Course Information


Course Description

This experimental course will explore how AI can be safely, ethically, and effectively leveraged by students to enhance their ability to learn. Students will be expected to both collaboratively and independently pursue AI applications within their own domains of interest, and to participate in course discussions. Basic concepts of how AI systems work and how they are built will be covered at a general audience level. This course has no prerequisites and is open to all students. This is a four credit course.

Course Outcomes

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

Instructor


Assignments


Regular Assignments

Regular assignments will typically require students to use an AI tool, read a selection about a topic, and/or engage with an ethical challenge related to the use of AI in some domain or context. Students will then write a short reflective essay conveying their experience and understanding to the instructor and TAs. Regular assignments are expected to be completed as individuals.

The Project

Students will be expected to gain hands-on experience using generative AI tools while completing a self-directed educational project resulting in an artifact or body of work which can be shared and explained by the student. Projects should be chosen which are suitable to tackle with AI. All projects must involve practice of a skill and/or application of knowledge. Examples include building a portfolio of images, audio, text with a high degree of curation and clarity, computer programs, websites, music videos, explainers, or new benchmarks for AI. Great projects from Fall 2025 included learning to cook specific cuisines, improving skill at playing guitar, building an optimal solver for poker games, getting better at playing chess, learning to program in Python, learning futures trading strategies, enhancing conversational foreign language skills, building a new benchmark for music theory, experimental work surveying other students and studying use of AI during learning, and several cool new web applications.

Due to the nature of our educational environment, students will also be required to apply AI to the problem of learning. Whether or not this is a "good thing" is still an open question. The primary risks are developing an over-reliance on AI, believing falsehoods resulting from LLM confabulations (hallucinations), and curricular gaps or blindspots in studied area. To combat these risks, students will be required to develop a learning plan, to identify a reliable method of evaluating their progress, and at least two "traditional" sources of reliable information, such as a textbooks, peer-reviewed (or similar) articles, a professor, or a knowledgeable friend.

The project requires students to show deliberate practice using AI tools and application of course concepts in the form of a project journal. Students will share their projects, artifacts, and findings via recorded video presentations and in-class activities during the last week of classes. The project may be completed as individuals or in small groups.

Grading


Your Overall Numeric Grade

Your scores on the individual components of this course will be weighted to obtain your course score. All appeals of grades on individual scores must be made within one week of the grade being available. The following table represents the weighting of course components.

Category Weight
Regular Assignments50%
Project40%
Participation10%
Total100%

Letter Grades

Letter grades will follow the Official University of Rochester Grading Scheme . Note that the University scheme places “average” between C and B. The following table is an estimate of how numeric grades map to letter grades.

Letter Grade Threshold
A (Excellent)≥ 93%
A−≥ 90%
B+≥ 87%
B (Above Average)≥ 83%
B−≥ 80%
C+≥ 77%
C≥ 73%
C− (Minimum satisfactory)≥ 70%
D (Minimum passing)≥ 60%
E< 60%

Schedule


DateDoWTopicAssignments
Jan 20TIntroduction
Jan 22RAI before Deep Learning (1950-2015)HW 1 assigned (Teachable Machine).
Jan 27TThe Rise of Deep Learning (2015-2017)
Jan 29RHow LLMs Work
Feb 3TEffective PromptingHW 2 assigned (Prompting Techniques). HW 1 Due.
Feb 5RHuman Learning
Feb 10TFirst Project WorkshopMini-proposals due night before class.
Feb 12RTrust and Verification
Feb 17TThe AI Model LandscapeHW 3 assigned (Personal Benchmark). HW 2 Due.
Feb 19RReasoning: The Jagged Frontier
Feb 24TAgents and Tools
Feb 26RMultimodal AI: Images and Audio
Mar 3TSecond Project WorkshopProposals due night before class. HW 3 Due.
Mar 5REthics and Impacts
Mar 10TNo Class (Spring Break)
Mar 12RNo Class (Spring Break)
Mar 17TArt and Creativity
Mar 19RCopyright, Fair-use, and PlagiarismHW 4 assigned (AI Policy).
Mar 24THealthcare and Mental Health
Mar 26RPrivacy, Security, and Scams
Mar 31TMisinformation and PersuasionHW 5 — assigned (TBD). HW 4 Due.
Apr 2RInfrastructure and Externalities
Apr 7TThird Project WorkshopProject check-ins due night before class.
Apr 9RThe Job Market
Apr 14TLaw and GovernmentHW 6 — Law — assigned. HW 5 Due.
Apr 16RConsciousness
Apr 21T(Humanoid) Robots
Apr 23RPredicting the FutureHW 6 Due. Project recordings/submissions due Friday.
Apr 28TSymposium
Apr 30RSymposium

Policies


Academic Honesty

All assignments and activities associated with this course must be performed in accordance with the University of Rochester’s Academic Honesty Policy. More information is available at: www.rochester.edu/college/honesty

All incidents of academic dishonesty will be reported. Violations of the academic honesty policy carry significant penalties, such as a zero on the assignment and additional reduction by whole letter grades. (I.e., from B+ to C+). Repeat offenders may be expelled from their majors and from the university.

Artificial Intelligence

Because of the topic of this course, students will be required to use generative AI tools in specific ways to complete assignments. However, the reflective essays and other writing requirements are meant to solicit YOUR thoughts and arguments, so they should be written by you without the use of AI. Misrepresentation of AI generated (or substantially edited) work as human authored will be considered academic dishonesty in this course. Similarly, while you are encouraged to discuss your work and the challenging questions of this course with others, submission of another student's work as your own will also be considered academic dishonesty.

Disability Resources

The University of Rochester respects and welcomes students of all backgrounds and abilities. In the event you encounter any barrier(s) to full participation in this course due to the impact of disability, please contact the Office of Disability Resources. The access coordinators in the Office of Disability Resources can meet with you to discuss the barriers you are experiencing and explain the eligibility process for establishing academic accommodations. You can reach the Office of Disability Resources at: disability@rochester.edu; (585) 276-5075; Taylor Hall.

Students with an accommodation for any aspect of the course must make arrangements in advance through the Disability Resources office. Then, as instructed by the office, contact the instructor to confirm your arrangements.