participants who completed that 2025 track are encouraged to select a different option, as the material will be largely overlapping.

Certification Prep

While not tied to a single certification, this track can support preparation for:

Entry-level data analytics and BI certifications that expect basic Python, API, and dashboarding familiarity (e.g., vendor-neutral data analytics microcredentials)
Microsoft Power BI–related skills validation where Python visuals and scripting are considered a plus
Institutional or system-level “AI literacy” or “data literacy” badges that emphasize applied tools in common teaching environments

Objectives

By the end of this track, participants will be able to:

Design Colab-based labs that use Python and external APIs (e.g., finance and crypto data) to support course outcomes in computing and data-related classes.
Implement Python-powered activities in Excel, Streamlit, and Power BI that introduce students to AI-assisted analysis and visualization.
Adapt provided lab templates and code samples to align with their own course learning objectives, student populations, and institutional constraints.
Evaluate which tools (Colab, Excel + Python, Streamlit, Power BI) are most appropriate for different course levels and assignments, then integrate them into syllabi and weekly teaching plans.

Pre-requisites

Attendees should:

Have basic familiarity with Python (variables, functions, simple data structures) or equivalent experience in another programming language.
Be comfortable using spreadsheets (Excel or similar) and basic file management in a web browser.
Have experience teaching (or planning to teach) courses in CIS, CS, data analytics, business analytics, or related fields.
No prior experience with Streamlit, external APIs, or Power BI is required.

Required Textbook

No required textbook. All materials (notebooks, slides, lab sheets, and example code) will be provided digitally.

Suggested/optional Textbook

An introductory Python for data analysis book or OER resource
Online vendor documentation for Power BI, Streamlit, and Python-in-Excel tools. These are recommended for deeper follow-up but not needed to complete the track.

At-Home Computer Requirements

A laptop or desktop capable of running a modern web browser (Chrome/Edge/Firefox)
Reliable internet access to use cloud-based tools (Google Colab, Streamlit Community Cloud, Power BI service)
A working installation of Microsoft Excel and Power BI Desktop (Windows strongly preferred for Power BI compatibility)
Permission to install add-ins or lightweight tools as needed (for Python in Excel scenarios)

Please note that content is subject to change or modification based on the unique needs of the track participants in attendance.  

Agenda

Feb. 13 – Google Colab & External APIs

Class Introductions and goal setting for integrating these tools into community college courses
Introduction to Google Colab and Python refresher
Using yfinance and cryptocurrency/market APIs (e.g., Coingecko) in Colab
Exploring datasets from Kaggle in Colab and framing them as student labs

Feb. 20 – Python in Excel (Boardflare or similar)

Installing and configuring a Python-in-Excel add-in
“Hello World” in Excel with Python functions
Using functions such as WORDCLOUD, EXCHANGE_RATE, and VADER_SENTIMENT to create spreadsheet-based labs
Explore how to obtain a free Mistral AI API key and call its LLM from Python in Excel to power AI-assisted spreadsheet activities.
Exploring AI helper functions (e.g., AI_CHOICE, AI_ASK, AI_LIST, AI_TABLE)
Open exploration and adaptation time for participants to prototype their own Excel labs

Feb. 27 – Streamlit I

Introduction to the Streamlit framework and the framework’s app-building mindset
“Hello World” Streamlit app from a Colab/Notebook workflow
Deploying Streamlit apps to a hosted environment (e.g., Community Cloud)
Creating a simple multipage app suitable for a student project
Exploration time to refine or extend sample apps

Mar. 6 – Streamlit II (Chatbots & AI Apps)

Recap of Streamlit basics and architecture
Building chatbot-style apps: Echo bot, Chat bot, and AI-augmented chat bot.
Discussing instructional uses: demos, guided labs, and capstone mini-projects
Work time to customize chatbot labs for participants’ own classes

Mar. 13 – Power BI & Python

Power BI quick start: importing data and building basic reports
Importing data using Python scripts within Power BI
Transforming data using Python in Power Query or Python visuals
Visualizing data with Python libraries (e.g., Seaborn) inside Power BI
Designing a complete Power BI + Python lab that students can perform in one or two class sessions

Instructor

Chris Santo is residential faculty in the Mathematics, Computer Science, and Engineering Division at Scottsdale Community College, part of the Maricopa Community College District, where he teaches courses in Java programming, data structures, data analytics, Tableau, Power BI, and Python for data analytics. He has taught mathematics, computer science, and computer information systems courses across multiple Maricopa colleges, including Glendale, Paradise Valley, Scottsdale, Rio Salado, and Mesa Community College, since 2008.

Chris has more than 30 years of experience in the information technology sector, spanning roles in computer lab management, technical support, systems administration, application development, database programming, systems integration, project management, IT management, and reliability-focused consulting. His work bridges industry and academia, using data analytics and advanced IT solutions to improve enterprise operations while mentoring the next generation of technologists.

He also teaches in the Master of Information Studies program at Trine University and is a Certified Reliability Leader (CRL). Chris is currently pursuing a Ph.D. in Computer Science with a concentration in Artificial Intelligence at the University of Southern Mississippi, and holds an MS in Computer Science (Cybersecurity and Big Data, with distinction) and an MBA from Arizona State University, an MS in Information Sciences and Telecommunications from the University of Pittsburgh, and a BS in Mathematics and Computer Science from PennWest California.

SESSION I: Python in the Data Analysis Workflow (INTERMEDIATE) - CANCELLED

Fridays, February 13 – March 13, 1 PM – 5 PM ET

Description

Learn how to use Python’s advanced data structures; explore Python libraries useful for Data Analysis; acquire and clean data with Python tools – LLMs need good, clean data!

Certification Prep

N/A.

Objectives

Students will be able to:

Write Python applications using advanced data structures: lists, dictionaries, sets, and tuples
Write Python applications which use data analytics libraries such as Numpy, Pandas & Polars, Matplotlib, Seaborn, Plotly, SciPy, Scikit-learn, Requests
Write a Python application to scrape the web using Scrapy, BeautifulSoup
Write a Python application to transcribe and translate audio to text

Pre-requisites

A familiarity with introductory-level Python; basic syntax and simple program structure. Students should create a Google account if they do not already have one. Students should create a GitHub account if they do not already have one.

Required Textbook

None.

At-Home Computer Requirements

PC or MAC, fast internet, webcam & microphone.

Please note that content is subject to change or modification based on the unique needs of the track participants in attendance.  

Agenda

Feb. 13:

Lists
Stacks
Queues
List Comprehensions

Feb. 20:

Tuples and Sequences
Sets
Dictionaries

Feb. 27:

Fetching data: scrape the web with BeautifulSoup and Scrapy
Use Requests to read public data with APIs

Mar. 6:

Clean & visualize data: Numpy
Pandas & Polars
Matplotlib
Seaborn

Mar. 13:

Transcribe & Translate audio data: openai-whisper

Instructors

Pamela Brauda is a faculty member in the School of Technology at Florida State College at Jacksonville, where she teaches courses in programming, networking, and data science. Pamela is a co-designer of the A.S. in Data Science Technology program at FSCJ, co-principal investigator for the DataTEC project (NSF Grant #1902524 “Meeting Industry Needs through a Two-Year Data Science Technician Education Program”), a faculty co-advisor for the FSCJ STARS Computing Corps, and the proud owner of an autographed copy of “R for Data Science” by Hadley Wickham. Before teaching at FSCJ, Pamela worked as a Metadata Analyst with the Florida Department of Law Enforcement, taught programming and software development at the University of North Florida, created and operated several small businesses, and taught high school mathematics. She graduated from the University of Georgia with a B.S. and from the University of North Florida with an M.S. in Computer Science.

David Singletary is a faculty member in the School of Technology at Florida State College at Jacksonville. He teaches courses in software development, data science, and AI. In a previous life David was employed as a software engineer at Cisco and various startup companies in Silicon Valley. David graduated from the University of Central Florida with a B.S. and from the University of Colorado with an M.S. in Computer Science.

SESSION II: AI Fundamentals to Classroom Transformation: Tools, Assessment, and Curriculum Reimagined (INTRO)

Fridays, March 20 – April 17, 1 PM – 5 PM ET

Description

Certification Prep

No formal certification
Supports institutional AI literacy goals, faculty professional development hours, and workforce-aligned AI skill development

Objectives

By the end of this track, participants will be able to:

Explain how generative AI is transforming teaching, learning, and curriculum across disciplines.
Apply LLMs and AI tools to course design, content creation, and student engagement.
Design authentic, AI-aware assessments that emphasize critical thinking and process over product.
Evaluate AI tools and workflows to determine appropriate, ethical classroom use.

Pre-requisites

None, curiosity and willingness to explore new instructional approaches recommended.

Required Textbook

None.

Suggested/optional Textbook

The instructor may provide digital resources, examples, and templates. The instructor may provide optional open-access articles and tool documentation.

At-Home Computer Requirements

Laptop or desktop computer (Windows or macOS)
Reliable internet connection
Ability to create free accounts for AI tools (Google, OpenAI, Anthropic)

Please note that content is subject to change or modification based on the unique needs of the track participants in attendance.  

Agenda

Mar. 20 — AI Fundamentals & the Changing Classroom:

What AI is (and isn’t)
Ethical and responsible classroom use
Overview of LLMs and AI ecosystems

Mar. 27 — LLMs as Teaching Assistants:

ChatGPT, Claude, Gemini, Grok
Projects, Gems, Games, and lightweight models (NanoBanana)
Prompting for thinking, not answers

Apr. 3 — AI Tools That Save Time & Increase Clarity:

NotebookLM for study guides and podcasts
Scribe & Guidde for walkthroughs and microlearning
Reducing student confusion and faculty workload

Apr. 10 — Authentic Assessment in an AI World:

Redesigning assessments AI can’t replace
Video, teach-backs, reflections, and AI critique assignments
Rubrics and mastery-based evaluation

Apr. 17 — Curriculum Reimagined & Capstone Build:

Embedding AI literacy into curriculum
Faculty sustainability workflows
Capstone showcase: redesigned module or course artifact

Instructor

SESSION II: Cloud Computing Fundamentals (Cloud+) (INTRO)

Fridays, March 20 – April 17, 1 PM – 5 PM ET

Description

This session introduces faculty to CompTIA Cloud+ certification content and prepares instructors to teach vendor-neutral cloud computing concepts in their courses. Participants will explore cloud architecture, security, deployment, and operations while gaining familiarity with the exam objectives and effective teaching strategies for hands-on cloud labs. Faculty will leave equipped to develop curriculum that prepares students for cloud computing careers and industry certification.

Certification Prep

Cloud+ CV0-003

Objectives

Explain the CompTIA Cloud+ CV0-003 certification exam structure, including the five domains (Cloud Architecture and Design, Security, Deployment, Operations and Support, and Troubleshooting) and their respective weightings in the exam.
Evaluate and apply standard cloud methodologies to implement, maintain, and deliver cloud technologies including network, storage, and virtualization solutions in instructional settings.
Assess and integrate IT security principles and industry best practices into cloud implementation curriculum and hands-on learning activities.

Pre-requisites

Introductory computer science courses or experience.

Required Textbook

None.

Suggested/optional Textbook

CompTIA Cloud+ Guide to Cloud Computing, 2nd Edition, Cengage MindTap, 9798214027531 (can request evaluation access through Cengage)

At-Home Computer Requirements

Instructors will want to request instructor evaluation access to the CompTIA Cloud+ Guide to Cloud Computing eBook with MindTap, 2nd edition from Cengage.

Please note that content is subject to change or modification based on the unique needs of the track participants in attendance.  

Agenda

Mar. 20:

Introductions
Material Review
Module 1: Introduction to Cloud Computing
Module 2: Virtual Hardware

Mar. 27:

Module 3: Migration to the Cloud
Module 4: Cloud Networking

Apr. 3:

Module 5: Cloud Connectivity and Troubleshooting
Module 6: Securing Cloud Resources

Apr. 10:

Module 7: Identity and Access management
Module 8: Cloud Storage

Apr. 17:

Module 9: Managing Cloud Performance
Module 10: Cloud Automation, and final assessment

Instructor

Dr. Stephanie Wascher is an experienced cybersecurity and IT faculty member with over 20 years of experience teaching networking, cybersecurity, virtualization, and cloud-related technologies in higher education and workforce training environments. She currently serves as Academic Chair and Professor of Computer and Information Systems, where she leads curriculum development, manages secure lab environments, and delivers instruction aligned to industry certifications including CompTIA, Cisco, and VMware. Her background includes extensive hands-on experience with virtualization, Windows Server, networking, and security infrastructures that directly support Cloud+ concepts. Stephanie is also actively engaged in workforce alignment initiatives, grant-funded programs, and industry partnerships, ensuring training remains relevant, practical, and certification-focused.

SESSION II: Introduction to Quantum Computing (INTRO)

Fridays, March 20 – April 17, 1 PM – 5 PM ET

Description

An introductory, hands-on workshop that equips participants with practical models and Python skills to design, simulate, analyze, and execute quantum circuits across simulators and cloud-based quantum hardware.

Certification Prep

AWS Braket–related skill badges

Objectives

Explain basic quantum computing concepts and how they differ from classical computing.
Build and run simple quantum circuits using Python tools.
Interpret quantum results from simulations and hardware runs.
Compare ideal, noisy, and real-hardware execution outcomes.

Pre-requisites

Basic programming experience in any language
Introductory familiarity with Python syntax and notebooks
Comfort working with simple mathematical and logical concepts

Required Textbook

None.

Suggested/optional Textbook

Building Quantum Software With Python: A Developer’s Guide (Manning Publications, 2025).

At-Home Computer Requirements

Laptop or desktop (Windows, macOS, or Linux) capable of running a modern web browser
Reliable internet connection to access Google Colab and cloud-based quantum platforms
Google account for using Colab notebooks

Please note that content is subject to change or modification based on the unique needs of the track participants in attendance.  

Agenda

Mar. 20 – Foundations of Quantum Computing:

Welcome and Introductions
Classical vs. quantum computation
Qubits, superposition, and measurement
Hands-on exercise: Visualizing single-qubit states in Colab
Hands-on exercise: Measuring qubits and interpreting results

Mar. 27 – Quantum Gates and Circuits:

Review of basic quantum gates
Multi-qubit systems and entanglement
Circuit-based quantum computing model
Hands-on exercise: Building simple quantum circuits
Hands-on exercise: Simulating circuits and analyzing outcomes

Apr. 3 – Quantum Programming & Hybrid Workflows:

Python quantum SDK overview
Parameterized circuits and reuse
Hybrid quantum–classical workflows
Hands-on exercise: Parameter sweeps in quantum circuits
Hands-on exercise: Implementing a simple hybrid loop

Apr. 10 – Noise, Errors, and Result Analysis:

Sources of noise in quantum systems
Shot-based execution and statistics
Introductory error mitigation concepts
Hands-on exercise: Ideal vs. noisy simulation comparison
Hands-on exercise: Debugging and validating results

Apr. 17 – Cloud Platforms & Hardware Execution:

Overview of major cloud-based quantum platforms
Simulators vs. real quantum hardware
AWS Braket execution workflow
Hands-on exercise: Preparing and validating a circuit for cloud execution
Hands-on exercise: Executing on a managed QPU and comparing results
Wrap-up