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TU-A101 Syllabus

AI101 Course Syllabus

Instructor Information

Dr. Theodore Ulysseys Jackson

Position: Professor

Email: TUJ@temple.edu

Office: City Campus Faculty Office 509C

Phone: +81 0019 2834

Course Information

Location and Time

Location: City Campus, Room 302

Time: Tuesday and Thursday 13:30 to 14:50

Course Description

Introduction to Artificial Intelligence (AI101) provides a non-technical, interdisciplinary foundation in artificial intelligence (AI). Designed for introductory students, this course demystifies how modern AI systems work, explores the history and rapid evolution of machine learning, and examines AI’s societal, ethical, and economic impact.

AI101 Learning Goals

Define Core Concepts: Explain key AI terminology (machine learning, deep learning, NLP, computer vision, generative AI) in plain language.
Evaluate Tool Capabilities: Understand the practical strengths and limitations of current AI models.
Analyze Societal & Ethical Impacts: Critically discuss bias, privacy, intellectual property, workforce automation, and global AI governance.
Contextualize Regional Trends: Examine how Japan integrates AI and automation in response to demographic shifts and industrial strategies.
Apply Practical AI Workflows: Utilize modern generative AI platforms effectively through responsible prompt engineering and no-code tools.

Message from Dr. Jackson

Provide an ecouraging and enthusiastic message to students.

Key Assignments

Major Assignments for Fall 2026

Below are the details for the major assignments this term.

Example

Overview

What do students need to know about this assignment? What course content, resources, and skills are important for the successful completion of of the assignment? What are the expectations for the assignment?

Deliverable

What do students need to sumbit? How is the assignment structured and/or scaffolded? What are the due dates and recommended completion dates to stay on task? Where should students submit?

Weekly Homework and Reflections

Overview

Brief, hands-on weekly assignments designed to bridge theory and practice. Each week, students complete a short technical or conceptual prompt—such as experimenting with a specific prompt engineering technique, testing image generation parameters, or evaluating search AI tools.

Deliverable

A 250–400 word reflection log submitted via Canvas analyzing the output, noting model limitations or "hallucinations," and connecting the hands-on experience to the week’s reading.

Midterm Case Study

Overview

A deep-dive written assignment that evaluates a real-world AI implementation or ethical controversy. Students select a specific topic from a provided prompt list (e.g., algorithmic bias in hiring, facial recognition privacy concerns, or intellectual property disputes in generative art).

Deliverable

A 1,000–1,200 word structured essay assessing the problem through technical, ethical, and regulatory lenses, offering reasoned recommendations for governance or improvement.

Final Project

Overview

A collaborative final project where teams of 3–4 students design an AI-driven solution or policy framework to address a real-world issue, with a strong suggestion to leverage Japan-specific contexts (e.g., aging demographics, hospitality automation, or language translation in tourism).

Deliverable

Interactive Demo / Proposal: A working no-code/low-code AI workflow demo (e.g., a custom GPT, a workflow diagram, or a Teachable Machine model) accompanied by a 1,500–2,000 word project proposal.

In-Class Presentation: A 10-minute presentation with slides during Week 14, followed by a brief Question and Answer session with the instructor and peers.

Course Grading Scheme

Fall 2026

AI101 Grading Scheme
Percent Range	Letter Grade
97-100%	A+
93-96.9%	A
90-92.9%	A-
87-89.9%	B+
83-86.9%	B
80-82.9%	B-

Course Calendar

Fall 2026

AI101 Fall 2026 Course Calendar
Week	Topic	Activity & Content
1	Introduction to AI: What is Intelligence?	History of AI, Turing Test, syllabus review.
2	How AI Thinks: Data, Pattern Recognition & Rule-Based Systems	Lab 1: Rule-based logic vs. probabilistic models.
3	Machine Learning Basics: Supervised vs. Unsupervised Learning	Understanding training data, overfitting, and features.
4	Deep Learning & Neural Networks: Simple Mental Models	Visualizing neural networks with Google Teachable Machine.
5	Natural Language Processing (NLP) & LLMs	How Large Language Models predict tokens and output text.
6	Generative AI & Prompt Engineering	Lab 2: Effective prompting, system instructions, and chain-of-thought.

For inclement weather and other extenuating circumstances, please refer to university notices and instructor commmunications for guidance.

Student Support at TUJ

Academic Success and Support

TUJ provides guidance, tools, and meaningful support so students can thrive academically, build confidence, and stay on track to graduate.

Schedule an Appointment

Schedule and appointment with the Director of Academic Success to discuss your academic strugles and plan strategies for success.

Accessibility Services

Temple University, Japan Campus (TUJ) is committed to inclusion and diversity. We want to ensure all students have the support they need to learn. Accessibility Services (also known as Disability Resources and Services/DRS) ensures that students with particular educational needs can access the full university experience.

Contact Temple University, Japan Accessibility Services Office

Email:tujaccessibility@tuj.temple.edu
Office Hours: Monday-Friday 9:00-17:00 by appointment
Drop-In Hours: Drop-in hours are a great time to ask a quick question without needing to make an appointment!