AI 380: Course Syllabus

Human-AI Interaction · Fall 2026

Author

Professor Fred Agbo, PhD

Published

August 23, 2026

Important

Living Document: This syllabus is subject to modification as the semester progresses — particularly the class schedule. Students will be informed of any updates via Canvas announcement and class Discord.

Course Information

Basic Course Details

Course Title AI 380: Human-AI Interaction
Semester Fall 2026
Lecture Days/Time Monday & Wednesday, 10:20 AM – 11:50 AM
Lecture Hall Ford Hall 202
Credits 4
Prerequisites CS 151 (Software Engineering experience is a plus)
Canvas willamette.instructure.com/courses/8131
Discord discord.gg/JR4yUSSP

Instructor

Professor Fred Agbo, PhD
Assistant Professor of Computer Science
School of Computing & Information Sciences

Email fjagbo@willamette.edu
Office Ford Hall 209
Office Hours Tuesday & Thursday, 10:30 AM – 12:00 PM
Website fredagbo.com
Google Scholar View Publications
Tip

Communication Policy: For course-related questions, please post on the Discord server first so all students can benefit from the answer. Reserve email for personal or sensitive matters. Include “AI 380” in the subject line of all emails.

Course Description

This course introduces the core principles of human-centered design for conversational AI systems, including chatbots, virtual assistants, and interactive voice interfaces. Students explore how natural language processing (NLP), dialogue management, and user experience (UX) principles come together to create intuitive, accessible, and inclusive AI interactions.

Through hands-on projects, students learn to design, prototype, and rigorously evaluate conversational interfaces that handle real-world user intent while accounting for system limitations, user trust, and safety.

Note

Interdisciplinary Welcome: Because of the interdisciplinary application of concepts in this course, students from all academic backgrounds are strongly encouraged to enroll — not just Computer Science majors. Different perspectives are essential to designing AI systems that serve diverse users.

Course Learning Objectives

At the end of this course, students will be able to:

  1. Critique and Synthesize Research — Review scientific literature and secondary research in human-AI interaction to synthesize trends, evaluate technological tradeoffs, and inform evidence-based design decisions.

  2. Design and Implement Conversational Systems — Apply human-centered design principles alongside natural language processing (NLP) and dialogue management frameworks to design, prototype, and build functional, context-aware conversational interfaces.

  3. Evaluate Usability and Ethics — Conduct rigorous usability testing to assess user trust, efficiency, and error recovery, while analyzing ethical risks, accessibility constraints, and failure modes inherent to conversational AI.

  4. Collaborate and Communicate Technical Outcomes — Work effectively in multidisciplinary teams to manage interactive AI projects, clearly communicating project outcomes, system architectures, and findings through professional presentations and technical reports.

Required Materials

Textbooks

Note

Weekly readings will be provided as PDFs or links via Canvas. No single textbook purchase is required. See the Schedule page for the complete list of assigned weekly materials.

Tools and Technology

Tool Purpose Cost
Discord Course communication Free
Canvas Assignments, slides, grades Free (Willamette login)
Google Site Project journal for reporting Free
Google Drive Shared project documents Free
GitHub Project repository Free
Figma Wireframing and prototyping Free (Education)

Course Structure and Assessment

This course consists of lectures, class activities and discussions, individual weekly readings and homework, group projects, a mid-term presentation, and a final project report with presentation.

1. Lectures

Lectures and class activities will be held every Monday and Wednesday from August 24 through December 4, 2026, except for university holidays. Slides and resources are accessible from the Canvas course page. Full class attendance and participation are mandatory and are graded.

2. Weekly Readings and Homework

Weekly readings are to be completed individually before class to prepare for discussions. Class activities may include:

  • Short quizzes or reading responses
  • Open dialogue and Socratic discussion
  • Collaborative in-class design exercises
  • “Teaser” activities to connect readings to real AI systems

There will also be 3–4 individual homework assignments and one individual short project throughout the semester.

3. Group Project

This course centers on two major group project requiring 2–3 members per group. The project works students through the full UX design process — from need-finding through usability evaluation — within a chosen HCI genre. There will be four formal deliverables (see Project for full details including deliverables, grading rubrics, and due dates):

4. Mid-term Quizes

In week 7, there will be an individual mid-term quizes to test students understanding of contents and concepts discussed in the class and from the readings. The

5. Final Course Reflection Essay

The course ends with an individual reflection essay in which students describe concrete learning outcomes and connect course concepts to broader themes in AI design and society.

Grading

Grade Allocation

Component Weight
Active Class/Lab/Workshop Participation 20%
Homework / Personal Project 20%
Mid-term Quizzes 10%
Group Project 40%
→ GP1: VR Interventions for Older Adults 10%
→ GP2: Needfinding, Personas & Workflow 10%
→ GP3: System Implementation 10%
→ GP4: Deployment, Usability Testing & Final Docs 10%
Final Course Reflection Essay 10%
Total 100%

Grade Distribution

Score Grade
≥ 95.00 A
90.00 – 94.99 A–
85.00 – 89.99 B+
80.00 – 84.99 B
75.00 – 79.99 B–
60.00 – 74.99 C
< 60.00 F

Participation Grading

Participation is graded based on:

  • Engagement in in-class discussions, activities, and peer critiques
  • Demonstrating preparation through readings
  • Contributing meaningfully to group activities
  • Professional and respectful interaction with peers

Students who do not actively participate in group activities (including the project) will lose the grade for that activity.

Time Commitment

Willamette’s Credit Hour Policy holds that for every hour of class time, there is an expectation of 2–3 hours of work outside of class. For this 4-credit course with approximately 3 lecture hours per week, students should anticipate spending 6–9 hours outside of class per week on:

  • Reading course materials
  • Preparing for discussions and activities
  • Working on group projects
  • Preparing and writing reports or presentations
  • Individual homework

Course Policies

Collaboration

Collaboration is an integral part of this class and is highly encouraged both during and outside of the classroom. Students may work together to prepare for reviews and class discussions. All submitted work must clearly identify individual contributions for group deliverables.

Attendance

Students are responsible for all missed work, regardless of the reason for absence. It is also the absentee’s responsibility to get all missing notes or materials from classmates. If you know you will be absent, contact the instructor before class.

Late Submission Policy

Warning

No Late Submissions Without Prior Approval. If extenuating circumstances arise, contact the instructor immediately and before the deadline via email. No lateness beyond 24 hours will be tolerated without explicit instructor approval. Penalties (loss of grade points) may apply at instructor discretion.

The procedure: 1. Email the instructor before the deadline explaining the situation 2. Receive a written reply confirming any extension 3. No extension is valid without a written reply

Academic Honesty

Cheating is defined as any form of intellectual dishonesty or misrepresentation of one’s knowledge. Plagiarism — intentionally or unintentionally representing someone else’s work as your own — is strictly prohibited.

Penalties may range from a grade reduction on an assignment to failing the course. An instructor can also involve the Office of the Dean of the School of Computing and Information Sciences for further action. For further information, read the School of Computing and Information Science students handbook and also see the Willamette Academic Integrity Policy for full details.

Regarding AI Tools: Students are expected to disclose any use of AI writing or coding assistants. For design projects, AI-assisted prototyping tools are permitted and encouraged as part of learning the ecosystem. AI-generated text submitted as original writing without disclosure is a violation of academic integrity.

Intellectual Property & Privacy

Class materials and discussions, including recorded lectures, are for the sole purpose of educating enrolled students. Recording or sharing class content without explicit instructor permission constitutes an Honor Code violation and may violate state and federal law, including the Copyright Act.

University Policies

Accommodations and Disability Services

Willamette University values diversity and inclusion. If there are aspects of instruction or course design that result in barriers to your inclusion or accurate assessment, please notify the instructor as soon as possible. Students with disabilities are also encouraged to contact:

Commitment to Positive Sexual Ethics

Title IX and Willamette policy prohibit discrimination on the basis of sex. As a mandatory reporter, the instructor is required to report disclosures of sexual misconduct to the Title IX Coordinator. Confidential support is available via:

SOAR Center

The SOAR Center provides free, confidential access to food, toiletries, clothing, textbooks, and scholarly resources for all Willamette students (Putnam UC, 3rd floor).

Land Acknowledgement

We are gathered on the land of the Kalapuya, today represented by the Confederated Tribes of the Grand Ronde and the Confederated Tribes of the Siletz Indians. We offer gratitude for the land itself and for those who have stewarded it for generations. We acknowledge our University’s history as fundamentally tied to the first colonial developments in the Willamette Valley.

DACA / Undocumented Students

Willamette is committed to supporting DACA/undocumented students. For resources, contact:
Olivia Muñoz — omunoz@willamette.edu · 3rd Floor UC · 503-370-6447

Semester Calendar Overview

For the detailed week-by-week schedule including all readings and due dates, see the Schedule page.

Module Weeks Dates Theme
Module 1 1–4 Aug 24 – Sep 16 Human-AI Interaction & User-Centered Design
Module 2 5–8 Sep 21 – Oct 14 Human-AI Collaborations
Module 3 9–11 Oct 19 – Nov 4 AI Design & Conversational Orchestration
Module 4 12–15 Nov 9 – Dec 2 UX, Usability Study, Report & Presentation

Key Dates:

Date Event
Aug 24 First day of class
Aug 28 HW0 Due (Canvas Discussion)
Sep 4 Last day for Add/Drop
Sep 5 HW1 Due
Sep 7 Labor Day — No class
Sep 18 HW2 Due
Sep 23 Last day to withdraw (partial semester)
Sep 25 GP0 Due — Team Charter & Website
Oct 2 GP1 Due — VR Interventions
Oct 7 Midterm Quiz (in class)
Oct 9 Mid-semester break — No class
Oct 16 GP2 Due — Needfinding, Personas & Workflow
Oct 23 Last day to withdraw (full semester)
Nov 13 HW3 Due
Nov 20 GP3 Due — System Implementation
Nov 25–27 Fall Break — No class
Nov 30 & Dec 2 Final Project Presentations
Dec 2 Final Reflection Essay Due
Dec 3 GP4 Due — Public Deployment & Final Docs
Dec 4 Last day of class
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