Fall 2026 Working Connections

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.
• 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.  

SESSION I: Cloud+ (INTRO)

Fridays, September 11 – October 9, 1:30 PM – 5:30 PM ET 

Description

This professional development series introduces IT 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 CV0-004 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.

NOTE: This track is a repeat of Cloud+ from Spring 2026. Participants who previously completed this course are not eligible to register for this track again.

Certification Prep

CompTIA Cloud+ (CVO-004)

Objectives

• Execute cloud migration strategies by analyzing system requirements, selecting migration methodologies, and transitioning workloads to cloud environments with minimal disruption.
• Manage and maintain cloud operations through backup/recovery procedures, resource monitoring, capacity optimization based on SLA requirements, and automation techniques.
• Troubleshoot cloud computing issues using systematic methodologies to diagnose and resolve deployment, connectivity, security, capacity, and automation problems.
• Analyze and compare cloud service and deployment models to recommend vendor-neutral solutions aligned with business requirements and technical constraints.

Pre-requisites

Introductory computer and networking knowledge.

Required Textbook

None.

Suggested/optional Textbook

CompTIA Cloud+ Guide to Cloud Computing, 3rd Edition by Jill West, MindTap edition, ISBN: 9798214409900.

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

Sept. 11:

• Introduction to Cloud: Characteristics, Deployment Models, Service Models, Cloud Services, and Troubleshooting
• Cloud Compute: Virtualization Technologies, VMs in the Cloud, VM Alternatives

Sept. 18:

• Cloud System Design: Cloud Migration, Cloud-Native Design, System Development, Planning
• Cloud Networking: Networking Concepts in the Cloud, IP Addressing

Sept. 25:

• Cloud Connectivity: Hybrid Cloud, Multi-Cloud Networking, Network Services, Troubleshooting
• Securing Cloud Resources: Cloud security, VM Security, Compute Security, Data Security

Oct. 2:

• Identity and Access Management: Cloud Accounts, Authentication, Authorization, IAM
• Cloud Storage and Databases: Storage Types, Configuration, Database Services, Backups, Security

Oct. 9:

• Observability and Monitoring: Resources, Events, Logs, Traces, Monitoring Services, Troubleshooting
• Cloud Automation: Automating Code Pipelines, Cloud Maintenance, Troubleshooting

Instructors

Stephanie Wascher is an experienced educator and cybersecurity professional with a strong background in computer networking, information security, and instructional technology. She currently serves as the Computer Information Systems Academic Chair and Professor at Rock Valley College, where she leads programs in networking and cybersecurity. With extensive experience in both higher education and high school instruction, she has been instrumental in developing comprehensive curricula and aligning courses with industry standards. Stephanie recently earned her Doctorate in Information Technology, specializing in Information Assurance and Cybersecurity at Capella University.

SESSION I: Introduction to Containers and Microservices (INTERMEDIATE)

Fridays, September 11 – October 9, 1:30 PM – 5:30 PM ET 

Description

Hands-on exploration of the skills needed to create, deploy, and manage containers. Attendees learn how microservices architectures differ from monolithic designs, then work through the container lifecycle—from CLI basics and image management to Containerfiles, storage, networking, logging, security, and orchestration. Instruction combines lectures, open-book quizzes, and guided labs. A running boot-camp scenario walks through converting a monolithic Python/Flask application into containerized microservices, applying each new concept at every stage. The track emphasizes container technology and infrastructure operations (not application development inside containers), using Podman as the primary engine with Docker-compatible workflows.

NOTE: This track was delivered in person at NITIC Working Connections in July 2024 (Collin College, Frisco). The core curriculum—microservices concepts, Podman CLI, images, Containerfiles, storage, networking, logging, security, orchestration, and the monolith-to-microservices boot camp—is largely the same. Participants who previously completed this course are not eligible to register for this track again.

Certification Prep 

Foundational preparation for Red Hat Certified Specialist in Containers (EX180) and introductory concepts relevant to Kubernetes certifications (e.g., CKA/CKAD). Also supports Linux and container skills useful for Docker-related credentials and Red Hat OpenShift learning paths.

Objectives 

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

• Explain differences between monolithic and microservice architectures and describe how containers provide isolation, portability, and efficient resource use.
• Apply Podman CLI commands to run, inspect, and manage container lifecycles, images, and basic connectivity.
• Build container images from Containerfiles and *configure* persistent storage, networking, logging, and foundational security practices for containerized workloads.
• Compose multi-container applications locally and *describe* the role of orchestration at scale.

Pre-requisites 

Basic familiarity with the Linux command line. Attendees should be comfortable opening a terminal, navigating directories, and editing text files. Prior exposure to networking concepts (IP addresses, ports, DNS) is helpful but not required. No prior container, microservices, or IDE remote-development experience is assumed—Visual Studio Code/Cursor and SSH basics are taught on Day 1.

Required Textbook

Containers and MicroServices by Professor Juan Medina (provided as a PDF by the instructor).

Suggested/optional Textbook  

• Official Podman documentation https://docs.podman.io[docs.podman.io
• containers-labs repository https://github.com/containersorg/containers-labs 

At-Home Computer Requirements

• Reliable internet connection 
• A laptop (PC or Mac) with the following minimum specs:
  • 8 GB of RAM (16 GB or more is highly recommended)
  • Reliable internet connection for Zoom sessions and Tailscale VPN access
  • Windows, macOS, Linux, or ChromeOS host operating system
• Software and accounts required:
  • Tailscale account and client (You will receive an invite from the instructor before the course)
  • Visual Studio Code, with the following extensions installed on the host:
  • Remote-SSH (required—for connecting to the Fedora Server VM)
  • Podman or Docker (container engine integration)
  • ssh-fs (optional—browse remote files)
  • YAML and JSON (for Containerfiles and Compose manifests)
  • Git
• GitHub account

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

Agenda

Sept. 11: 

• Welcome, course materials overview, and hosted lab connectivity verification (Tailscale + SSH + VS Code)
• Visual Studio Code basics: Remote-SSH connection, integrated terminal, GitHub workflows, and container extensions
• Understanding Microservices (monolithic vs. microservices trade-offs, container concepts)
• CLI Commands (Podman run, inspect, lifecycle management)

Sept. 18:

• Images (layers, registries, building and tagging)
• Containerfile (authoring, building, and running custom images)

Sept. 25: 

• Storage (volumes, bind mounts, persistence)
• Networking (bridge networks, port mapping, container-to-container communication)

Oct. 2: 

• Logging and Monitoring (stdout/stderr, health checks, observability basics)
• Security (rootless containers, isolation, vulnerability scanning with Trivy)

Oct. 9: 

• Orchestration (Docker Compose, multi-container stacks, introductory Kubernetes concepts)
• Assignments, grading overview, and open Q&A

Instructor

Juan Medina — Adjunct Professor at Collin College (since 2019); Ansible Specialist Adoption Architect at Red Hat. Computer Systems Engineer with certifications in Linux, Ansible, Containers, AIX, Solaris, and PMP. Former Infrastructure Architect at IBM, Linux/Unix Sr. Advisor at DELL, and Sr. Linux/Unix System Administrator at Perot Systems. Daily work spans Ansible, Linux, Kubernetes, containers, Python, OpenShift, cloud computing, and automation. 

LinkedIn: https://www.linkedin.com/in/jmedinar/

SESSION I: Teaching the Stack: Full-Stack Python for Educators (INTERMEDIATE)

Fridays, September 11 – October 9, 1:30 PM – 5:30 PM ET 

Description

This hands-on seminar provides college instructors with the pedagogical and technical framework necessary to teach full-stack web development using Python. Over five intensive sessions, participants will build a functional web application from the ground up, utilizing Flask for routing and Peewee for data modeling to demystify the core mechanics of the HTTP request-response cycle and database persistence. By blending live-coding exercises with discussions on instructional scaffolding and common student misconceptions, this course empowers educators to translate professional development practices into effective, project-based classroom experiences, ensuring they return to their institutions with both a robust technical foundation and a refined strategy for teaching modern web architecture.

Certification Prep 

N/A.

Objectives

• Analyze the Request-Response Lifecycle by mapping the interaction between HTTP protocols, routing, and server-side processing to demystify how a web application delivers content.
• Design persistent data structures using an ORM to translate complex relational database schemas into functional, object-oriented Python models.
• Construct dynamic CRUD-capable applications.
• Develop instructional scaffolds that decompose abstract full-stack concepts into manageable, project-based learning modules designed for introductory-level students.

Pre-requisites

To ensure all participants feel prepared, resources will be provided one month prior to the start of the seminar to help bridge any gaps in prerequisite knowledge, as we are committed to making this course accessible to the widest possible audience of educators.

• Elementary Python: A functional understanding of variables, control structures (loops, conditionals), and lists.
• HTML & JavaScript: The ability to structure web content using standard HTML tags and implement basic client-side interactivity with JavaScript.
• SQL Basics: A foundational grasp of relational database concepts, including the purpose of tables, rows, columns, and primary keys, as well as the ability to perform basic SELECT, INSERT, and DELETE queries.

Required Textbook

N/A.

Suggested/optional Textbook

Flask Web Development with Python (ISBN: 979-8199160155)

At-Home Computer Requirements

To successfully complete this course, you will need a reliable laptop or desktop computer. Please ensure your machine meets the following criteria: 

• Operating System: Windows (10 or 11), macOS (latest 3 versions recommended), or a modern Linux distribution. 
• Hardware: 
  • Processor: Multi-core processor (Intel Core i5/AMD Ryzen 5 or better recommended). 
  • RAM: 8 GB minimum (16 GB highly recommended for smoother performance). 
  • Storage: 50 GB+ free space on an SSD (Solid State Drive) is preferred for faster development. 
• Recommended Setup: It is highly ideal to have a second monitor, or a tablet configured to serve as a second display, to easily view your code editor and web browser side-by-side. 
• Administrative Access (Required): You must have full administrative privileges on your computer. You will need the ability to: 
  • Install software, development tools, and programming languages (e.g., Python, VS Code, Git). 
  • Configure system settings and firewall policies to allow your local web development server to run and communicate with your browser. 
• Connectivity: A reliable high-speed internet connection is necessary for downloading course materials and accessing online development resources. 

Important Note: Tablets (like iPads or Android tablets) and Chromebooks are generally not sufficient for this course, as they typically restrict the ability to install local development software and manage system-level configurations. 

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

Agenda 

Sept. 11: Web Architecture 

• Analyze the HTTP Request-Response cycle and configure the Flask development environment.

Sept. 18: Frontend (Templates) 

• Construct dynamic web pages using Jinja2 templates and integrate frontend HTML/JS logic.

Sept. 25: Backend (Data Persistence) 

• Design relational database models using on ORM and map them to Python classes.  

Oct. 2: Full-Stack CRUD 

• Develop complete CRUD (Create, Read, Update, Delete) workflows within a unified application.

Oct. 9: Project Synthesis & Instructional Design 

• Complete comprehensive projects. Design effective lessons, activities, and assessments for introductory students

Instructor

Dr. Matt Green is the program lead for the Web and Software program and has been an instructor at Waukesha County Technical College for 17 years. He specializes in web development, Python, Microsoft stack, AI Literacy, AI Ethics, software architecture, business process engineering, and automation with scripts. Before his academic career, he worked as a software developer, project coordinator, and data conversions analyst, followed by a decade of organizational development management in the banking IT segment.

At WCTC, Dr. Green serves as the instructor for the global IT experience. Additionally, he teaches Python at an all-men’s high school and provides media development instruction to a team in Bangladesh.

Dr. Green holds a Doctorate in Ministry with a focus in Education from Northwind Theological Seminary, a Master’s in Educational Technology and Curriculum, and an undergraduate degree in Business and Math Computing from Eastern Illinois University. He has also completed a post-graduate certificate in Artificial Intelligence and Machine Learning at Marquette University.

SESSION II: AI + Cyber: AI Powered Cybersecurity (INTERMEDIATE)

Fridays, October 16 – November 20, 1:30 PM – 5:30 PM ET 

Description

This professional development course provides faculty with practical strategies for integrating artificial intelligence into cybersecurity instruction within the next 12 months. Participants will examine how AI is changing cybersecurity work, explore the risks associated with AI-enabled systems, and evaluate tools that can enhance student learning while keeping cybersecurity and technology programs aligned with evolving threats and workforce demands.

Participants will work with local large language models using Ollama, retrieval-augmented generation and research platforms, AI-assisted cybersecurity tools, and secure coding assistants. These technologies will be used alongside established cybersecurity frameworks, defensive tools, vulnerability-assessment methods, and instructional design practices.

The course is organized around a continuing scenario involving a fictional technical college responding to AI-related instructional and cybersecurity challenges. Across five intensive Friday afternoon sessions, participants will complete guided demonstrations, controlled cyber-range labs, instructor-provided scenario injects, curriculum-development activities, and peer reviews. Each participant will redesign an existing instructional activity and develop a practical 12-month implementation roadmap for their home institution.

The course addresses three connected areas of practice:

• Securing AI applications, models, data, retrieval pipelines, and tool integrations
• Using AI to support cybersecurity research, analysis, defense, and secure development
• Recognizing and responding to adversaries who use AI to increase the speed, scale, or effectiveness of cyberattacks

Commercial platforms may be demonstrated, but the course emphasizes transferable capabilities rather than dependence on a particular vendor. Core activities will include local, open, preserved-output, or institutionally approved alternatives whenever possible.

NOTE: This track is a repeat from Summer 2026 in Ohio. Participants who previously completed this course are not eligible to register for this track again.

Certification Prep 

N/A.

Objectives 

• Analyze emerging AI technologies, including local language models, retrieval-augmented generation frameworks, research platforms, coding assistants, and agentic cybersecurity tools, to determine their relevance, risks, and potential application within existing academic programs.
• Evaluate cybersecurity instructional tools, digital platforms, and lab environments using criteria such as learning alignment, technical accuracy, privacy, security, accessibility, cost, licensing, hardware requirements, and workforce relevance.
• Use technical evidence and authoritative sources to validate AI-generated claims, identify hallucinations or unsafe recommendations, and communicate appropriate levels of confidence and uncertainty.
• Design or revise classroom activities, lesson plans, labs, assessments, or project-based learning experiences that integrate AI with cybersecurity tools and industry practices while preserving student accountability and measurable skill development.
• Develop practical syllabus guardrails addressing acceptable AI use, disclosure, attribution, academic integrity, data protection, and required evidence of student work.
• Implement a realistic 12-month action plan for introducing AI-enabled cybersecurity tools, resources, and labs at the participant’s home institution.

Pre-requisites 

• Be active faculty members or instructors teaching cybersecurity, information technology, computer science, or a related technology discipline.
• Have the ability to modify or influence course content within the next 12 months.
• Bring an existing course, lesson, lab, assignment, or instructional area that can be revised during the course.
• Possess basic digital literacy and familiarity with common instructional technologies and learning management systems.
• Have a foundational understanding of cybersecurity concepts such as networking, system security, application security, risk management, vulnerability assessment, or security operations.
• Be comfortable reviewing basic terminal output, logs, scan results, or source code.
• Demonstrate a willingness to evaluate new technologies critically and translate course resources into practical student-learning experiences.
• Advanced programming, machine-learning development, and previous experience running local language models are not required.

Required Textbook

None. 

Suggested/optional Textbook  

None. Current standards, frameworks, advisories, documentation, and instructor-provided resources will be used instead of a fixed textbook.

At-Home Computer Requirements

• Laptop or desktop computer running Windows, macOS, or Linux
• Modern AMD Ryzen 5, Intel Core i5, Apple Silicon processor, or equivalent
• Minimum 16 GB RAM
• Minimum 100 GB available disk space
• Reliable broadband internet connection
• Administrative permission to install approved software
• Hardware-virtualization support when using the local virtual appliance
• GPU optional

The primary virtual appliance will target supported x86-64 virtualization environments. Participants using Apple Silicon or another ARM64 system will receive an architecture-appropriate appliance, container-based alternative, or hosted environment designed to provide equivalent lab outcomes.

If you do not have access to a machine that can run a VM in VirtualBox, reach out to the instructor to ensure you have access to a cloud-provisioned environment.

Participants using institution-managed computers should verify software-installation, virtualization, and network-access permissions before the course begins.

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

Agenda

Oct 16: Local AI, Privacy, and Trust

• Participants deconstruct common AI myths and examine what generative AI can and cannot reliably accomplish in cybersecurity. They use Ollama or an equivalent local platform to compare local and hosted execution, examine privacy and data-handling implications, and practice the CO-STAR prompt-engineering framework.
• In the first scenario mission, participants must determine whether Northbridge Technical College should permit faculty to process student or institutional information with an AI system. The accompanying lab requires participants to classify the data, select an appropriate processing environment, construct a structured prompt, and validate the resulting output.

Oct 30: Curriculum Injection and Academic Integrity

• Participants complete a hands-on curriculum-injection sprint using an existing lesson, lab, assignment, or course module from their own institution. They examine how generative AI may allow students to bypass intended learning and redesign the activity around authentic performance, decision-making, process evidence, technical validation, and student accountability.
• The Northbridge scenario introduces a legacy cybersecurity assignment that can be completed by AI without demonstrating the intended competency. Participants revise the assignment and draft practical syllabus guardrails covering acceptable AI use, attribution, disclosure, prohibited data, evidence retention, accessibility, and academic integrity.

Nov 6: AI-Assisted Research, RAG, and Threat Intelligence

• Participants compare open-web AI research with source-bounded analysis. Perplexity AI may be used to demonstrate rapid web discovery, while NotebookLM or an approved equivalent is used to analyze a curated collection of standards, advisories, threat reports, and institutional evidence.
• The lab introduces conflicting sources, circular citations, unsupported claims, and potentially malicious instructions embedded within source material. Participants create a claim ledger, verify consequential citations, identify possible prompt injection, and produce a confidence-qualified threat assessment using authoritative resources such as NIST, MITRE ATT&CK, MITRE ATLAS, CISA, and relevant industry reporting.

Nov 13: AI-Assisted Cyber Operations and Authorized Security Assessment

• Participants examine how AI co-pilots can support—but should not replace—human interpretation of vulnerability scans, terminal output, network evidence, and security telemetry. Activities emphasize scope control, false-positive analysis, authorization, human approval, and verification of AI-recommended actions.
• Within the isolated AI + Cyber Range, participants investigate a Northbridge system using approved scan results and lab evidence. Instructor-controlled injects introduce misleading banners, conflicting findings, incomplete telemetry, and unsafe AI recommendations. Participants then redesign a legacy penetration-testing or vulnerability-assessment lab so that students are evaluated on evidence, interpretation, decision-making, and responsible tool use rather than command execution alone.

Nov 20: Secure Code Review and Institutional Implementation

• Participants use Claude AI, another approved coding assistant, or preserved coding-agent output to review a vulnerable Northbridge application. They compare AI findings with static-analysis results, distinguish confirmed defects from false positives, evaluate proposed patches, identify incomplete fixes, and develop appropriate regression tests.
• The course concludes with participants finalizing and peer-reviewing their 12-month implementation roadmaps. Each roadmap identifies proposed curriculum changes, required resources, institutional stakeholders, policy or approval needs, faculty-development requirements, anticipated risks, implementation milestones, and measures of success.

Instructor

Dr. Frazier Smith brings a unique combination of technical expertise and educational leadership to the field of information technology and artificial intelligence education. His research centers on the application of artificial intelligence in career development programs and evaluating the effectiveness of post-secondary career and technical education programs in preparing students for industry. 

With over a decade of experience spanning corporate technical training and higher education, including leadership roles at VMware and Broadcom, Dr. Smith has developed expertise in curriculum design, workforce development, and educational technology. His technical foundation spans cloud infrastructure (AWS, Azure, Google Cloud), virtualization technologies, cybersecurity, and network architecture, complemented by hands-on experience in data center design and systems administration.

As co-Principal Investigator on an NSF grant focused on “Generating Artificial Intelligence Talent” and a key contributor to developing one of the first associate-degree AI programs in North Carolina, he works at the crossroads of AI innovation and workforce readiness. His doctoral research and practical experience position him to address critical questions about how post-secondary institutions can best prepare students for careers in diverse technology fields.

SESSION II: Braided AI: Integrating Artificial Intelligence Across the Curriculum (INTRO)

Fridays, October 16 – November 20, 1:30 PM – 5:30 PM ET 

Description 

This track introduces community college faculty to braided AI: an approach that weaves AI literacy and use directly into existing, discipline-specific courses rather than isolating it in a new standalone AI elective. Across five sessions, participants protect the fundamental skills their courses already teach while identifying concrete, discipline-appropriate places to bring AI into assignments, pedagogy, and critical inquiry. The track is discipline-agnostic and welcomes faculty from STEM; humanities and social sciences; and CTE/allied health fields. Participants leave with a complete, classroom-ready braided course module and present it in a closing capstone showcase.

Certification Prep

Not applicable. This track does not map to an external certification; it produces a portfolio deliverable (a complete braided course module ready for classroom use) rather than certification credit.

Objectives

• Analyze the trade-offs between standalone AI courses and AI integrated across existing curricula.
• Distinguish protected, discipline-specific fundamental skills from skills that can reasonably be augmented by AI tools.
• Design a redesigned course assignment, an aligned rubric, and an AI-literacy activity for an existing course that preserve its core learning outcomes.
• Develop a 90-day implementation plan for introducing braided AI into their own course and department.

Pre-requisites

None. Open to faculty in any discipline and at any level of prior AI experience. Participants should bring one existing course syllabus or assignment they are willing to redesign during the track. No prior AI experience required; open to faculty at any stage of AI adoption. Faculty should be comfortable with standard classroom/course design work.

Required Textbook

None.

Suggested/optional Textbook

None.

At-Home Computer Requirements

Reliable internet connection; laptop or desktop computer with webcam and microphone; a modern web browser; free or institutional access to a general-purpose AI chatbot; a Google account (or equivalent) for shared documents and collaborative editing during breakout work.

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

Agenda

Oct 16 Session 1: Foundations — Why Braid, Not Bolt On:

• 1:30–2:30 Hour 1: The Bolt-On Problem (discussion)
• 2:30–3:30 Hour 2: AI Strand Mapping (hands-on)
• 3:30–3:40 Break
• 3:40–4:40 Hour 3: Cross-Disciplinary Case Panel (discussion)
• 4:40–5:30 Hour 4: Draft the Braid Map (hands-on)

Oct 30 Session 2: Disciplinary Fundamentals — Protecting the Core:

• 1:30–2:30 Hour 1: What Must Never Be Automated? (discussion)
• 2:30–3:30 Hour 2: AI Stress-Test of Core Skills (hands-on)
• 3:30–3:40 Break
• 3:40–4:40 Hour 3: Discipline Cluster Inventory Build (hands-on)
• 4:40–5:30 Hour 4: Cross-Cluster Report-Back (discussion)

Nov 6 Session 3: Weaving AI Into Assignments and Pedagogy:

• 1:30–2:30 Hour 1: Roles AI Can Play (discussion)
• 2:30–3:30 Hour 2: Assignment Redesign Lab (hands-on)
• 3:30–3:40 Break
• 3:40–4:40 Hour 3: Structured Peer Review (hands-on)
• 4:40–5:30 Hour 4: Assessment Rubric Co-Design (hands-on)

Nov 13 Session 4: Critical AI Literacy Within the Discipline:

• 1:30–2:30 Hour 1: Hunting Discipline-Specific Failure Modes (hands-on)
• 2:30–3:30 Hour 2: Academic Integrity Reframed (discussion)
• 3:30–3:40 Break
• 3:40–4:40 Hour 3: Equity and Access (discussion)
• 4:40–5:30 Hour 4: Build a Literacy Activity (hands-on)

Nov 20 Session 5: Capstone Showcase — Sharing and Sustaining the Braided Model:

• 1:30–2:30 Hour 1: Showcase Preparation (hands-on)
• 2:30–3:30 Hour 2: Capstone Showcase, Part 1 — whole-group presentations (guests welcome)
• 3:30–3:40 Break
• 3:40–4:40 Hour 3: Capstone Showcase, Part 2 — whole-group presentations and synthesis
• 4:40–5:30 Hour 4: Sustaining the Work — Scaling and 90-Day Planning

Instructor

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 II: Software Development with Agentic AI (INTRO)

Fridays, October 30 – November 20, 1:30 PM – 5:30 PM ET 

Description

Agentic AI is powering large-scale changes in the software development industry, and our students need to keep pace with the ever-changing workforce demands. Tools such as Anthropic’s Claude Code and OpenAI’s Codex power newfound velocity, but employers require more from their software development processes than mere vibe coding. This course introduces coding with agentic AI tools, demonstrates usage of Claude Code and Codex, walks through common software engineering approaches that can help ensure high-quality work, and demonstrates a range of classroom integrations for programming classes.

Certification Prep

N/A.

Objectives

• Install and configure various agentic AI applications that facilitate coding.
• Design and modify high-quality software applications using agentic AI coding practices.
• Develop practical classroom integrations of agentic AI coding tools and practices that embrace authentic assessment.

Pre-requisites 

While knowing a programming language such as Python is helpful, the course can be completed by participants from a broad range of technical backgrounds.

Required Textbook

 None.

Suggested/optional Textbook

N/A.

At-Home Computer Requirements

Webex, ability to install/configure basic software, and stable connectivity.

Subscription Requirement: Participants will need subscriptions to Anthropic Claude Code and OpenAI Codex (currently about $20/month each). At the end of the course, participants who meet the attendance requirements may submit a receipt for reimbursement of up to two months of each subscription for a one time payment (up to $80 total, excluding tax). NITIC cannot pay for subscriptions in advance and cannot reimburse sales tax. Any cancellations, renewals, or charges beyond the reimbursable period will be the participant’s responsibility. Setup instructions will be provided to accepted participants before the course begins.

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

Agenda

Oct 16: 

• NONE.

Oct 30: 

• Introduction to Agentic AI Coding

Nov 6: 

• Using Anthropic’s Claude Code & OpenAI’s Codex

Nov 13:

• Agentic AI Software Development Workflows  

Nov 20: 

• Agentic AI Classroom Integrations for Programming Classes

Instructor

Adam Richardson is a full-time Professor within the information technologies and computer science programs at Lansing Community College (LCC). Adam worked full-time in the IT industry for 16 years prior to transitioning to the academic world and possesses a Master of Information Technology degree from Virginia Tech that focused on software engineering and machine learning. Adam has earned over thirty technical certifications from CompTIA, AWS, Intel, Nvidia, IBM, and Stanford University. Adam currently teaches a broad range of courses at LCC, including introductory Python, AI/machine learning, cloud computing through AWS Academy, introductory computer science, and electronics. He currently acts as the advisor for the LCC Artificial Intelligence Club, and he is an elected senator to LCC’s Academic Senate who also serves on LCC’s Curriculum Committee. He serves as part of NITIC’s technology team focused on data analytics. Additionally, Adam consults through work in his company Adam on AI, LLC.