PHIL-190 -sam.noll-2026-09-29-10-28-15

How to Survive and Thrive in the World of AI

(Link to Syllabus PHIL_190_syllabus.docx)

Prefix and Number: PHIL 190 (02400)     Semester and Year: Fall 2027    

Number of Credit Hours: 3

Prerequisites: None

Course Details

Day and Time: 12:10 – 1pm, Mon, Wed, and Fri.

Meeting Location: Spark G45

Instructor Contact Information

Instructor: Michael Goldsby

(michael.goldsby@wsu.edu)

Office: 718 Johnson Tower                                       Phone: (509) 335-0942

Student Hours: 2-3:30pm Mon; 1-2pm Tues; or by appointment

Teaching Assistant: Luke Ryan

(luke.ryan@wsu.edu)

Office: 715 Johnson Tower

Student hours: 1:15-3:15pm Fridays or by appointment

Course Description

The rise of generative Artificial Intelligence (AI) has been meteoric. Its rapid implementation has already caused some disruptions in our society. One that may be particularly salient to you is the fact that employers are outsourcing entry-level positions to AI agents. Nonetheless, Natural Intelligence (NI) – the thing that ought to be the product of the gray matter in your skull – is still valued by employers. The effective use of AI requires the effective use of NI by its users. Conversely, AI slop is the result of NI slop. The purpose of this class is to introduce you to some of the skills that might give you and your NI an edge in this world that is becoming more defined by its reliance on AI. This class will require the hands-on use of AI.

The primary goal of this course is to develop the NI skills you need to more effectively use AI. It also aims to develop the knowledge, skills, and experiences that help you thrive in the future – that is to make you Career Ready, World Ready (CRWR). Over the semester, we will concentrate on developing three CRWR competencies – (a) Critical Thinking & Problem Solving, (b) AI Literacy, and (c) Ethical & Professional Engagement – although we will also try to nurture the other CRWR competencies along the way. 

Course Materials

Required Texts:

(1) A Claude Pro subscription. You need at least 4 months (2 Sep – 2 Jan), which should run about $20 a month ($80 total before tax). A year’s subscription runs about $200 before tax.

(2) On Bullshit Anniversary Edition by Harry Frankfurt. ISBN: 0691276781. It costs about $12 on Amazon.

(3) There will also be readings that can be accessed via Canvas, at no additional cost to you.

Other Materials: Readings accessed via Canvas, at no additional cost to you.

Fees: No separate course fee specified.

Student Learning Outcomes (SLOs)

Course Learning Outcomes	Activities Supporting the Learning Outcomes	Assessment of the Learning Outcomes
(1) Develop the NI skills necessary to evaluate arguments CRWR competency: Critical Thinking & Problem Solving	Logic readings and problem sets	Homework; midterm exam
(2) Develop a basic understanding of Contemporary AI tools and their developmental history CRWR competency: AI Literacy	AI history and AI basics readings	Homework; midterm exam
(3) Identify which tasks are best reserved for NI and those which can be safely delegated to AI CRWR competencies: AI Literacy; Critical Thinking & Problem Solving	AI journal and final project	AI journal; final project
(4) Understand the limits of generative AI tools and given those limits which AI tools are best for which tasks CRWR competencies: AI Literacy; Ethical & Professional Engagement	AI journal, AI reliability and design lessons	AI journal; Paper 2
(5) Develop skills to distinguish AI slop from good AI output CRWR competencies: AI Literacy; Critical Thinking & Problem Solving	AI journal and AI output evaluation	AI journal; final project
(6) Develop skills to distinguish between real media and AI faked media CRWR competencies: AI Literacy; Critical Thinking & Problem Solving	Logic of confirmation and detecting AI-fakes	Homework; midterm exam
(7) Identify the risks associated with delegating tasks to AI, and how to mitigate those risks CRWR competencies: AI Literacy; Ethical & Professional Engagement; Critical Thinking & Problem Solving	AI hazards, responsibility and disclosure lessons	Paper 2; final project
(8) Become familiar with the cost associated with AI use CRWR competencies: AI Literacy; Ethical & Professional Engagement	The Costs of AI readings and discussions	Homework; final project

Tentative Course Schedule

The readings assigned are to be read by the date indicated. The following schedule is subject to change. 

Date	Topic	Readings	Problem Sets
Introduction and Necessary NI Skills
M 24 Aug	What is this class about? What is expected of me? What is AI? (Introduction)	This Syllabus	I talk about testable content on the first day – always.
W 26 Aug	What’s the difference between AI, NI, and Human intelligence? What is an argument? (Logic)	Dr. G.’s Guide to Deductive Logic (ER)
F 28 Aug	What does it mean for the conclusion to follow from the premises? How can you evaluate the logic of AI or NI output even when you’re unfamiliar with the content? (Logic)	Dr. G.’s Guide to Deductive Logic (ER)	AI-Use Reflection Due
M 31 Aug	What does it take to know something? Also, does AI know anything? (Epistemology)	Frankfurt, H.G. (2025) [7]; Selection from the SEP article on “Social Epistemology” — Goldman (2026) [8]	Problem Set 1 due; Paper 1 assigned
W 2 Sep	Is AI aimed at the truth? (AI Reliability)	Hicks et al. (2024) [10] (ER)
F 4 Sep	How do we weigh evidence? (Logic of Confirmation and Detecting AI-Fakes)	Dr. G.’s Guide to Induction; Hampson (2024) [9]
M 7 Sep	Labor Day	No Class	No class
History of AI and AI Basics
W 9 Sep	How do I write a philosophy paper with just NI?		Problem Set 2 Due; Paper 1 Assigned
F 11 Sep	What is the Mind/ NI? (Philosophy of Mind)	Descartes (1993) [5]
M 14 Sep	What is the Mind/ NI? How do we copy NI? (Philosophy of Mind)	Putnam (1967) [20]	Problem Set 3 Due
W 16 Sep	What is consciousness? (Philosophy of Mind)	Nagel (1974) [18] (ER)
F 18 Sep	What’s the history of AI? (Philosophy of Cognitive Science)	Turing (1950) [27] (ER)
M 21 Sep	What’s the history of AI? (Philosophy of Cognitive Science)	Searle (1980) [25] (ER)	Problem Set 4 Due
W 23 Sep	How did we move from cognitive modeling to modern AI? (From Cognitive Science to AI)	Bogard and Asbury (2024) [2]
F 25 Sep	Are all AI models the same? (AI Typology)	Ramakrishnan (2025) [21] (ER)	Paper 1 due
M 28 Sep	Review	Review	Problem Set 5 Due
W 30 Sep	MIDTERM	MIDTERM
Potential Hazards of AI (and how to avoid them)
F 2 Oct	What mistakes do humans commonly make? (Fallacies and Cognitive Biases)	Dr. G.’s Guide to Informal Fallacies and Cognitive Biases
M 5 Oct	What are common mistakes made with generative AI? (Common AI Fallacies)	MIT Sloan (n.d.) [16] (ER)	Problem Set 6 Due Paper 2 Assigned
W 7 Oct	How do we double-check AI-Output? (Research Literacy)	Library Presentation
F 9 Oct	How do we use NI to determine what we should do? (Decision Theory/ Practical Reasoning)	Dr. G’s Guide to Practical Reasoning
M 12 Oct	How is it that AI can fool some people? (How AI can Hack NI)	Sandberg (2014) [23] (ER)	Problem Set 7 Due
W 14 Oct	Why does AI Hallucinate? (AI Hallucinations)	Kalai et al. (2025) [11]
F 16 Oct	How do training decisions lead to problems? (AI Design)	Woodside and Toner (2024) [31]	Paper 2 Due
M 19 Oct	Why is my AI agent such a suck-up? (AI Sycophancy Problem)	OpenAI (2025) [19]	Problem Set 8 Due
W 21 Oct	What’s wrong with a little flattery? (The Dangers of Sycophancy)	Turner and Eisikovits (2026) [28]
F 23 Oct	Why do the hard work? (The De-skilling Problem)	Ferdman (2026) [6]
AI in The Workplace
M 26 Oct	How is AI going to affect my employability? (AI and the Job Market)	Lichtenberg (2025) [13]	Problem Set 9 Due
W 28 Oct	Interview: Software Engineer
F 30 Oct	Interview: Lawyer
M 2 Nov	Interview: Physician		Problem Set 10 Due
W 4 Nov	Does good AI usage really lighten the workload? (AI Task Efficiency)	Baily and Kane (2024) [1] McKendrick (2026) [15]
F 6 Nov	Am I responsible for AI output? (Responsibility for AI Ouputs)	Matthias (2004) [14]
M 9 Nov	Why does anyone care about AI disclosure? (Deceptive AI Use)	Sahebi and Montefiore (2026) [22] Simon (2025) [26]	Problem Set 11 Due
W 11 Nov	How do we detect AI Fakes?	No Class
F 13 Nov	R U A TREE?	Review your answers to problem Set 1
M 16 Nov	How do I add Value? (Practical AI Skill Development)	de Fontaines (2026) [4] Kosmyna et al. (2025) [12]	Problem Set 12 Due
W 18 Nov	Prompt Engineering	Mollick (2023) [17] CFDTI (2023) [3]
F 20 Nov	Prompt Engineering	Saravia (2022) [24]
M 23 Nov	THANKSGIVING BREAK	Show your family the crazy skills you’ve learned	Don’t forget Homework
W 25 Nov	THANKSGIVING BREAK	Formalize your crazy uncle’s argument and show how it’s not valid	Don’t forget Homework
F 27 Nov	THANKSGIVING BREAK	Apologize to your family for Wednesday	Don’t forget Homework
The Costs of AI
M 30 Nov	Am I ready for the final presentation next week? Also, Is AI fair?	Bring in some of your materials.	Problem Set 13 Due
W 2 Dec	What are the Environmental Costs of AI Usage?	Dr. G and Claude’s Guide to the Environmental Costs of Generative AI
F 4 Dec	What are the Human costs of AI usage?  (Interview with a second Generation Wisconsin Farmer)	Walayat (2026) [30] United States DOJ (n.d.) [29]
M 7 Dec	Presentations	TBD	Problem Set 14 Due
W 9 Dec	Presentations	TBD
F 11 Dec	Presentations	TBD

Final Project (Written) and Final AI Journal submission due Monday December 14th.

Expectations for Student Effort

For each hour of lecture equivalent, students should expect to have a minimum of two hours of work outside class. As discussed below, our time together is very precious, and as such, I expect you to attend the lectures having read the assigned readings and generally ready to learn. 

Grading

Your grade in this class will be determined by your (1) performance on the midterm exam, (2) work on the homework assignments, (3) the quality of your written work, (4) the success of your final presentation, and (5) participation, as indicated by the following breakdown:

Type of Assignment	Points	Percent of Overall Grade
Participation/Attendance	As specified in assignment
Homework	As specified in assignment	20%
Midterm Exam	As specified in assignment	15%
Paper 1	As specified in assignment	15%
Paper 2	As specified in assignment	15%
Final Project	As specified in assignment	25% (Plus an opportunity for extra credit)
AI Journal	As specified in assignment	10%

You will receive a numerical score (0-100) based on weighted average that corresponds to the breakdown indicated above, which corresponds to a letter grade as indicated by the following scale:

Grade	Percent / Score
A	93-100
A-/B+	88-92
B	83-87
B-/C+	78-82
C	70-77
D	60-69
F	< 60

You will note that there are some swing grades (e.g. between A and A-/B+ (92-93). If you receive a final score that is such a swing grade, then your letter grade will be determined by two factors: (1) how close you are to one score or the other (e.g. 92.75 is closer to 93 than 92.25); and (2) how well you’ve participated in class.

Assignment Details

Midterm Exam: You will have only one in-class exam in this course: a midterm. The midterm is intended to test your natural intelligence skills, and it will be comprised of short answer questions and essay questions. The midterm exam is tentatively scheduled for the 30th of September during our regularly scheduled lecture time.  No makeups for exams will be administered after the regularly scheduled exam time.

 

Homework: There will be problem sets roughly every week. These problem sets will be given a score between 0 (for no work) and 4. Your homework score will be determined by the sum of your ten best problem sets, and will constitute 20% of your final grade. No late homework will ever be accepted.

 

Papers: During the course of the semester you will be asked to produce two papers. Each Paper Assignment will constitute 15% of your grade. Paper Assignment 1 is both assigned and due before the embargo on generative AI has been lifted. Thus, Generative AI will not be used for the first Paper, and failure to respect the embargo is a case of academic dishonesty. Paper Assignment 2, however, will require the use of AI. Of course, you will be required to provide chat logs and disclose your use of AI when you turn in your assignment. More on both assignments will be revealed when they are assigned.

 

AI Journal: You will be keeping a journal of your AI usage. At least 3 times a week, but no more than once a day, after the embargo is lifted on the 15th of September, you will provide a log of a task that you asked Claude to perform. Then in no more than 250 words provide a critical reflection of the output: what did it do well, what did it get wrong (or hallucinate), what could it have done better. This critical evaluation should be raw NI output and you should apply lessons from epistemology, philosophy of mind, and logic to make your case. Additionally, it would be helpful for your final project if you consider how you might’ve used Claude better to get a better result. This is obviously a hands-on AI project, so you will have to use AI to get the data. However, I strongly recommend that you use NI to compose your reflection.

 

Final Project: Throughout the semester, you will develop a personal epistemic toolbox for the more effective use of AI. What rules or guidelines (i.e., tools) have you developed to use AI better? Why are these rules necessary? As part of the final project, you will present one of your tools (from your toolbox) to the class – identifying the problem that your tool is supposed to solve and how it does so. The best tools will be added to the AI Codex, which will be shared with you as it evolves and with future classes. If your tool is selected for inclusion in the Codex, you will receive five extra credit points to your final grade.

Attendance and Make-Up Policy

Since Covid, I have relaxed the requirement for attendance. Moreover, I post what I can on Canvas. Thus, if you are ill stay home. However, make sure to do the readings and talk with your friends about what you may have missed. Nonetheless, try to attend every day that you are healthy. I have data that establishes a strong correlation between grades and attendance. That strong correlation also extends to how much you participate in class.

No makeups for exams will be administered after the regularly scheduled exam time.

No late homework will ever be accepted.

Academic Integrity Statement

I expect all of you to hold yourselves to the highest standards of academic integrity in this class. I have adopted a zero-tolerance policy with regard to academic dishonesty. Academic dishonesty includes (but is not limited to) cheating on exams and misrepresenting your attendance status. Any instance of academic dishonesty will result in your receiving a failing grade for the course.

You are responsible for reading WSU’s Academic Integrity Policy, which is based on Washington State law. If you cheat in your work in this class you will:

• Receive a failing grade for the course.
• Be reported to the Center for Community Standards.
• Have the right to appeal my decision.
• Not be able to drop or withdraw from the course until the appeals process is finished.

If you have any questions about what you can and cannot do in this course, ask me. If you want to ask for a change in my decision about academic integrity, use the form at the Center for Community Standards website. You must submit this request within 21 calendar days of the decision.

Artificial Intelligence

Until the 2nd of September, the use of generative AI is strictly prohibited except for the AI-Use Reflection piece.  After the 2nd of September, you may use your Claude account (for this class only), unless otherwise specified in the assignment. Indeed, you must activate your Claude Account no later than the 2nd. In order to control for variables, no other generative AI is authorized for class work. Also, any time you use AI, you must disclose your use of AI.

Classroom Guidelines and Policies

Lecture: Our time together is very precious, and as such, I expect you to attend the lectures having read the assigned readings and generally ready to learn. Oh yeah, WSU requires me to say, “For each hour of lecture equivalent, students should expect to have a minimum of two hours of work outside class.” During class time there should be nothing on your desk that is not required for class (e.g. no newspapers, books from other classes, etc.).  All cell phones should be silenced.  Laptops and tablets may be used for note-taking if you wish.

Respect in the Classroom: Over the course of the semester, we may discuss some very sensitive issues, ones that may cause your blood to boil.  I think J.S. Mill said it best when he said, “there is nothing that offends no one, and no one who is offended by nothing.”  It is possible that you may leave lecture very upset about the day’s discussion – good!  I hope that I challenge you to think about issues of such import that they would make us angry, sad and wanting to make a change in our lives or the world. However, I expect everyone to treat every other member of this class with the highest degree of respect.  Acting otherwise will result in dire consequences for the transgressors.

Communications Policy: The best way to get in touch with me is via email. I have my phone number listed above, and you are welcome to give me a call. However, you are much more likely to get a response or acknowledgment via email. I check my email frequently, and I will generally make every effort to respond to your emails in a timely manner (usually within a couple of hours). However, there might be rare cases during the semester when it takes up to 48 hours for me to do so. Thus, you should plan accordingly, if there is a time constraint that you are worried about. One final note: WSU has just changed their email policy. All official emails must be sent to WSU email addresses. I may still respond to your personal email, but I will send all class business (e.g., announcements) exclusively to your WSU email. It is your responsibility to monitor your WSU email. Claiming that you did not receive word about a class issue because you did not check your WSU account is an excuse that carries no weight for this class. Finally, I will never discuss grades via email due to the possibility of a violation of your privacy.

University Policies

Students are responsible for reading and understanding all university-wide policies and resources pertaining to all courses (for instance: accommodations, care resources, policies on discrimination or harassment), which can be found in the university syllabus.

Course Bibliography

1. Baily, M.N. and Kane, A.T. (2024) 'How will AI affect productivity?', Brookings, 2 May. Available at: brookings.edu (Accessed: 6 September 2026).
2. Bogard, G. and Asbury, M. (2024) 'How we think: brain-inspired models of human cognition contribute to the foundations of today's artificial intelligence', Golden Goose Award, 16 September. Available at: goldengooseaward.org (Accessed: 6 September 2026).
3. Centre for Faculty Development and Teaching Innovation, Centennial College (2023) 'Engineer a prompt', in Generative Artificial Intelligence in Teaching and Learning. Available at: ecampusontario.pressbooks.pub (Accessed: 6 September 2026).
4. de Fontaines, N. (2026) 'The risks of cognitive offloading', Duke Corporate Education, June. Available at: dukece.com (Accessed: 6 September 2026).
5. Descartes, R. (1993) Meditations on First Philosophy. 3rd edn. Translated by D.A. Cress. Indianapolis, IN: Hackett Publishing.
6. Ferdman, A. (2026) 'AI deskilling is a structural problem', AI & Society, 41(4), pp. 3001–3013.
7. Frankfurt, H.G. (2025) On Bullshit. 20th anniversary edn. Princeton, NJ: Princeton University Press.
8. Goldman, A. (2026) 'Social epistemology', in E.N. Zalta and U. Nodelman (eds.) The Stanford Encyclopedia of Philosophy. Available at: plato.stanford.edu/entries/epistemology-social (Accessed: 10 September 2026).
9. Hampson, M. (2024) 'The AI arms race to combat fake images is even — for now', IEEE Spectrum, 8 June. Available at: spectrum.ieee.org (Accessed: 8 September 2026).
10. Hicks, M.T., Humphries, J. and Slater, J. (2024) 'ChatGPT is bullshit', Ethics and Information Technology, 26, article 38.
11. Kalai, A.T., Nachum, O., Vempala, S.S. and Zhang, E. (2025) 'Why language models hallucinate', OpenAI. Available at: openai.com (Accessed: 6 September 2026).
12. Kosmyna, N., Hauptmann, E., Yuan, Y.T., Situ, J., Liao, X-H., Beresnitzky, A.V., Braunstein, I. and Maes, P. (2025) 'Your brain on ChatGPT: accumulation of cognitive debt when using an AI assistant for essay writing task', arXiv:2506.08872 [Preprint]. Available at: arxiv.org (Accessed: 6 September 2026).
13. Lichtenberg, N. (2025) 'The AI skills gap is really a "critical thinking" gap: the Fortune 500 fears it can't find talent with enough sharp thinking', Fortune, 12 December. Available at: fortune.com (Accessed: 6 September 2026).
14. Matthias, A. (2004) 'The responsibility gap: ascribing responsibility for the actions of learning automata', Ethics and Information Technology, 6(3), pp. 175–183.
15. McKendrick, J. (2026) 'AI offers a great productivity boost. Or maybe not', Forbes, 10 February. Available at: forbes.com (Accessed: 6 September 2026).
16. MIT Sloan Teaching & Learning Technologies (n.d.) 'When AI gets it wrong: addressing AI hallucinations and bias'. Available at: mitsloanedtech.mit.edu (Accessed: 6 September 2026).
17. Mollick, E. (2023) 'Working with AI: two paths to prompting', One Useful Thing, 1 November. Available at: oneusefulthing.org (Accessed: 6 September 2026).
18. Nagel, T. (1974) 'What is it like to be a bat?', The Philosophical Review, 83(4), pp. 435–450.
19. OpenAI (2025) 'Sycophancy in GPT-4o: what happened and what we're doing about it'. Available at: openai.com (Accessed: 6 September 2026).
20. Putnam, H. (1967) 'Psychological predicates', in W.H. Capitan and D.D. Merrill (eds.) Art, Mind, and Religion. Pittsburgh: University of Pittsburgh Press, pp. 37–48.
21. Ramakrishnan, R. (2025) 'When to use GenAI versus predictive AI', MIT Sloan Management Review, 24 March. Available at: sloanreview.mit.edu (Accessed: 6 September 2026).
22. Sahebi, S. and Montefiore, T. (2026) 'People are using AI to communicate without disclosing it. Is this morally wrong?', The Conversation, 5 June. Available at: theconversation.com (Accessed: 7 September 2026).
23. Sandberg, A. (2014) 'Eugene the Turing test-beating teenbot reveals more about humans than computers', The Conversation, 9 June. Available at: theconversation.com (Accessed: 6 September 2026).
24. Saravia, E. (2022) 'Basics of prompting', Prompt Engineering Guide. Available at: promptingguide.ai (Accessed: 6 September 2026).
25. Searle, J.R. (1980) 'Minds, brains, and programs', Behavioral and Brain Sciences, 3(3), pp. 417–424.
26. Simon, J. (2025) 'Generative AI and four types of deception', Blog of the APA, 29 August. Available at: blog.apaonline.org (Accessed: 7 September 2026).
27. Turing, A.M. (1950) 'Computing machinery and intelligence', Mind, 59(236), pp. 433–460.
28. Turner, C. and Eisikovits, N. (2026) 'Programmed to please: the moral and epistemic harms of AI sycophancy', AI and Ethics, 6, article 168. Available at: link.springer.com (Accessed: 6 September 2026).
29. United States Department of Justice, Environment and Natural Resources Division (n.d.) 'History of the federal use of eminent domain'. Available at: justice.gov (Accessed: 6 September 2026).
30. Walayat, A. (2026) 'When can a power company take your land for data center infrastructure?', The Conversation, 19 July. Available at: pbs.org (republished from The Conversation; Accessed: 6 September 2026).
31. Woodside, T. and Toner, H. (2024) 'How developers steer language model outputs: large language models explained, part 2', Center for Security and Emerging Technology. Available at: cset.georgetown.edu (Accessed: 6 September 2026).