AI Workshops for Your Organization

Below is an AI Workshop Proposal titled "Six Weeks to AI Mastery: A Friendly, Hands-On Workshop: No AI or Coding Experience Needed — Just Bring Your Curiosity!" which we can deliver to your potential DECEP trainees in live and/or online (hybrid) mode in either Spanish or English or German.

Six Weeks to AI Mastery: A Friendly, Hands-On Workshop: No AI or Coding Experience Needed — Just Bring Your Curiosity!

Instructor: Walter Rodriguez, PhD, PE

Introduction and Audience

Artificial Intelligence (AI) is rapidly transforming education, business, and everyday life. Tools like ChatGPT have become phenomenally popular, reaching over a million users in mere days after its launch – yet many smart, motivated people still feel left behind. “Six Weeks to AI Mastery: A Friendly, Hands-On Workshop: No AI or Coding Experience Needed — Just Bring Your Curiosity!” is a six-week hybrid workshop designed to bridge that gap. Co-developed by Prof. Dr. Walter Rodriguez, PE, and his colleagues, for academic rigor, this program welcomes students, corporate professionals, and curious members of the general public. No prior AI or coding experience is assumed. Participants will start from the ground up (truly “Zero" AI knowledge) and progress to an expert-level practical understanding of modern AI, focusing on large language models (LLMs) and their applications. Along the way, learners earn stackable certificates for each module they complete, building up to a comprehensive certification that covers the entire series.

Key Features:

• Hybrid Format: Combines flexible online learning with weekly live sessions (join via video conference or in-person) for guided practice and Q&A.

• Hands-On Learning: Emphasizes practical activities – from crafting effective AI prompts to deploying a custom chatbot – to ensure skills aren’t just theoretical.

• Stackable Certifications: Four sequential module certificates (e.g., AI Foundations, Prompt Engineering, Local Deployment, Chatbot Building) serve as micro-credentials, which stack into a full course certification upon completion.

• Clarity and Accessibility: Concepts are explained in plain language with real-life examples. The math professor co-instructor will demystify the underlying logic, while the AI coach provides step-by-step technical guidance. This ensures a general audience can follow along, from non-technical educators to seasoned IT professionals looking to upskill.

Workshop Modules and Schedule

The workshop is structured into four cumulative modules delivered over six weeks. Each module corresponds to specific weeks and outcomes, with a certification earned upon completion:

• Module 1: AI Foundations (Week 1) – Certificate: AI Foundations. Introduces core AI concepts and the world of LLMs.

• Module 2: Prompt Engineering (Week 2) – Certificate: Prompt Engineering. Develops skills in crafting prompts and understanding token usage.

• Module 3: Local LLM Deployment (Week 3) – Certificate: Local Deployment. Explores running LLMs on personal devices with local tools.

• Module 4: Chatbot Building (Weeks 4–5) – Certificate: Chatbot Building. Guides participants in creating an AI chatbot with a web interface and LLM backend.

• Week 6: Final Showcase & Beyond – Integrates all modules in a capstone project presentation and discusses next steps (this final week crowns the "Zero to Expert" journey; participants who have earned all module certificates receive the Expert LLM Practitioner certificate for the full workshop).

Each week consists of self-paced online content followed by a live interactive session. Below is a detailed curriculum breakdown with weekly objectives, activities, and the split between asynchronous and live components:

Week 1: Introduction to AI and LLMs

Learning Objectives: By the end of Week 1, participants will be able to define what artificial intelligence is, recognize examples of AI in daily life, and explain in simple terms what large language models (LLMs) are and how they have emerged. We demystify key terms and set the stage for hands-on exploration of AI. Participants also gain familiarity with how transformative AI tools, such as ChatGPT, have become so prominent.

Online Content (Self-Paced): Short video lectures and readings introduce AI basics – from the history of AI to the difference between traditional algorithms and machine learning. A friendly primer on “What is an LLM?” explains that an LLM is a type of AI program that can understand and generate human language text. Real-world examples (e.g. Siri, Alexa, translation apps) illustrate AI’s ubiquity. One mini lecture focuses on the ChatGPT success story, highlighting how generative AI entered the mainstream. Interactive quizzes reinforce key concepts (e.g. “AI or Not?” scenarios where learners identify if a technology uses AI).

Live Session (1.5 hours, hybrid online/onsite): In the first live class, we foster a welcoming environment. Instructors and participants introduce themselves (icebreaker: share one way you’ve seen AI used, such as a recommendation on Netflix or an autocorrect feature). The math professor co-host will clarify any questions on AI fundamentals in layperson’s terms. We’ll demo an AI in action – for example, asking ChatGPT to answer a common question. This demo helps visualize what an LLM-driven chatbot can do, sparking excitement. A group discussion addresses common misconceptions (“Can AI think on its own?”) and ethical considerations (fairness, privacy) at a high level.

Hands-On Activity: Everyone will try a simple AI interaction themselves. For instance, participants are guided to access a public chatbot (such as ChatGPT’s free interface or a similar tool) and pose a question or task of their choice. They will then share their experience on the course forum or Padlet: What did they ask, and were they surprised by the answer? This light exercise builds confidence and curiosity from the outset. (Completion of Week 1 yields the AI Foundations certificate, recognizing understanding of AI/LLM fundamentals.)

Week 2: Prompt Engineering and Token Usage

Learning Objectives: Week 2 dives into the art of communicating with an AI. Participants will learn what a “prompt” is and why phrasing matters, understand basic strategies to elicit better responses from LLMs, and grasp the concept of tokens – the units of text that LLMs process. By week’s end, attendees will know how to craft effective prompts for various scenarios and be mindful of token limits and costs when using AI APIs.

Online Content (Self-Paced): A series of interactive tutorials introduce Prompt Engineering – the emerging discipline of designing prompts to get optimal outputs. We use analogies (prompting as asking an expert the right question) and examples to make it accessible. One tutorial defines prompt engineering as “designing and developing a prompt that elicits the best response from the LLM”. Learners explore good vs. bad prompts: for instance, a poorly phrased query versus a well-structured prompt, and observe the difference in the AI’s answer. Another short video covers tokenization – explaining that LLMs break text into tokens (generally, about four characters per token in English text). This section shows how a sentence is tokenized and explains the idea of a context window (the limit of tokens an LLM can handle in one prompt). Reading materials include a simple guide on tokens and why they matter for AI, with rule-of-thumb facts (e.g. 100 tokens ≈ 75 words). We also provide a link to an online token counter tool so participants can experiment with counting tokens in sample text.

Live Session (1.5 hours, hybrid): This session is a hands-on workshop on crafting prompts. We start with a recap quiz (e.g. “What is a token? Why should we care about token limits?”) to reinforce the online content. Instructors then demonstrate prompt engineering techniques live using a volunteer’s idea, we will iteratively refine a prompt to get a better answer from an AI. For example, we might show how adding context (“You are an expert travel guide...”) or specifying format (“Answer in 3 bullet points”) can dramatically improve the result. Participants break into small groups (in breakout rooms or tables on-site) to tackle a Prompt Challenge: each group receives a task (like “get the AI to explain a complex topic in simple terms” or “have the AI generate a grocery list from a recipe”) and works together to formulate the clearest, most effective prompt. Groups then share their prompt and the AI’s output with everyone. We will discuss what worked well in each case, drawing out general best practices (clarity, providing context, specifying output format, etc.). The session also addresses token usage pragmatically – we show how overly long prompts or outputs can hit limits, and how to trim them. Participants see a quick demo of a token counter in action and learn, for instance, that one token typically corresponds to ~4 characters of text. This gives a concrete sense of why a prompt must be concise.

Hands-On Activity: After the live session, a homework exercise solidifies skills: Each participant must design two prompts for a given scenario (for example: ask an AI to summarize an article, first without any special instructions, then with a role and format specified). They will use an AI (e.g. ChatGPT or a free local model from next week’s tools) to test their prompts and save the responses. On the forum, they’ll post a short reflection on how the outputs differed and which prompt was more effective. In doing so, they practice the iterative mindset of prompt engineering – a key skill going forward. (Completion of Week 2 yields the Prompt Engineering certificate, validating competency in interacting with LLMs effectively.)

Week 3: Local LLM Tools and Deployment

Learning Objectives: In Week 3, participants will discover how to run LLMs on their own computers or private networks. They will learn about the advantages of local models (privacy, cost savings) and get hands-on experience with user-friendly tools like Ollama and LM Studio that make local deployment possible. By the end of the week, everyone should be able to set up a small-scale LLM on their device or understand how to, and know scenarios when local AI is beneficial versus using cloud APIs.

Online Content (Self-Paced): Video tutorials introduce the concept of open-source LLMs (e.g. Meta’s LLaMA family) and why they matter. We present Ollama (a command-line tool) and LM Studio (a graphical desktop app) as two approachable options for running LLMs offline. One article, “6 Best LLM Tools to Run Models Locally,” notes that “you can experiment with LLMs locally using GUI-based tools like LM Studio or the command line with Ollama.”  This reassures learners that even without deep technical skills, local AI is within reach. A screencast then demonstrates step-by-step installation of LM Studio and downloading a lightweight model (for example, a 7-billion-parameter variant that can run on a modern laptop). We highlight key features of these tools: LM Studio’s chat interface and settings, and how Ollama can fetch models and answer queries through a simple terminal command. Importantly, the content emphasizes privacy and control: unlike cloud AI services, local LLM tools do not send your data to external servers – all computations happen on your machine. We include a brief reading on data privacy, explaining that with LM Studio “all your chat data stays on your local machine,” which is a major draw for sensitive applications. A short text also covers practical limitations: local models may be slower or less capable than giant cloud models, and hardware requirements (participants learn the terms CPU/GPU, and why a better computer can handle a bigger model).

Live Session (1.5 hours, hybrid): This week’s live meeting is a guided tech lab. Instructors will help participants get a local LLM running in real-time. Those on-site or on Zoom will follow along as we set up a basic local chatbot using LM Studio (preferred for its easy interface). We’ll walk through launching the app, selecting a model, and entering a simple prompt. Participants see immediate results – an AI response generated with no internet connection needed. For those who prefer coding, we’ll also show how to use Ollama in a terminal to achieve the same. During the session, we will discuss use cases: When might you choose a local LLM over an online one? (Answers include when data is confidential, or to avoid API costs, or if internet access is unreliable). We underscore that local LLMs enhance privacy and reduce dependency on cloud providers, echoing what they learned in the readings. If some participants had trouble installing the tools, instructors or peer helpers assist during the session (we allocate time for troubleshooting common issues like incompatible hardware or missing library installations). By the end of the live lab, most participants will have generated at least one answer from a local model – a milestone in demystifying AI technology.

Hands-On Activity: The homework is to continue exploring on their own: each participant is tasked with a small experiment using a local LLM tool. For example, we ask them to compare two models (perhaps a smaller vs. larger model) on the same prompt, or to adjust a parameter (like the temperature setting that controls creativity) and note differences in the response. They will document their findings in a shared spreadsheet or forum thread, contributing to a joint understanding of the tools. This peer-sharing lets them see a variety of models and settings without individually trying everything. (Completion of Week 3 yields the Local Deployment certificate, signifying the ability to deploy and use AI models locally.)

Week 4: Building a Chatbot with Streamlit (Part 1 – Interface Design)

Learning Objectives: Week 4 marks the transition from using AI tools to building with them. Participants will learn the basics of Streamlit, a Python framework for creating web apps, and begin developing a simple chatbot interface. The goal by the end of this week is that each participant can create a rudimentary web-based chat interface that takes user input and (in Part 2 next week) will display AI responses. In other words, they start constructing the front-end of their own chatbot application.

Online Content (Self-Paced): The learning materials this week assume minimal programming knowledge and focus on hands-on instruction. A video tutorial, “Intro to Streamlit for Beginners,” walks through how Streamlit works. It introduces Streamlit as an open-source Python library for creating beautiful, interactive web applications with just a few lines of code. The tutorial shows examples of how easy it is to put together a text input box and display output. We include code snippets with explanation: for instance, how to use st.text_input() for user text and st.write() to print output on the webpage. A short reading or cheat-sheet is provided for reference, summarizing key Streamlit commands used in the project. Additionally, a brief overview of the chatbot architecture is given (at a conceptual level): explaining that our app will consist of a front-end (Streamlit UI) and a back end (an LLM that generates responses). This helps participants understand the pieces they are building and how this week’s work will connect to next week’s LLM integration.

Live Session (2 hours, hybrid): This longer session is a live coding workshop. Together, we will start building the chatbot step by step. The instructor shares their screen (and those in person can code along on laptops) to write a basic Streamlit app from scratch. We begin by creating a new Python script (app.py) with a simple “Hello, AI” example to ensure everyone can run a Streamlit app. Once the environment is set up (ensuring Streamlit is installed via pip), we proceed to implement the chatbot UI:

• First, we add a title and description to the web app (using st.title() and markdown text for context).

• Next, we include an input field for the user’s message (user_input = st.text_input("Ask me anything:")).

• We make sure participants understand that nothing will happen yet when they type, because we haven’t connected it to any model. So, as a placeholder, we write a bit of logic: if the user enters text, the app will echo it back or respond with a canned message (e.g., “👋 Hi there! You said: [user’s input]”). This way, our app can simulate a response, confirming that input/output flow works.

• We also demonstrate how to maintain a conversation history on the interface by storing past inputs and outputs (using a st.session_state or simply appending to a list that we display). This is important for the chatbot feel, and we’ll leverage it once real AI responses are in place.

Throughout the live coding, participants follow along, and we pause frequently for everyone to catch up or troubleshoot. Those with less coding experience are encouraged to copy the provided snippets and see the result, emphasizing that understanding how to glue pieces together is more important than memorizing code. By the session’s end, each participant has a basic Streamlit chatbot UI running locally: it can take an input and display a response (albeit a dummy one for now).

Hands-On Activity: The project continues after class. Participants are given template code (from the session) and a list of “stretch goals” to try at home. These might include adding an avatar or icon next to chatbot responses, changing the theme or styling of the app (Streamlit makes this easy via settings), or writing a custom welcome message that shows when the app loads. These optional enhancements let more advanced participants be creative, while beginners can solidify what was done in class without feeling pressure to do extra. Everyone should at least rerun their app and share a screenshot on the forum for accountability. (Week 4 is part of the larger Chatbot Building module; progress is cumulative. By end of Week 5, upon completing the chatbot, the Chatbot Building certificate will be awarded.)

Week 5: Building a Chatbot with Streamlit (Part 2 – LLM Integration and Deployment)

Learning Objectives: In Week 5, participants will complete their chatbot projects by integrating an actual LLM to generate responses and making the app available to others. They will learn how to connect to the Groq platform to power their chatbot’s brain and how to deploy the finished application for anyone to try. By the end of this week, each participant will have a working AI chatbot that leverages a large language model for responses, and they’ll understand the basics of deploying a web app to the cloud (using Streamlit’s free sharing service). This is the “and Beyond” part, taking their AI skills to the level of creating shareable tools.

Online Content (Self-Paced): The preparatory materials focus on the new pieces: Groq API usage and app deployment. A short explainer video introduces Groq – described as “a cutting-edge platform for fast AI inference that offers free APIs for a wide range of LLMs (from open-source models like LLaMA to paid ones like OpenAI’s), launched in early 2024." The video (or reading) explains why Groq is a game-changer: it removes the need for personal high-end hardware or paid cloud accounts by allowing developers to access powerful models through a simple API call. We provide a step-by-step guide (with screenshots) on how to sign up for a free Groq account and obtain an API key. Next, we have a tutorial on connecting the Streamlit app to Groq. This includes sample code using the groq Python library (or REST calls) to send the user’s input to an LLM and receive the output. The code snippet is straightforward and well-documented, so even those new to APIs can follow for example, showing how groq.Completion.create(prompt=user_input, model="llama2-13b") might be used to get a response from a LLaMA-2 model hosted on Groq. Finally, content covers deployment: a short video “Deploy your Streamlit App in 5 Minutes” demonstrates how to push the app code to a GitHub repository and use Streamlit Cloud to host it. The video shows that with just a few clicks (selecting the repo and branch in Streamlit’s interface), the app goes live on the web. This lets participants know that making their project accessible to others is easy – “deploy...making it accessible to everyone, all at no cost."

Live Session (2 hours, hybrid): This is the climax of the building process. During the live session, we integrate the LLM and test the full chatbot. We start by helping everyone plug in their Groq API key securely (in Streamlit, this might involve using a secrets file or .env configuration – we guide them through this, taking care not to reveal keys publicly). Once the setup is done, we modify the Streamlit app code from Week 4: replacing the placeholder response with an actual call to the Groq API. The instructor demonstrates, and participants follow when the user submits a query, our code sends it to Groq’s servers, which run an LLM (e.g., a 13-billion parameter model) and returns a completion. We then display that output in the chat interface. The moment of truth comes as everyone tries asking their now fully functional AI chatbot a question. There’s often a delightful surprise when seeing the app respond with real AI-generated answers from a custom interface they built themselves! We troubleshoot any issues (common ones might be forgetting to paste the API key, hitting rate limits, etc.). Next, we cover deployment live: the instructor walks through deploying their app to Streamlit Cloud, and participants mirror the steps with their own GitHub repositories. For those unfamiliar with GitHub, we prepared a template repository they can fork, containing their code, and then use Streamlit’s UI to deploy. By the end of the session, many participants will have their chatbots running on a public URL. We encourage everyone to open each other’s deployed apps, try them out, and share supportive feedback. This creates a celebratory atmosphere of “Look what we made!”

Hands-On Activity: The immediate task is to finish and polish the project. Participants who couldn’t complete deployment during the session can do so after, using the workshop recording and guide. Everyone is asked to prepare a brief presentation (2-3 minutes) about their chatbot for the final showcase in Week 6. This includes naming their chatbot, describing its intended use case or what they focused on (some might tailor it to answer math questions, given the math professor’s involvement, or to assist in their professional field), and what they learned in building it. As an optional extension for advanced learners, we suggest experimenting with different models via Groq or adding features (for example, integrating a second input to set the AI’s “persona” or using Streamlit widgets for file upload if someone wanted the bot to analyze text files). These enhancements are not required but provide room for growth. (By completing the chatbot project in Week 5, participants earn the Chatbot Building certificate, demonstrating their ability to develop an AI application. They are now ready for the final module: showcasing their expertise.)

Week 6: Final Showcase and Beyond

Learning Objectives: The final week solidifies the journey “to LLM and Beyond.” Participants will reflect on and demonstrate their learning via a capstone showcase and engage in forward-looking discussions about the broader AI landscape and how to continue learning. By the end of Week 6, participants will feel confident in their ability to understand and apply AI/LLM technology, and they’ll have concrete evidence – their project and certificates – to show for it. We also aim for them to be inspired to go “beyond,” whether that means integrating AI in their teaching or work, pursuing advanced courses, or simply staying curious about new developments.

Online Content (Self-Paced): This week has lighter content to allow time for project prep. The materials include guidance on how to make an effective short presentation of their project (tips on storytelling: the problem it solves or the idea behind it, a quick demo, and key takeaways). We also provide an article or video on “The Future of AI: Beyond LLMs.” For example, a short lecture might discuss upcoming trends like multimodal AI (models that handle images, audio, etc.), or how LLM technology is evolving (more efficient models, new applications). This is kept high-level and exciting, not technical – the idea is to show that what they learned is part of a fast-moving field and encourage them to continue exploring. A reading on ethics and responsible AI use is also provided to wrap up the ethical considerations: for instance, ensuring they know about biases in AI and the importance of using these tools thoughtfully. Participants are asked to read or watch these as a stimulus for the final live discussion.

Live Session (2 hours, hybrid – onsite showcase and streaming): The finale is designed as an engaging, celebratory event. If feasible, we host an in-person showcase event on campus (for those local, with others joining via video). Participants each briefly present their chatbot or key learnings in a friendly “science fair” format. Depending on class size, this could be done sequentially (each shares their screen or comes to the front for 2 minutes) or in small groups, demonstrating to each other. The math professor and instructor(s) act as moderators and cheerleaders, highlighting the progress made: “Just six weeks ago, many of you had never tried an AI tool – and now you’ve built your AI-powered application!” We encourage a couple of faculty or industry guests to attend (even virtually) to ask questions and provide real-world perspective, making the showcase more rewarding for participants. After presentations, we have an open discussion titled “Beyond LLM – What’s Next?”. Drawing on the provided future-looking content, we chat about how AI might further impact teaching, business, or daily life. Participants share how they plan to use their new skills – some faculty might brainstorm integrating AI into their curriculum, professionals might identify a workflow to improve with AI, and so on. Instructors highlight resources for continued learning (for example, mentioning advanced courses or communities of practice, and how to stay updated on AI news). We also circle back to the ethical use guidelines, ensuring everyone leaves with a sense of responsibility to use AI for positive outcomes. Finally, the session (and workshop) concludes with awarding the Certificates: participants who completed each module receive their corresponding certificates (many will have all four), and those who finished the entire series receive a special “From 0 to LLM” completion certificate, symbolizing their expert-level achievement. A group photo (with certificates in hand, for those on-site, and screenshots of online attendees) caps off the celebration.

Hands-On Activity: (None – Final Showcase) Instead of new homework, this week’s “activity” is the project presentation. Participants are encouraged to share their deployed chatbots with friends or colleagues and revel in their accomplishment. We also invite them to fill out a feedback survey about the workshop. Their journey from Zero AI to Expert AI practitioners is now complete, but we provide an open door for future support: an alumni Slack or mailing list to continue exchanging ideas as they go “beyond” this workshop in their AI endeavors.

Stackable Certification Framework

To recognize participants’ achievements and allow flexibility, the workshop uses a stackable certification model. Learners earn a certificate for each module completed, and these can stack toward the full program certification. The four module certificates and the capstone are:

• AI Foundations Certificate: Awarded after Module 1 (Week 1). Indicates mastery of foundational AI concepts and familiarity with LLM basics. Participants can cite this micro-credential to show they understand core AI terminology and context.

• Prompt Engineering Certificate: Awarded after Module 2 (Week 2). Validates that the earner can effectively interact with AI models, craft well-structured prompts, and comprehend tokenization and its implications.

• Local LLM Deployment Certificate: Awarded after Module 3 (Week 3). Signifies ability to set up and run AI models locally, reflecting skills in using tools like LM Studio/Ollama and an understanding of data privacy in AI.

• Chatbot Building Certificate: Awarded after Module 4 (end of Week 5). Demonstrates practical development skills – the holder has built a functional chatbot application using Streamlit and connected it to an LLM (via Groq or similar). This is a strong portfolio piece for technical and non-technical professionals alike.

• “AI LLM” Program Certificate (Expert-Level): Granted at the end of Week 6 to participants who complete all modules (the four above). This capstone certificate acknowledges the comprehensive achievement of progressing from no prior knowledge to building an AI solution. It can be described as an “AI Literacy and Application” certification, representing ~6 weeks of hybrid training. Participants also receive a digital badge for the full program, which they can share on LinkedIn or other platforms.

Each certificate is issued by our institution/organizers and co-signed by the lead instructor and the collaborating math professor, lending academic and professional credibility. The stackable nature means some participants might opt to take just the first module or two (earning, say, AI Foundations and Prompt Engineering badges) if that’s all they need, while others will complete the entire series. This flexible framework is designed to accommodate different goals, while motivating learners to keep going to “collect them all” and gain full expertise.

Course Format and Delivery (Online vs. Live)

This workshop is designed to maximize both flexibility and personal interaction through its hybrid format:

• Asynchronous Online Content: Each week, learners spend ~2–3 hours on self-paced materials (hosted on a learning platform). This includes video lessons, readings, quizzes, and simple practice exercises. Participants can engage with these on their schedule, which is ideal for busy professionals and students. The content is chunked into short segments (typically 5–15 minutes each) to maintain engagement and accommodate different paces. Discussion forums are available for questions at any time. The online content delivers the core knowledge (concepts, definitions, demos) in a convenient form, which learners can review as needed.

• Synchronous Live Sessions: Once a week, the cohort meets for a live video conference (approximately 1.5–2 hours, via Zoom and in-person for locals). These sessions are the interactive cornerstone of the course, led by the instructors. They are deliberately scheduled after participants have completed the week’s online materials, so that everyone comes in with some background and questions. The live sessions focus on discussion, Q&A, and hands-on activities rather than lecturing on new theory. This is where participants practice skills with guidance – whether it’s refining prompts in a group or coding a Streamlit app together. The professor co-designer often takes a role in these live meetings to provide insights (e.g., offering a clear explanation of a concept like tokenization, or relating AI logic to problem-solving approaches familiar in math). We use breakouts and collaborative tools (Google Docs, coding together on repl.it or similar) to make sessions engaging. All live meetings are recorded for those who might miss a session or want to re-watch demonstrations.

• Onsite Option: For participants in the local area, we offer an onsite venue (e.g., a computer lab or smart classroom at the university) during the live session times. This means some participants and the professor/instructors can gather physically, while others join remotely – a true hybrid experience. The onsite attendees benefit from face-to-face interaction and hands-on support, while remote attendees still fully participate through screen-sharing and online collaboration tools. We ensure the classroom has the necessary A/V setup so remote participants can see and hear everything, and vice versa. Having an onsite option is especially welcoming for faculty or local professionals who prefer an in-person learning environment, and it also facilitates the final showcase event being at least partly face-to-face.

• Time Commitment: In total, participants should allocate roughly 4–5 hours per week (2–3 hours self-paced + ~2 hours live). The schedule is structured so that online content is released at the start of each week (e.g., every Monday), and the live session occurs mid- or end-week (e.g., every Thursday evening). This cadence gives learners a few days to go through the materials before meeting live. We chose a 6-week duration to balance depth and feasibility – long enough to cover a rich curriculum and build a project, but short enough for working adults to commit.

• Support and Communication: Throughout the course, instructors are available via email and a community chat (Slack or MS Teams channel). We hold “virtual office hours” each week (an optional drop-in video call) for anyone needing extra help with installations, understanding content, or just wanting to brainstorm project ideas. Peer support is encouraged via the forums – often participants will share tips (for example, if one finds a cool prompt technique or solves a software glitch, they can post about it). This hybrid model of blended learning has been proven effective – for instance, a similar 6-week micro-credential program successfully combined 4 live sessions with self-paced work. We expect our format to likewise offer the best of both worlds: flexibility plus personal connection.

Hands-On Learning and Final Showcase

Practical application is at the heart of this workshop. Each week features hands-on activities designed to reinforce learning by doing, and the program culminates in a final showcase project that ties all the skills together. Here we summarize the key experiential components and the showcase event:

• Weekly Hands-On Activities: Every module includes exercises where participants actively apply concepts:

• Interactive Demos: In live sessions, participants don’t just watch – they use AI tools in real time (asking ChatGPT questions in Week 1, tweaking prompts in Week 2, etc.). This immediate practice, with instructors on hand, builds confidence.

• Group Work: Collaborative challenges (like the prompt-crafting challenge in Week 2) encourage learning from peers and simulating real-world problem solving in teams.

• Lab Exercises: Weeks 3–5 especially involve lab-style work. For example, setting up a local LLM in Week 3 is a guided technical exercise. Building the Streamlit app in Weeks 4–5 is essentially a mini-project carried out with instructor supervision.

• Homework Assignments: These are often practical tasks: designing prompts, experimenting with tools, or coding small features. They are directly related to what was taught and serve as “learning by doing.” The assignments are not graded in a traditional sense, but completion is tracked as part of earning the module certificates. Participants receive feedback on their work either via automated checks (quizzes) or instructor/peer comments (for shared exercises like posting prompt results).

• We ensure all activities have a clear purpose and are achievable by novices. For instance, when introducing programming in Week 4, we provide starter code and focus on modifying it, rather than expecting learners to write everything from scratch. This guided approach lets even those with no coding background participate and learn the concepts of how the code works.

• Project-Based Learning: The chatbot that participants build is a unifying project that spans multiple weeks. This capstone project encapsulates many learning outcomes: understanding AI (they have to explain what their chatbot does), prompt engineering (crafting how the bot should respond or behave), using local/online models (Groq API integration), and basic app development. By gradually building it, piece by piece, participants experience the full lifecycle of an AI application – a powerful way to cement their knowledge. They also gain a tangible artifact (the deployed chatbot and its code) they can showcase professionally or even continue to improve after the workshop.

• Final Showcase: The Week 6 showcase event is both a learning experience and a celebration. Presenting their work helps participants develop an ability to communicate about AI – an important skill when bringing AI innovations to their workplace or community. During the showcase, they’ll practice explaining the problem their chatbot addresses or the idea behind it in simple terms, and demonstrate it live. This exercise reinforces their understanding (if you can teach or demo it, you truly know it) and boosts their confidence as newly minted “AI practitioners.” We encourage creativity in presentations: some may do a live role-play showing the chatbot answering questions; others might share a short slide deck with their design process. The showcase also solidifies cross-pollination of ideas: participants see each other’s projects, inspiring further learning (e.g., “Oh, you added voice input to your chatbot, how did you do that? I want to try it too!”).

• Community and Networking: Because the audience is diverse (faculty, students, professionals), the hands-on components double as networking opportunities. Group discussions and the final showcase let participants connect with others outside their usual circle, all united by interest in AI. We anticipate, for example, a university professor might end up chatting with an industry professional about collaborating on an AI initiative, or students might impress potential mentors with their projects. The workshop fosters an ongoing community: after Week 6, participants keep access to the course Slack/forum where they can continue asking questions or sharing success stories as they apply their new skills in the real world.

In summary, the workshop’s practical focus – weekly exercises, cumulative project, and final showcase – ensures that learning sticks. By actively creating with AI (not just reading about it), participants will finish the program with concrete skills and the confidence to continue innovating with AI long after the workshop ends.

Promotional Materials

To attract and inform potential participants, we have created sample promotional content tailored for different channels: an email announcement, a short blurb for flyers, and a website course description. These emphasize the workshop’s benefits in an engaging, accessible tone to appeal to our target audience.

1. Email Announcement (Sample)

Subject: Unlock AI in 6 Weeks – “Six Weeks to AI Mastery: A Friendly, Hands-On Workshop: No AI or Coding Experience Needed — Just Bring Your Curiosity!” Workshop (Hybrid, starts <TBA>)

Dear Colleagues and Friends,

Are you curious about Artificial Intelligence and tools like ChatGPT, but not sure where to begin? We invite you to Six Weeks to AI Mastery: A Friendly, Hands-On Workshop: No AI or Coding Experience Needed — Just Bring Your Curiosity!, a 6-week hybrid workshop designed for beginners to gain expert-level AI skills in a friendly, hands-on environment. This program, co-led by a math professor and an AI specialist, will take you from zero knowledge to building your own AI chatbot in just over a month!

What’s in it for you?

• Learn by Doing: Each week, you’ll explore a new facet of AI – from understanding how AI “thinks”, to crafting effective prompts, to running AI models on your laptop, and finally creating a chatbot from scratch. No boring lectures – you’ll try it out yourself, with guidance at every step.

• Beginner-Friendly: Absolutely no prior experience with AI or coding is required. We start with the basics and gradually move to advanced applications, in plain English. If you can browse the web and use a spreadsheet, you can do this!

• Hybrid Flexibility: Learn online at your convenience with short videos and activities each week, then join us for a live session (in-person or via Zoom) to practice and ask questions. It’s the best of both worlds – flexibility plus personal support.

• Stackable Certificates: Earn 4 micro-certificates (AI Foundations, Prompt Engineering, Local AI, Chatbot Building) as you progress, plus a final certificate for completing the whole series. Showcase these achievements on LinkedIn or your resume to highlight your up-to-date AI skills.

• Community and Fun: You’ll be part of a diverse group of faculty, students, professionals, and lifelong learners. Work together on mini-challenges and share ideas. Our final session is a showcase where you’ll present your project and celebrate how far you’ve come!

Workshop Details:

• Dates: <TBA> (6 weeks)

• Live Sessions: <TBA> (join on campus or online) – recordings available.

• Cost: $TBA (covers all materials and certification; discounts available for students)

• Registration: <TBA> (Limited slots available, so please register early!)

Whether you’re a professor wanting to integrate AI into your classes, a professional aiming to upskill, or just someone fascinated by AI’s possibilities, this workshop will empower you with practical knowledge and confidence. By the end, you’ll not only understand how AI and LLMs (Large Language Models) work – you’ll have built something amazing with them.

Ready to go from 0 to 100 in AI? Don’t miss this opportunity. Secure your spot today by registering at <TBA>.

Feel free to reach out if you have any questions. We hope to see you at the workshop!

Sincerely,

Walter

2. Flyer Blurb (for Print or Social Media)

Six Weeks to AI Mastery: A Friendly, Hands-On Workshop: No AI or Coding Experience Needed — Just Bring Your Curiosity!

6-Week Hybrid Workshop – Learn AI & Build Your Own Chatbot!

Ever wondered how AI like ChatGPT works? This beginner-friendly workshop will take you on a journey from zero knowledge to creating a functional AI chatbot in just 6 weeks. Co-designed by university professors and AI experts, the program is perfect for students, professionals, and curious minds of all backgrounds.

• No Experience Needed: Start with the basics of Artificial Intelligence and Large Language Models (what they are and how they’re changing the world).

• Hands-On Learning: Practice prompt engineering (the art of talking to AI) and discover how to run AI models on your computer.

• Build & Deploy: Using simple tools like Streamlit (for web apps) and Groq (free AI model API), you’ll develop and deploy your very own chatbot by the end of the course.

• Hybrid Format: Watch short online lessons on your schedule, then join interactive weekly sessions (in-person or via Zoom) to apply what you learned.

• Earn Certificates: Gain four stackable certificates – AI Foundations, Prompt Engineering, Local AI, Chatbot Building – plus a capstone certificate for completing the series.

Dates: <Month Day – Month Day, Year> (weekly sessions every <Day> at <Time>)
Location: Online or Onsite at <Venue if applicable>
Registration: <Contact/URL> (Limited seats)
Cost: $X (scholarships available)

Join us and turn your AI curiosity into a real-world skill. By the finale, you’ll be showcasing an AI project you built yourself! Don’t miss this chance to go “from Zero AI Knowledge” to confidently working with cutting-edge tech.

(For more info and to sign up, visit <Coursewell.com> or email <walter@coursewell>.)

3. Course Description 

Workshop Title: Six Weeks to AI Mastery: A Friendly, Hands-On Workshop: No AI or Coding Experience Needed — Just Bring Your Curiosity!

Overview:
“Unlock the power of AI – no experience required.” This innovative 6-week course takes you on a guided journey into the world of Artificial Intelligence, with a special focus on Large Language Models (LLMs) like GPT (the technology behind ChatGPT). Co-created by a university math professor and an AI industry expert, the workshop is a unique blend of theory, hands-on practice, and project-based learning. It’s designed for absolute beginners in AI, yet by the end, you’ll achieve an expert-level understanding of how to use AI tools and even build your own AI applications. If you’re an educator, student, professional, or just an enthusiastic learner, this workshop will demystify AI and equip you with tangible skills for the future.

What You Will Learn:

• Foundations of AI & LLMs: Understand what AI means, how it “learns” from data, and discover the magic of large language models that can generate human-like text. We’ll explore real-world examples (from smart assistants to AI in business) to see these concepts in action.

• Prompt Engineering: Learn to communicate with AI effectively. You’ll practice crafting prompts (questions/commands) to get the best results from AI models – an emerging skill set that’s highly valued. We break down prompt techniques, teach you about tokens (the units of text AI works with), and how to finesse your inputs to guide the AI’s output.

• Local AI Tools: Not comfortable sending data to the cloud? No problem. We introduce tools like Ollama and LM Studio that let you run powerful language models right on your PC. You’ll gain the know-how to set up a private AI assistant, enhancing privacy and control while saving on API costs.

• Building an AI Chatbot: Experience the full development of an AI project. Using Streamlit (a simple app builder) and Groq (a platform providing free access to advanced AI models), you’ll create a working chatbot step-by-step. This includes designing a user interface and connecting it to an AI model that generates responses. By Week 5, you’ll deploy your chatbot online for others to try – a rewarding achievement that you can proudly share.

• Ethical AI and Future Trends: Throughout the course, we emphasize responsible AI use (fairness, transparency, and limitations). In our concluding module, we’ll also peer into the future, discussing new advancements beyond LLMs and how you can continue learning on your own.

How It Works (Hybrid Format):
You’ll learn through a combination of self-paced online lessons and weekly live sessions. Each week, unlock a set of bite-sized videos, readings, and simple quizzes on our platform – learn at your own pace, when it fits your schedule. Then join our instructors and fellow participants for a live 90-minute session (via Zoom or in-person) to deepen your understanding. Live sessions involve Q&A, discussions, and hands-on workshops (e.g., coding alongside the instructor, or group brainstorming activities). Can’t make a live meeting? No worries – we record them. Our approach ensures you get flexibility and personal support. Expect to spend about 4-5 hours per week in total.

Stackable Certificates:
This workshop offers four micro-certificates as milestones: AI Foundations, Prompt Engineering, Local LLM Deployment, and Chatbot Building. Complete all four modules to earn the comprehensive “From 0 AI to LLM and Beyond” Certificate, a testament to your journey from novice to AI practitioner. These certifications are included in the course and will be issued as digital badges – perfect for showcasing on professional profiles.

Who Should Enroll:

• Educators (K-12 or higher ed) looking to incorporate AI into teaching or simply understand tools their students are using.

• Students from any discipline who want to gain cutting-edge skills and bolster their resumes with an AI project.

• Business and Industry Professionals in any field – marketing, finance, healthcare, you name it – who suspect AI could boost their productivity or creativity. No technical background needed, just a willingness to learn by doing.

• Lifelong Learners and the general public are intrigued by AI. If you’ve played with chatbots and want to peek “under the hood” in an approachable way, this is for you.

Why This Workshop?
Unlike massive open online courses, this workshop is small and interactive – you’ll get to know your instructors and peers. It’s also extremely practical: you won’t just learn theory, you’ll apply it immediately, reinforcing your knowledge. By the end, you won’t be asking “What is AI?” – you’ll be explaining it to others and showing off your AI creation! And with our stackable certificates, you’ll have proof of your new expertise.

Schedule & Logistics:

• Duration: 6 weeks, starting <TBA> and ending <TBA>.

• Live Session Time: Every <TBA), hosted at <TBA> and simulcast on Zoom or TEAMS.

• Technical Requirements: A computer with internet access. For the coding parts, a modern laptop/desktop (Windows, Mac, or Linux) is recommended. We’ll guide any necessary software installs (like Python, which is free). No prior coding experience is required.

• Cost: $TBA (includes all materials and certificate fees). Early-bird and group discounts available.

• Registration: <TBA> (Limited seats to maintain quality; first come, first served).

Testimonials / Past Feedback: “I never thought I could build an app like this myself – this workshop was eye-opening!”

Join Us:

AI is the literacy of the future. Don’t miss the chance to empower yourself with knowledge and skills that demystify AI and enable you to create with it. Six Weeks to AI Mastery: A Friendly, Hands-On Workshop: No AI or Coding Experience Needed — Just Bring Your Curiosity! is more than a course – it’s a launchpad for your AI journey. Whether you aim to leverage AI in your career or simply understand the technology shaping our world, we welcome you to learn, build, and explore with us. Secure your spot today and take the first step from AI novice to AI innovator!

____

Walter Rodriguez, PhD, PE 

CEO, Adaptiva Corp 

CLO, Coursewell.com 

Web: Coursewell.com and AdaptivaCorp.com

Cell/Text: 239.405.3339 

E-Mail: walter@coursewell.com and wrodrigu@mit.edu 

Studio: 552 111th Avenue North, Naples, FL 34108 

Mailing: 3537 Heron Cove Court, Bonita Springs, FL 34134 

LinkedIn: https://www.linkedin.com/in/walter-rodriguez-phd-pe-608454/ 

Zoom: https://us06web.zoom.us/j/9849520274?pwd=UlJXQ1BMMEhzbVZSSTRFc2tnNGRKdz09 

Certification for Digital and AI-Driven Work

Certificación para el Trabajo Digital con IA

🎓 Program Overview / Descripción del Programa

This program equips participants with the most in-demand digital skills and AI tools to enhance their employability and enable success in today’s digital economy. Learners complete a stackable sequence of four microcredentials, each focusing on a high-demand area of expertise.

Este programa prepara a los participantes con habilidades digitales y herramientas de inteligencia artificial (IA) de alta demanda, para mejorar su empleabilidad y facilitar su éxito en la economía digital actual. Los participantes completan una secuencia acumulativa de cuatro microcredenciales, cada una centrada en un área de alta demanda.

📅 Program Duration / Duración del Programa

• Total: Approx. 50–60 hours over 5 weeks (self-paced + optional live sessions).

• Each microcredential: Approx. 12–15 hours.

🎯 Learning Outcomes / Resultados de Aprendizaje

Participants will be able to:

• Apply AI tools to professional tasks.

• Manage and analyze digital information securely.

• Utilize CRM platforms to enhance customer service.

• Apply AI-powered techniques for business management and entrepreneurship.

• Conduct digital talent acquisition processes using modern tools.

• Communicate effectively in bilingual (English-Spanish) professional contexts.

Los participantes podrán:

• Aplicar herramientas de IA en tareas profesionales.

• Gestionar y analizar información digital de forma segura.

• Utilizar plataformas CRM para mejorar el servicio al cliente.

• Aplicar técnicas impulsadas por IA en gestión empresarial y emprendimiento.

• Desarrollar procesos de reclutamiento digital modernos.

• Comunicarse de manera efectiva en contextos profesionales bilingües (español-inglés).

🗂 Program Structure / Estructura del Programa

4 Stackable Microcredentials / Microcredenciales Acumulables:

1️⃣ AI Talent / Talento + IA

Specialist in Digital Talent Acquisition and Artificial Intelligence
Especialista en Reclutamiento Digital e Inteligencia Artificial

Topics / Temas:

• Fundamentals of Digital Talent Acquisition

• Use of AI in Recruiting: GPT, ATS systems

• Digital skills assessment tools

• Building inclusive recruitment pipelines

• Hands-on project: Design an AI-enhanced recruitment process

2️⃣ Digital Competencies + AI / Competencias Digitales + IA

Digital Competencies for Work with AI
Competencias Digitales para el Trabajo con Inteligencia Artificial

Topics / Temas:

• Microsoft 365 and Google Workspace advanced use

• Cybersecurity essentials for all employees

• Responsible use of AI in workplace settings

• Data literacy and basic data visualization

• Hands-on project: AI-powered digital document management workflow

3️⃣ AI for Microbusinesses / IA para Microempresas

Practical AI Tools for Entrepreneurs and Freelancers
Herramientas Prácticas de IA para Emprendedores y Autónomos

Topics / Temas:

• AI for marketing and social media management

• Using GPT and Canva AI for content creation

• Automating business processes with AI

• Financial management tools with AI

• Hands-on project: AI-driven marketing campaign for a microbusiness

4️⃣ Bilingual Service + CRM / Servicio Bilingüe + CRM

Bilingual Customer Service with CRM Technology and AI
Servicio al Cliente Bilingüe con Tecnología CRM e Inteligencia Artificial

Topics / Temas:

• Principles of effective customer service

• CRM systems: Salesforce, Hubspot basics

• Using AI chatbots for customer service

• English-Spanish customer service communication skills

• Hands-on project: Create a bilingual AI-supported customer service workflow

🖥 Delivery Mode / Modalidad de Entrega

• 100% Online through Coursewell LMS

• Integrated AI-powered Learning Assistant (GPT)

• Self-paced modules with instructor-moderated forums

• Optional live workshops and coaching sessions

100 % en línea a través de Coursewell LMS
Asistente de aprendizaje impulsado por IA (GPT)
Módulos autoguiados + foros moderados por instructor
Talleres y sesiones de coaching en vivo (opcionales)

🎓 Assessment / Evaluación

• Practical project per module (graded pass/fail)

• Participation in discussion forums

• Final integrated capstone project (if pursuing full certification)

Proyecto práctico por módulo (evaluación aprobada/no aprobada)
Participación en foros de discusión
Proyecto final integrador (para certificación completa)

🏅 Certification / Certificación

• Digital Microcredential badge per module

• Integrated Certification for Digital and AI-Driven Work upon completing 4 modules

• Compatible with LinkedIn and professional platforms

Badge digital por microcredencial
Certificación integral al completar los 4 módulos
Compatible con LinkedIn y plataformas profesionales

Certified AI Consultant (AIC): Certification Training

Instructor: Walter Rodriguez, PhD, PE, CM, PM
Platform: Canvas LMS | Format: Self-paced with optional live Zoom office hours
Duration: 1 to 6 weeks (flexible pacing)
Prerequisites: None. Ideal for professionals transitioning into AI advisory or solution design roles.

🎯 Course Description

This course empowers professionals to become AI consultants by blending foundational knowledge with practical consulting skills. Participants will explore AI technologies, develop use-case proposals, address ethical and compliance concerns, build no-code prototypes, and craft a personalized consulting strategy. The course culminates in a project portfolio and AI consulting pitch deck. It also prepares learners to pursue AI consulting independently or in alignment with industry-recognized certifications such as Microsoft AI Fundamentals, CPMAI™, and IEEE Ethical AI. Participants will also earn a certificate upon successful completion.

📌 Course Objectives

By the end of this course, participants will:

• Understand the role and scope of AI consulting across sectors

• Identify AI tools and platforms relevant to client needs

• Translate business or organizational problems into AI-powered solutions

• Design and assess ethical, feasible AI use cases

• Prototype AI tools using no-code/low-code platforms

• Build a consulting toolkit including proposals, pricing, and branding

📦 Course Modules

Week 1. Why Become an AI Consultant? > AI market > Consulting minset > Business Understanding) > MS AI900 > Use Case Awareness

Week 2. Understanding AI Tools > Core AI/ML concepts, tools, platforms > AI-900: Core AI Workloads (vision, NLP, decision) > Data Understanding > AIBusiness

Week 3. Scoping and Proposing AI Solutions > Needs analysis, feasibility, ROI > Data Prep > Modeling > MS AI900: AI in Business Scenarios

Week 4. Ethics and Compliance in AI > Risk, bias, trust, laws, governance > IEEE AI Ethics > Governance Layers

Week 5. Prototyping with No-Code Tools > Use ChatGPT, Zapier, Notion, etc. > MS AI900: Conversational AI & Demos > Evaluation > Building Use Case Solutions

Week 6. Becoming a Practicing AI Consultant > Pitch deck, pricing, branding > Deployment & Ops > General consulting certification prep

🔍 Detailed Certification Mapping per Module

🧠 Module 1: Why AI Consulting?

• Business Understanding

  • Deliverable: Statement of Purpose + Client Discovery Outline

• MS AI900 Fundamentals: Identify real-world use cases for AI

🧠 Module 2: Understanding AI Tools

• MS AI900 Domains:

  • Machine Learning, NLP, Vision, Knowledge Mining

• Data Understanding

• IEEE: Literacy in capabilities and risks

• Activity: Build your "AI Tech Radar" customized to your consulting niche

🧠 Module 3: Scoping & Proposal Design

• Data Prep, Modeling Approaches

• MS AI900: Choose AI model types based on business needs

• Project: 1-page consulting proposal + feasibility matrix

• Peer Review: Evaluate peers using a certification-aligned rubric

🧠 Module 4: Ethics & Compliance

• IEEE AI Ethics & CertNexus:

  • Topics: Transparency, privacy, bias, auditability

• CPMAI™ Governance Layer

• Project: Fill out AI Use Case Risk Matrix + Reflection Post

• Discussion: “Would you take on this ethically risky project?”

🧠 Module 5: Prototyping AI Solutions

• AI-900: Demonstrate Conversational AI + Responsible AI

• Build low-code or business-aligned solutions

• Project: No-code screencast or tool integration storyboard

🧠 Module 6: Practicing AI Consulting

• AI Deployment & Business Integration

• MS AI900/IEEE: Explain how your solution supports responsible deployment

• Final Project: Pitch deck + branding strategy with a nod to certification next steps

🏅 Final Certificate

Upon completion, learners receive:

✅ Certificate of Completion: AI Consultant
✅ Preparation Summary for AI-900, CPMAI™, and IEEE AI Ethics Certifications

📚 Required Materials

All materials are provided within Canvas, including:

• Video lectures

• Readings and templates

• Downloadable tools and demos

• Links to free tools, such as ChatGPT, Zapier, Notion AI, and other no-code tools

🧠 Grading & Completion

• This is an active learning, non-graded, certificate-based course

• Completion is based on the submission of weekly activities (discussions, projects) and the final consulting pitch

• Feedback is provided via peer review and instructor comments

• A certificate is issued upon completing all six modules

🔄 Participation Expectations

• Engage in weekly discussion boards and project-based learning

• Provide feedback to peers when requested

• Schedule at least one optional Zoom session with the instructor for feedback

👥 Who Should Take This Course

• Professionals seeking to enter or enhance AI consulting or teaching practice

• Faculty, entrepreneurs, and veterans transitioning into digital careers

• Consultants or advisors expanding their services with AI capabilities

🧰 Capstone Outcome

Participants will leave the course with:

• A defined AI consulting practice area and mission

• A case-based use case proposal and ethics analysis

• A no-code demo or prototype concept

• A pitch deck and brochure to attract clients