Instructor’s Manual:
Chapter 1—Overview of AI in Business
Table of Contents
1. Chapter Summary
2. Learning Objective Section
a. Quiz Questions
b. Discussion Prompts
c. In-Class Activities
d. Homework Assignments
e. Pedagogical Strategies
f. Common Student Misconceptions
3. Professional Learning Courses
Chapter Summary
This chapter introduces the core types of AI, machine learning, neural networks, and
generative AI, and their business applications. It explores how AI enhances tools like VR,
computer vision, and speech recognition across industries. The importance of data science
is emphasized, especially in ensuring AI accuracy and fairness. Ethical concerns such as data
privacy, environmental impact, and the global AI divide are critically examined.
MODULE PREVIEW AND LEARNING OBJECTIVES
LO1-1: Types of AI—Questions
In-Class Quiz Questions
• What are the three main classifications of AI discussed in this chapter?
• How does machine learning differ from traditional programming?
• What is backpropagation in neural networks?
• What role does generative AI play in business?
Discussion Prompts
• How does Airbnb’s use of machine learning reflect ethical responsibility?
o Airbnb uses machine learning algorithms to detect fraudulent listings, fake
reviews, and suspicious user activity.
©
, • The company actively works to prevent bias within its machine
learning systems, monitoring, and refining algorithms to reduce
discrimination and promote fair outcomes in recommendations,
pricing, and search results.
• Airbnb is committed to user data privacy, implementing compliance
measures such as GDPR and informing users how their data and
algorithmic decisions affect their experience.
• In what ways do neural networks mimic human cognition?
o Neural networks share a hierarchical structure like the brain; layers process
raw sensory input through increasingly abstract representations,
resembling how different regions in the brain manage information.
o Learning in neural networks involves adapting weights based on experience,
mirroring how human brains modify synaptic connections over time in
response to experiences.
o Neural networks perform pattern recognition, generalization, and inference
like human thought processes.
• Should generative AI outputs be considered original intellectual property?
o Most legal frameworks state that works produced solely by generative AI,
without meaningful human input, do not qualify for copyright.
o Some legal experts suggest that if a user, designer, or producer contributes
to the output, such as through creative prompts or editing, the result may be
eligible for IP protection.
o The lack of clear IP protection for purely AI-generated works raises practical
issues around control, attribution, and commercial exploitation.
In-Class Activities
Activity 1: AI Classification Match-Up
• Provide students with real-world applications and have them match each to
machine learning, neural networks, or generative AI (see example table below).
©
, Real-World Application Category (ML, NN, or GenAI)
Email spam filtering Machine Learning
Self-driving car recognizing pedestrians Neural Networks
and traffic lights
Chatbots that write stories or answer Generative AI
questions in natural language
Credit card fraud detection Machine Learning
Facial recognition unlocking your phone Neural Networks
Creating realistic images of people who Generative AI
don’t exist
Predicting house prices based on features Machine Learning
like size and location
Voice assistants converting speech to text Neural Networks
AI tools that generate music, art, or videos Generative AI
Activity 2: Mini Case Study
• Airbnb employs machine learning models to flag and block potentially high-risk
reservations. For this activity, analyze Airbnb’s anti-party initiative and discuss its
impact on trust and safety.
Homework Assignments
Assignment 1: Compare and Contrast Types of AI
Write a detailed report (300–500 words) describing and comparing the three main types of
AI based on capability: Narrow AI, General AI, and Superintelligent AI.
For each type, provide:
• A definition in your own words.
• At least two real-world or theoretical examples.
• A discussion of their differences and why General and Superintelligent AI are still
largely theoretical.
Assignment 2: Case Study Analysis—AI in Daily Life
Choose three everyday technologies or applications that use different types of AI.
For each application:
©
, • Identify which type of AI it represents (from types based on capability or
functionality).
• Explain how the AI works in that context.
• Discuss the benefits and any ethical considerations relevant to its use.
Pedagogical Strategies Common Student Misconceptions
Interactive Learning Confusing all AI as the same thing
Use Synthesia or InVideo to create short, Assumptions are that machine learning,
AI Instructor avatar videos to help neural networks, and generative AI are
students understand at least one aspect unrelated categories. Actually, they are
nested and overlapping.
of generative AI. Encourage students to
create their own using these tools as
well.
LO1-2: Applications of AI in Business
In-Class Quiz Questions
• Name three industries where AI-powered VR is making an impact.
• What is computer vision and how does Blue River Technology use it?
• How does speech recognition enhance customer service?
Discussion Prompts
• How might AI in VR change the future of education?
o Personalized Immersive Learning: AI can adapt VR lessons in real time to
each student’s pace, style, and comprehension level.
(https://transfrinc.com/)
o Enhanced Engagement & Accessibility: AI powered VR can simulate real-
world environments (labs, field trips, workplaces) that are otherwise
inaccessible due to cost, distance, or safety.
(https://www.victoryxr.com/vxrlabs/)
o Smart Virtual Tutors & Assessment: AI-driven avatars and assistants
within VR can guide students, answer questions, provide instant feedback,
and track performance data, allowing for continuous assessment and
improved outcomes. (https://engagevr.io/school-of-ai/)
• What are the environmental and economic implications of computer vision in
agriculture?
©
Chapter 1—Overview of AI in Business
Table of Contents
1. Chapter Summary
2. Learning Objective Section
a. Quiz Questions
b. Discussion Prompts
c. In-Class Activities
d. Homework Assignments
e. Pedagogical Strategies
f. Common Student Misconceptions
3. Professional Learning Courses
Chapter Summary
This chapter introduces the core types of AI, machine learning, neural networks, and
generative AI, and their business applications. It explores how AI enhances tools like VR,
computer vision, and speech recognition across industries. The importance of data science
is emphasized, especially in ensuring AI accuracy and fairness. Ethical concerns such as data
privacy, environmental impact, and the global AI divide are critically examined.
MODULE PREVIEW AND LEARNING OBJECTIVES
LO1-1: Types of AI—Questions
In-Class Quiz Questions
• What are the three main classifications of AI discussed in this chapter?
• How does machine learning differ from traditional programming?
• What is backpropagation in neural networks?
• What role does generative AI play in business?
Discussion Prompts
• How does Airbnb’s use of machine learning reflect ethical responsibility?
o Airbnb uses machine learning algorithms to detect fraudulent listings, fake
reviews, and suspicious user activity.
©
, • The company actively works to prevent bias within its machine
learning systems, monitoring, and refining algorithms to reduce
discrimination and promote fair outcomes in recommendations,
pricing, and search results.
• Airbnb is committed to user data privacy, implementing compliance
measures such as GDPR and informing users how their data and
algorithmic decisions affect their experience.
• In what ways do neural networks mimic human cognition?
o Neural networks share a hierarchical structure like the brain; layers process
raw sensory input through increasingly abstract representations,
resembling how different regions in the brain manage information.
o Learning in neural networks involves adapting weights based on experience,
mirroring how human brains modify synaptic connections over time in
response to experiences.
o Neural networks perform pattern recognition, generalization, and inference
like human thought processes.
• Should generative AI outputs be considered original intellectual property?
o Most legal frameworks state that works produced solely by generative AI,
without meaningful human input, do not qualify for copyright.
o Some legal experts suggest that if a user, designer, or producer contributes
to the output, such as through creative prompts or editing, the result may be
eligible for IP protection.
o The lack of clear IP protection for purely AI-generated works raises practical
issues around control, attribution, and commercial exploitation.
In-Class Activities
Activity 1: AI Classification Match-Up
• Provide students with real-world applications and have them match each to
machine learning, neural networks, or generative AI (see example table below).
©
, Real-World Application Category (ML, NN, or GenAI)
Email spam filtering Machine Learning
Self-driving car recognizing pedestrians Neural Networks
and traffic lights
Chatbots that write stories or answer Generative AI
questions in natural language
Credit card fraud detection Machine Learning
Facial recognition unlocking your phone Neural Networks
Creating realistic images of people who Generative AI
don’t exist
Predicting house prices based on features Machine Learning
like size and location
Voice assistants converting speech to text Neural Networks
AI tools that generate music, art, or videos Generative AI
Activity 2: Mini Case Study
• Airbnb employs machine learning models to flag and block potentially high-risk
reservations. For this activity, analyze Airbnb’s anti-party initiative and discuss its
impact on trust and safety.
Homework Assignments
Assignment 1: Compare and Contrast Types of AI
Write a detailed report (300–500 words) describing and comparing the three main types of
AI based on capability: Narrow AI, General AI, and Superintelligent AI.
For each type, provide:
• A definition in your own words.
• At least two real-world or theoretical examples.
• A discussion of their differences and why General and Superintelligent AI are still
largely theoretical.
Assignment 2: Case Study Analysis—AI in Daily Life
Choose three everyday technologies or applications that use different types of AI.
For each application:
©
, • Identify which type of AI it represents (from types based on capability or
functionality).
• Explain how the AI works in that context.
• Discuss the benefits and any ethical considerations relevant to its use.
Pedagogical Strategies Common Student Misconceptions
Interactive Learning Confusing all AI as the same thing
Use Synthesia or InVideo to create short, Assumptions are that machine learning,
AI Instructor avatar videos to help neural networks, and generative AI are
students understand at least one aspect unrelated categories. Actually, they are
nested and overlapping.
of generative AI. Encourage students to
create their own using these tools as
well.
LO1-2: Applications of AI in Business
In-Class Quiz Questions
• Name three industries where AI-powered VR is making an impact.
• What is computer vision and how does Blue River Technology use it?
• How does speech recognition enhance customer service?
Discussion Prompts
• How might AI in VR change the future of education?
o Personalized Immersive Learning: AI can adapt VR lessons in real time to
each student’s pace, style, and comprehension level.
(https://transfrinc.com/)
o Enhanced Engagement & Accessibility: AI powered VR can simulate real-
world environments (labs, field trips, workplaces) that are otherwise
inaccessible due to cost, distance, or safety.
(https://www.victoryxr.com/vxrlabs/)
o Smart Virtual Tutors & Assessment: AI-driven avatars and assistants
within VR can guide students, answer questions, provide instant feedback,
and track performance data, allowing for continuous assessment and
improved outcomes. (https://engagevr.io/school-of-ai/)
• What are the environmental and economic implications of computer vision in
agriculture?
©
