Fall 2026

Artificial Intelligence in Story and Screen

COMARTS 110 explores how AI gains meaning through stories, examining media narratives that shape technology and human perception.

offering recorded3 credits
Recorded instructors · Fall 2026 Derek Johnson3.6/5

Summary

Lectures are mid-sized, with a median of 99 enrolled students in the recorded sections.

Section enrollment · Fall 2026 snapshot ↗

Grade history

average GPA
letter grades

No recorded grade history.

All recorded terms · compare terms & instructors

Prerequisites

Course map

None

  • None
COMARTS 110

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • None

Professors

Fall 2026
/5Adjusted rating
/5RMP difficulty
captured reviews
About this rating

Raw average: 3.63/5 from 43 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

RMP profile ↗ · All captured review dates; profile matched by name.

No course-specific feedback yet.

Historical instructors & teaching patterns

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

Calendar & sections

Fall 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction99 / 1000

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

Meeting source records

Student experience

the class

Explore how artificial intelligence (AI) gains meaning through the stories we tell about it. Through close readings of both nonfiction texts and science fiction narratives, examine how our AI-driven culture is shaped by the media texts that structure, theme, and provide perspective on new technologies. Learn how film, television, and other popular forms of media narrative are powerful forms of literature that influence the development and understanding of AI technologies - and how those visions in turn shape our perceptions of humanity.

difficulty & workload

No workload feedback recorded.

Topics

  • Science fiction narratives
  • Film and television media
  • AI-driven culture
  • Perceptions of humanity

Skills

  • Close reading of nonfiction and science fiction texts
  • Examining the influence of media on cultural perceptions
  • Analyzing popular media as literature

Grades

Instructor

Grade distribution · % of letter grades

No grades available yet.

More grade details Grade mix, volume & source data

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.

Selected offering source records
COMARTS 110 · Fall 2026

Artificial Intelligence in Story and Screen

Recorded 2026-09-07
Raw records
[
  {
    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
    "observed_at": "2026-09-07 15:55:43.033547+00:00",
    "offering_id": "1272:250:027333",
    "course_id": "COMARTS 110",
    "course_uid": "course_1a95d09dffbc5341c5d903d4",
    "term_id": "1272",
    "source_course_id": "027333",
    "source_subject_id": "250",
    "title": "Artificial Intelligence in Story and Screen",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews

No records available.

Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Model & dataset provenance
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "2388d1627d4538933212bdf69b8462c5e2fb96cc3ab8d15ccf0091c1e35d6496",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}