Session One: Joe Reilly and Prashant Mittal

This foundational module introduces professionals to the essential concepts of artificial intelligence and large language models (LLMs) needed for effective workplace integration. Learners will explore the evolution of AI from its historical origins to current applications, gaining a comprehensive understanding of key terminology including tokens, parameters, and retrieval-augmented generation (RAG). The module critically examines both the cognitive advantages AI offers professionals and the potential risks to human skill development, while addressing crucial challenges such as bias mitigation and hallucination prevention. Through hands-on exploration of popular open-source and proprietary models, participants will develop practical skills for implementing AI tools ethically and effectively in later modules, establishing a solid foundation for responsible AI adoption.

Learning Outcomes

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

1. Analyze the evolution and current landscape of AI by tracing key historical developments and explaining how modern LLMs differ from earlier AI approaches in terms of capabilities and applications.
2. Apply fundamental AI terminology and concepts by accurately defining and using key terms such as tokens, parameters, training data, RAG, fine-tuning, and prompt engineering in professional discussions about AI implementation.
3. Implement bias mitigation and hallucination prevention strategies by identifying common sources of AI bias, recognizing hallucination patterns, and applying verification techniques to ensure reliable AI-assisted work outputs.
4. Evaluate the professional impact of AI adoption by identifying specific cognitive advantages AI provides to workers while critically assessing potential risks to human skill development and decision-making autonomy.
5. Demonstrate practical AI tool selection by comparing open-source versus proprietary models and designing ethical AI workflows that complement human expertise.

Session Two: Joe Reilly & Prashant Mittal

This model consists of a 60-minute hackathon to engage learners in a fast paced, collaborative experience where they can put GenAI and communication skills into action on a diverse set of challenges of their choosing. Learners will work in online teams of 3–4, where they will iterate quickly from idea to prototype. The hackathon focuses on teamwork and role distribution while encouraging each member to contribute. Each team will submit to SkillStack a brief problem statement, a description of their AI-driven solution, and a 2–3 minute recorded presentation. Submissions will be assessed on impact and clarity, including how well the GenAI approach was applied. The format is intentionally made high-energy time bound, and will require preplanning. Learners will be asked to choose one of the following tasks from the three tracks:

Track 1: Productivity & Problem-Solving

1.Quiz Me: Build an instant quiz generator for any topic and difficulty level.

• At least 5 quiz questions generated from a topic
• Questions sorted by difficulty
• Answer key included

2.Barrier Buster: Make a resource more accessible for people with limited English proficiency.

• Text rewritten at simpler reading level
• Translated into at least one additional language
• Optional: Add an AI-generated audio narration

3.Disaster Helper: Create a chatbot that gives clear guidance in an emergency scenario.

• At least 3 emergency scenarios handled
• Step-by-step response for each
• Language kept clear and calm

Track 2: Creativity & Exploration

4.Smart Infographic Creator: Convert boring text or data into an engaging AI-generated infographic.

• Infographic includes at least 3 data points
• Colors and layout are visually appealing
• Content is easy to understand

5.GIS Mapper: Create an AI-assisted interactive map using real or sample location data.

• Map includes at least 5 locations
• Each location has a short AI-generated description
• Optional: Add AI-generated images for each location

6.Travel Genie: Plan a perfect one-day itinerary for a city based on interests.

• Morning, afternoon, and evening activities
• Map or route suggestion
• 1–2 AI-generated visuals of locations

7.What If? Generator: Spin absurd or fun alternate history scenarios on demand.

• At least 3 “What if…” scenarios generated
• 1–2 AI visuals per scenario
• Short description of how the world changes

Track 3: Community & Business Impact

8.Learning Booster: Create an AI tutor for STEM topics or a personalized study-planning assistant.

• Custom study plan or interactive tutor conversation
• Adaptive difficulty or feedback loop
• Visual aids (diagrams, flashcards) included

9.Community Connector: Build a local resource finder or accessibility tool to help a specific group in the community.

• Resource database with at least 5 entries
• Easy-to-use search or filtering
• Accessibility features (translation, text-to-speech)

10.Small Business Power-Up: Create a customer service chatbot or personalized product recommender for a small business.

• Handles at least 3 common customer queries
• Recommends relevant products/services
• Includes friendly and consistent brand tone

Session Three: Balazs Szelenyi

This workshop teaches advanced prompt engineering techniques specifically for academic research and scholarly writing. Students will learn systematic approaches to integrate AI tools into college-level research workflows, from literature review through final presentation.

Learning Objectives

Students will learn to:

• Design multi-step prompts for academic research tasks
• Create systematic workflows for literature reviews and methodology development
• Generate effective data analysis and visualizations using AI
• Apply advanced prompting for academic writing and citation management
• Develop presentation materials and oral presentation skills with AI assistance

Session Four: Lecturer Prashant Mittal

Course Description:

This session is about helping leaners understand how AI can tackle challenges in the insurance and finance industry while getting hands on experience with tools like Claude. I'll start by describing common pain points, think claims processing delays, fraud detection, risk assessment, and customer experience issues, and show how AI can make a difference with such real-world examples. Then, I'll move into a guided tour of Claude to explore its features, how it works, and why it's ideal for productivity and workflow optimization.

The last part will demonstrate where I will build a solution to a specific insurance industry issue using GenAI. I will create a standalone web app from scratch, complete with a predictive model tailored to the problem and a clean, user-friendly interface. This session will give the participants practical experience they can take back to their teams.

This workshop is designed for anyone curious about integrating AI into their workflows.

Learning Objectives:

1.Identify key challenges in the insurance and finance industry and understand how AI addresses business pain points.

2.Explore AI-powered tools like Claude to understand their features, psychology, and how they enhance productivity. 

3.Evaluate AI applications in Banking and Insurance use cases where AI driven solutions have improved efficiency and decision making.

4.Develop hands on experience with Claude by participating in the creation of a standalone web app, integrating a predictive model tailored to an insurance-related problem.

Session Five: Umesh Hodeghatta

To develop a deep understanding of the ethical issues that arise across the AI lifecycle, with special attention to the role of data, systemic bias, and governance structures in shaping AI outcomes. We begin by mapping the landscape of AI ethics — exploring why responsible AI is essential in modern society and how ethical lapses can lead to societal harm, reputational damage, and regulatory consequences. Students will examine the foundational dependencies of AI systems, especially data quality, privacy, and governance. We will analyze how incomplete, unrepresentative, or poorly governed datasets can embed systemic inequities into AI models. We will also discuss AI bias, where students will dissect how bias can enter at different stages — from data collection and labeling to algorithmic design and deployment contexts.

Activities:

• Case Study Analysis:Dissection of a high-profile AI ethics failure (e.g., facial recognition bias in law enforcement).
• Small Group Exercise:Identify potential bias sources in a hypothetical AI recruitment tool.

Session 2 – Building Reliable and Responsible AI

Objective:
To equip students with the frameworks, methods, and tools necessary for creating AI systems that are transparent, fair, and accountable. This session transitions from problem identification to solution design. Students will learn the principles of transparency and explainability, focusing on how to make AI decisions understandable to diverse audiences, including technical teams, business stakeholders, regulators, and end-users. We will explore different explainability techniques (e.g., feature importance, model-agnostic methods) and discuss their limitations.

The session concludes with a hands-on integration exercise, where students design a high-level AI system concept while explicitly mapping ethical safeguards.

Activities:

• Explainability Demo:Using a simple model to illustrate different transparency techniques.
• Framework Mapping Exercise:Apply an ethical AI checklist to a sample AI project.

Session Six: Umesh Hodeghatta

Session 1 – Ethical Challenges in AI

Objective:
To develop a deep understanding of the ethical issues that arise across the AI lifecycle, with special attention to the role of data, systemic bias, and governance structures in shaping AI outcomes. We begin by mapping the landscape of AI ethics — exploring why responsible AI is essential in modern society and how ethical lapses can lead to societal harm, reputational damage, and regulatory consequences. Students will examine the foundational dependencies of AI systems, especially data quality, privacy, and governance. We will analyze how incomplete, unrepresentative, or poorly governed datasets can embed systemic inequities into AI models. We will also discuss AI bias, where students will dissect how bias can enter at different stages — from data collection and labeling to algorithmic design and deployment contexts.

Activities:

• Case Study Analysis:Dissection of a high-profile AI ethics failure (e.g., facial recognition bias in law enforcement).
• Small Group Exercise:Identify potential bias sources in a hypothetical AI recruitment tool.

Session 2 – Building Reliable and Responsible AI

Objective:
To equip students with the frameworks, methods, and tools necessary for creating AI systems that are transparent, fair, and accountable. This session transitions from problem identification to solution design. Students will learn the principles of transparency and explainability, focusing on how to make AI decisions understandable to diverse audiences, including technical teams, business stakeholders, regulators, and end-users. We will explore different explainability techniques (e.g., feature importance, model-agnostic methods) and discuss their limitations.

The session concludes with a hands-on integration exercise, where students design a high-level AI system concept while explicitly mapping ethical safeguards.

Activities:

• Explainability Demo:Using a simple model to illustrate different transparency techniques.
• Framework Mapping Exercise:Apply an ethical AI checklist to a sample AI project.

Session Seven: Ganesh Subramanian

This 90-minute micro-workshop, Agentic AI in Action: Build Smart Python Agents for Real-World Impact, introduces students to the concept of Agentic AI and equips them with the skills to create functional AI agents using LangChain and Python. The session blends short theory segments with live coding, hands-on project work, and career alignment discussions. Students learn by building their own agent in Google Colab, using minimal code, and gain resources to continue developing AI solutions post-session.

Learning Objectives

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

1.Understand Agentic AI – Explain how AI agents differ from standard LLMs and why they matter.

2.Build with LangChain – Apply the basic components (LLM, Chain, Tools, Agent) to create an AI agent.

3.Develop Independently – Construct and run a working AI agent in Google Colab.

4.Connect to Careers – Identify job roles like AI Agent Developer, Prompt Engineer, where these skills apply.

5.Access Resources – Utilize provided cheat sheets, code templates, and links to keep building after the workshop.

Session Eight: John Wilder

This session demystifies machine learning for non-technical professionals. Participants will learn key ML terminology, understand why machine learning gets distinguished from broader AI concepts, and explore the spectrum from traditional algorithms (decision trees, k-Nearest Neighbors, Support Vector Machines) to modern neural networks. The session examines critical limitations including hallucinations, dataset bias, and the emerging problem of AI systems trained on AI-generated content. Concrete examples, such as adversarial images and LLM jailbreaking, will give attendees a nuanced understanding of how these technologies work and fail. The workshop explores real-world tools across the ML spectrum—from FICO’s traditional credit scoring algorithms and bank fraud detection systems to modern language model applications like Grammarly and Copilot—helping participants understand when different approaches are used and why. The session concludes with best practices for integrating these technologies into professional workflows while maintaining appropriate skepticism and verification processes, emphasizing that understanding the underlying technology makes you a more effective user regardless of which tools you choose.

This approach gives your audience a much richer understanding of the ML landscape beyond just the current LLM hype, showing them that ML has been quietly powering business tools for decades in forms they may not have recognized as “AI.”

Session Nine: Lecturer Anton Sinitskiy

Course Description

This module explores the cutting-edge developments in AI-driven automation of scientific research workflows, all the way from initial literature discovery to manuscript preparation and publication. Students will examine state-of-the-art systems that can autonomously conduct literature reviews, generate research hypotheses, design and execute experiments, analyze results, and draft scientific papers. Through detailed case studies of platforms like AI Scientist, Agent Laboratory, Copilot Researcher, and various DeepResearch agents, students will gain experience with these emerging tools while developing critical evaluation skills to assess their capabilities and limitations. The course emphasizes the transformative potential of automated scientific workflows, preparing students to navigate the evolving landscape of AI-assisted research across disciplines.

Learning Objectives

Analyze the current capabilities and limitations of AI systems for automating scientific work.

Design appropriate use cases for automated scientific workflows, identifying research problems that are well-suited for AI automation versus those requiring human oversight and intervention.

Identify and mitigate risks in automated scientific workflows, including recognizing hallucinations, citation errors, methodological flaws, bias propagation, and other failure modes that compromise research integrity.

Predict future developments in AI-automated research and develop frameworks for responsibly integrating emerging tools into scientific practice while maintaining methodological rigor and transparency.

Session Ten: Ilka Kostka, Allison Ruda, Chris Unger

This interactive workshop explores how AI can transform professional practice by augmenting human expertise rather than replacing it. Participants will examine core professional competencies—critical thinking, strategic decision-making, client communication, and complex problem-solving—and discover how AI tools can amplify these capabilities. Through hands-on activities and case studies, professionals will learn that effective AI integration depends on their domain expertise, professional judgment, and ability to guide AI systems. The workshop positions professionals as the intelligent leaders who leverage AI’s computational power while maintaining the human insight essential for excellent professional outcomes.

Workshop Objectives

Participants will be able to:

• Integrate AI tools effectively into existing professional workflows
• Apply strategic prompting and critical evaluation skills with AI systems
• Distinguish when to rely on AI assistance versus human professional judgment
• Design AI-enhanced processes that preserve professional standards and accountability
• Navigate ethical considerations and professional responsibilities when using AI tools

Note: This workshop will be highly interactive, featuring hands-on exploration of profession-specific AI tools, collaborative case study analysis, and strategic discussions on transforming professional practice.

Session Eleven: Dan Serig

This session explores the critical role of leadership in successfully navigating AI transformation within organizations. Participants will examine how leaders can champion AI adoption while addressing organizational culture, change management, and workforce concerns. The session will cover strategic decision-making frameworks for AI investments, building AI-literate teams, and fostering innovation while maintaining ethical standards. Through case studies and interactive discussions, participants will learn how to effectively communicate the value of AI to diverse stakeholders, manage the human aspects of AI implementation, and establish governance structures that strike a balance between innovation and responsibility. Special emphasis will be placed on developing an AI vision that aligns with organizational values and preparing teams for the evolving workplace where humans and AI collaborate effectively.

Learning Objectives

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

1.Develop strategic AI leadership approaches that align AI initiatives with organizational goals and values while managing stakeholder expectations.

2.Develop frameworks for ethical AI governance that strike a balance between innovation and responsibility, ensuring AI implementations align with organizational values and societal considerations.

3.Design change management strategies for AI adoption that address workforce concerns, skill gaps, and cultural resistance while building enthusiasm for AI-augmented work.

4.Communicate AI’s impact effectively to diverse audiences, translating technical concepts into business value propositions and addressing concerns about job displacement.

5.Build and lead AI-literate teams by identifying necessary competencies, creating learning pathways, and fostering a culture of continuous adaptation and human-AI collaboration.

Session Twelve: Ted Miller

This semester, I am teaching a course titled Business Ethics, and to attract and sustain student interest, I am using the music of Alan Parsons as a case study. Parsons, known for his work with The Beatles, Pink Floyd, and the Alan Parsons Project, is an ideal figure for exploring ethical questions through art. His music frequently addresses themes such as surveillance, free will, autonomy, and human responsibility. These issues are central to both business ethics and the ethical use of emerging technologies like AI.

This module will guide students in analyzing selected songs to uncover ethical dilemmas and societal critiques. It will also teach students how to use AI tools responsibly to support interpretation and discussion, while remaining compliant with copyright laws and intellectual property standards.

Classroom Discussion Question:

Alan Parsons’ Eye in the Sky explores themes of surveillance and control, while I Robot delves into questions of free will and technological determinism.

How do these musical narratives reflect real-world ethical dilemmas in AI and business today?

In what ways can we use AI tools to analyze and interpret artistic content responsibly—without violating copyright laws or misrepresenting the artist’s intent?

Learning Objectives:

• Use music as a lens to explore ethical questions relevant to business and technology.
• Analyze Alan Parsons’ work to identify and discuss themes of surveillance, autonomy, and societal ethics.
• Understand how to use AI tools ethically in academic settings, with attention to copyright compliance.
• Apply ethical reasoning frameworks to interpret artistic content and connect it to real-world business challenges.