Spring 2027

Introduction to Artificial Intelligence in Business

Introduces foundational AI concepts and business applications, covering predictive AI, deep learning, generative AI, tool usage, and responsible AI.

offering recorded1 credits
Recorded instructors · Spring 2027 Katie Gaertner

Summary

1 / 3

Recent recorded grades — Spring 2026: 3.76 GPA, 87.8% A/AB (n=41 letter grades).

What you’ll be able to do

  • Explain the basic principles of AI and machine learning.

  • Describe how AI systems learn, process data, and make decisions.

  • Discuss the application of AI in various business fields, and the resulting disruption.

  • Articulate ethical considerations and challenges surrounding AI.

  • Apply AI tools in both personal and professional settings to solve problems, meet user needs, and identify opportunities in business.

Source: UW Guide

Grade history

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average GPA
letter grades
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All recorded terms · compare terms & instructors

Prerequisites

Course map

None

  • None
GENBUS 107 used by

“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • None

Professors

Spring 2027

Recent recorded grades — Spring 2026: 3.76 GPA, 87.8% A/AB (n=41 letter grades).

Historical instructors & teaching patterns

KATIE GAERTNER is recorded teaching in Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Online Only0 / 3250
LEC 002Online Only0 / 1700
LEC 003Online Only0 / 1550

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records

No records available.

Student experience

the class

Recent recorded grades — Spring 2026: 3.76 GPA, 87.8% A/AB (n=41 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Predictive AI, deep learning, generative AI, leveraging AI tools, and responsible AI.
  • What AI is, how it works, and its impact on business contexts.
  • Ethical considerations and challenges surrounding AI.

Skills

  • Applying AI tools to solve problems and identify opportunities in business.
  • Articulating ethical considerations and challenges surrounding AI.
  • Discussing AI applications and disruption across business fields.

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F
More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1280 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

Download part 1
Selected offering source records
GENBUS 107 · Spring 2027

Introduction to Artificial Intelligence in Business

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:231:027163",
    "course_id": "GENBUS 107",
    "course_uid": "course_cc7645901d20b6a2881474b1",
    "term_id": "1274",
    "source_course_id": "027163",
    "source_subject_id": "231",
    "title": " Introduction to Artificial Intelligence in Business",
    "credits_min": 1,
    "credits_max": 1,
    "typically_offered": "Not Applicable"
  }
]
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Student reviews

Original comments behind the course and instructor summaries.

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No records available.

Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "fab5725d1d0c5dfc6fa4dcebf3dfb47eedd891aa746766a38c151e4010b40b51",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}