Syllabus

General Information

Course Information

This is an approved Science General Education distributive course.

This course introduces students from all disciplines to the fundamentals of artificial intelligence (AI) by examining how ideas from natural intelligent systems such as the human brain and biological perception inspire the design of AI systems. We will explore core AI concepts including pattern recognition, image processing, language models, and decision-making, with an emphasis on practical understanding rather than technical depth.

The students will engage in interactive, no-code labs using modern AI tools, including large language models and prompt engineering, to learn how these systems work and how to interact with them effectively. As a Science Distributive course, it emphasizes observation, data collection, analysis, and the role of theory and falsifiability in understanding AI and its potential integration into other scientific disciplines. The course also examines the ethical implications of AI, including fairness, transparency, and societal impact. There is no prerequisite for this course.

Learning Objectives

Course Student Learning Outcomes (CSLO)

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

  1. Apply the scientific method to evaluate artificial intelligence systems (formulate hypotheses, collect data, test, analyze, replicate).
  2. Employ quantitative methods (e.g. confusion matrices, accuracy/recall, threshold trade-offs) to examine AI behavior.
  3. Explain connections between natural intelligent systems (e.g., neurons, perception) and artificial models, recognizing both inspirations and limitations.
  4. Demonstrate effective communication of AI concepts and results through written reports and oral presentations.
  5. Critically evaluate AI claims by distinguishing scientific evidence from pseudoscience or unsupported assertions.
  6. Analyze ethical and societal implications of AI, including fairness, bias, safety, and human-AI collaboration.

Applicable Programmatic Student Learning Outcomes (PSLO):

  1. Students will gain foundational literacy in AI concepts without requiring technical prerequisites, supporting broader CS program goals of exposing non-majors to computing.
  2. Students will develop evidence-based reasoning skills transferable to other scientific and quantitative disciplines.
  3. Students will practice ethical reasoning about computing technologies, aligning with CS program emphasis on computing and society.

Applicable General Education Student Learning Outcomes (GSLO):

  1. Goal #1: Communicate effectively
    • 1a. Express oneself effectively in common college-level written forms.
    • 1c. Express oneself effectively in presentations.
    • 1d. Demonstrate comprehension of and ability to explain information and ideas accessed through reading.
  2. Goal #2: Think critically and analytically
    • 2a. Use relevant evidence gathered through accepted scholarly methods.
    • 2b. Construct and/or analyze arguments, considering assumptions and counterarguments.
    • 2c. Reach sound conclusions based on logical analysis of evidence.
  3. SCIENCE Goal #3: Employ quantitative concepts and mathematical methods
    • 3a. Employ quantitative methods to examine a problem in the natural or physical world.
    • 3b. Apply the basic methods and through processes of the scientific method for natural/physical science in a particular discipline.

Prerequisites

There is no prerequisite for this course

Required Text (either print or e-book):

Course Topics and Tentative Schedule

Course Topics

Tentative Schedule

Tentative Course Outline

Week Topic and Readings Lab/Activity Assessments
1 What Counts as Intelligence?
- Mollick, Introduction: Three Sleepless Nights
In-class falsifiability exercise Quiz 1
2 Brains, Neurons, and Signals
- Mollick, Ch. 1: Creating Alien Minds
- The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain
- Professor’s perceptron paved the way for AI - 60 years too soon
Neuron Simulation Quiz 2
3 Machines that Learn from Patterns
- Hendrycks, Ch. 2: Artificial Intelligence Fundamentals
TensorFlow Playground Quiz 3
4 Seeing Like a Machine
- Olah, C., Mordvintsev, A., & Schubert, L. (2017). Feature Visualization. Distill.
- Zeiler, M. D., & Fergus, R. (2014). Visualizing and Understanding Convolutional Networks. ECCV 2014.
CNN Explainer Quiz 4
5 Collecting and Questioning Data
- Mollick, Ch. 5: AI as A Creative
- Hendrycks, Ch. 6: Bias and Fairness
- Google Inclusive Images Challenge
Teachable Machine Quiz 5
Essay 1 Assigned
6 Living with Uncertainty
- Mollick, Ch. 5: AI as A Creative (cont.)
Threshold adjustment on Teachable Machine outputs (analyze true positive, false positive, false negative, true negative in Excel) Quiz 6
7 Talking with Machines
- Mollick, Ch. 6: AI as Co-worker
- (Brief) Mollick, Ch. 3: Four Rules for Co-Intelligence
- (Revisit) Mollick, Ch. 1: Creating Alien Minds
Google ngrams Quiz 7
Essay 1 Due
8 From Chatbots to Storytellers
- Mollick, Ch. 5: AI as A Creative (cont.)
- Google: Attention is All You Need
Transformer Explainer Quiz 8
9 Prompt Crafting 101
- Mollick, Ch. 3: Four Rules of Co-Intelligence
- Hendrycks, Ch. 6: Bias/Fairness
OpenAI Playground Quiz 9
Essay 2 Assigned
10 Teaching AI to Follow Your Lead
- Mollick, Ch. 6,7,8: AI as A …
Claude AI Quiz 10
11 When Humans and AI Work Together
- Towards an AI co-scientist
- Advancising AI-Scientist Understanding: Making LLM Think Like a Physicist with Interpretable Reasoning
Human versus AI labeling in Google Sheets (inter-rater reliability) Quiz 11
Essay 2 Due
12 AI in Our Lives
- Mollick, Ch. 8: AI as Our Future
- (Revisit) Mollick, Ch. 5, 6, 7.
Small-group case study debate prep Quiz 12
13 Can AI Fail Safely
- Hendrycks, Ch. 3: Single-Agent Safety
- Hendrycks, Ch. 4: Safety Engineering
Inducting an AI Hallucination Quiz 13
14 What’s Next for AI?
- Mollick, Ch. 8: AI As Our Future
- Hendrycks, Ch. 7: Collective Action Problems
Demo of small LLM on personal computers Quiz 14
15 Debate and Reflection
- Mollick, Epilogue
- Hendrycks, Ch. 8: Governance
In-class group debate/presentation (continue into Final Exam time) Quiz 15
Group Position Paper Due

Quizzes are typically disseminated once a week.

Evaluation Policy:

Method of Assessment

Assessment % of Final Grade
Essays 20%
Lab 40%
Quizzes 20%
Group Debates and Position Paper 20%

Assessing Student Learning Outcomes:

  1. In-class weekly quizzes (20%)
    • Purpose: Ensure reading comprehension and reinforce weekly concepts
    • Mapped Outcomes:
      • CSLO: 4, 5
      • GSLO: 1d
  2. Labs (5 labs total, 8% each: 40%)
    • Students submit short reports (hypothesis, data/graph, reflection) from selected labs.
    • Week 3: TensorFlow Playground (Decision Boundaries)
      • CSLO: 1, 2, 3
      • GSLO: 3a, 3b, 2c.
    • Week 5: Teachable Machine (Classifier and Bias)
      • CSLO: 1, 2, 5
      • GSLO: 3a, 3b, 2a.
    • Week 6: Threshold and ROC (Receiver Operating Characteristics)
      • CSLO: 1, 2
      • GSLO: 3a, 2c
    • Week 7: Text Prediction (N-grams)
      • CSLO: 3, 4
      • GSLO: 1d, 2a
    • Week 10: Persona Prompt Engineering
      • CSLO: 4, 6
      • GSLO: 1a, 1c, 2b
  3. Essays (2 essays, 10% each: 20%)
    • Structured essays (3-4 pages) evaluating AI in applied domains (e.g., health, environment, education).
    • Mapped Outcomes:
      • CSLO: 4, 5, 6
      • GSLO: 1a, 1d, 2a, 2b, 2c.
  4. Group Debate and Position Paper (20%)
    • Small groups take a position on a societal AI issue, present in a short in-class debate (5-10 min) and submit a 3-4 page position paper.
    • Mapped Outcomes:
      • CSLO: 4, 5, 6
      • GSLO: 1a, 1c, 2a, 2b, 2c.

Grade Scale:

Grade Quality Points Numeric Interpretation

Refer to the Grading Information section section of the Undergraduate Catalog for description of NG (No Grade), W, Z, and other grades.

Lateness Policy:

Assignments that are late are assessed a 10% per day late penalty. Saturday and Sunday are each days.

University Policies

Academic & Personal Integrity

It is the responsibility of each student to adhere to the university’s standards for academic integrity. Violations of academic integrity include any act that violates the rights of another student in academic work, that involves misrepresentation of your own work, or that disrupts the instruction of the course. Other violations include (but are not limited to): cheating on assignments or examinations; plagiarizing, which means copying any part of another’s work and/or using ideas of another and presenting them as one’s own without giving proper credit to the source; selling, purchasing, or exchanging of term papers; falsifying of information; and using your own work from one class to fulfill the assignment for another class without significant modification. Proof of academic misconduct can result in the automatic failure and removal from this course. For questions regarding Academic Integrity, the No-Grade Policy, Sexual Harassment, or the Student Code of Conduct, students are encouraged to refer to the Department Undergraduate Handbook, the Undergraduate Course Catalog, the Ram’s Eye View, or the University Website.

Accomodations for Students with Disabilities

West Chester University is committed to providing equitable access to the full WCU experience for Golden Rams of all abilities. Students should contact the Office of Educational Accessibility (OEA) to establish accommodations if they have had accommodations in the past or if they believe they may be eligible for accommodations due to a disability, whether or not it may be readily apparent. There is no deadline for disclosing to OEA or for requesting to use approved accommodations in a given course. However, accommodations can only be applied to future assignments or exams; that is, they can’t be applied retroactively. Please share your letter from OEA as soon as possible so that we can discuss accommodations. If you have concerns related to disability discrimination, please contact the university’s ADA Coordinator in the Office of Diversity, Equity, and Inclusion or 610-436-2433

The University’s Americans with Disabilities policy is available on the website. If you encounter an area of this course that is not accessible to you, please contact me.

University-Excused Absences Policy

Students are advised to carefully read and comply with the University-Excused Absences Policy, including absences for university-sanctioned events, contained in the WCU Undergraduate Catalog. In particular, please note that the responsibility for meeting academic requirements rests with the student, that this policy does not excuse students from completing required academic work, and that professors can require a fair alternative to attendance on those days that students must be absent from class in order to participate in a University-Sanctioned Event.

Reporting Incidents of Sexual Violence

West Chester University and its faculty are committed to assuring a safe and productive educational environment for all students. In order to comply with the requirements of Title IX of the Education Amendments of 1972 and the University’s commitment to offering supportive measures in accordance with the new regulations issued under Title IX, the University requires faculty members to report incidents of sexual violence shared by students to the University’s Title IX Coordinator. The only exceptions to the faculty member’s reporting obligation are when incidents of sexual violence are communicated by a student during a classroom discussion, in a writing assignment for a class, or as part of a University-approved research project. Faculty members are obligated to report sexual violence or any other abuse of a student who was, or is, a child (a person under 18 years of age) when the abuse allegedly occurred to the person designated in the University Protection of Minors Policy. Information regarding the reporting of sexual violence and the resources that are available to victims of sexual violence is set forth at the WCUPA Sexual Misconduct website.

Inclusive Learning Environment and Anti-Racist Statement

Diversity, equity, and inclusion are central to West Chester University’s mission as reflected in our Mission Statement, Values Statement, Vision Statement and Strategic Plan: Pathways to Student Success. We disavow racism and all actions that silence, threaten, or degrade historically marginalized groups in the U.S. We acknowledge that all members of this learning community may experience harm stemming from forms of oppression including but not limited to classism, ableism, heterosexism, sexism, Islamophobia, anti-Semitism, and xenophobia, and recognize that these forms of oppression are compounded by racism.

Our core commitment as an institution of higher education shapes our expectation for behavior within this learning community, which represents diverse individual beliefs, backgrounds, and experiences. Courteous and respectful behavior, interactions, and responses are expected from all members of the University. We must work together to make this a safe and productive learning environment for everyone. Part of this work is recognizing how race and other aspects of who we are shape our beliefs and our experiences as individuals. It is not enough to condemn acts of racism. For real, sustainable change, we must stand together as a diverse coalition against racism and oppression of any form, anywhere, at any time.

Resources for education and action are available through WCU’s Office for Diversity, Equity, and Inclusion (ODEI), DEI committees within departments or colleges, the student ombudsperson, and centers on campus committed to doing this work (e.g., Dowdy Multicultural Center, Center for Women and Gender Equity, and the Center for Trans and Queer Advocacy).

Guidance on how to report incidents of discrimination and harassment is available at the University’s Office of Diversity, Equity and Inclusion.

Emergency Preparedness

All students are encouraged to sign up for the University’s free WCU ALERT service, which delivers official WCU emergency text messages directly to your cell phone. For more information, visit https://www.wcupa.edu/wcualert. To report an emergency, call the Department of Public Safety at 610-436-3311.

Electronic Mail Policy

It is expected that faculty, staff, and students activate and maintain regular access to University provided e-mail accounts. Official university communications, including those from your instructor, will be sent through your university e-mail account. You are responsible for accessing that mail to be sure to obtain official University communications. Failure to access will not exempt individuals from the responsibilities associated with this course.