CSC 160 AI for All (Spring 2026)
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:
Understand general concepts, techniques, and models comprising the field of AI.
Understand limitations and strengths of current AI models and tools.
Make informed, ethically sound decisions about current and near future applications of AI.
Use current AI tools for and constructive applications and learning.
Apply metacognitive strategies to learn more effectively with and without AI.
Understand unique privacy and security risks posed by AI systems.
Instructor
- Instructor: Adam Purtee
- Homepage: cs.rochester.edu/u/apurtee.
- Email: apurtee AT cs DOT rochester.edu
- Office: Wegmans Hall 2109
- Office Hours: 2-3pm Tuesdays and Thursdays
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 Assignments | 50% |
| Project | 40% |
| Participation | 10% |
| Total | 100% |
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
| Date | DoW | Topic | Assignments |
|---|---|---|---|
| Jan 20 | T | Introduction | |
| Jan 22 | R | AI before Deep Learning (1950-2015) | HW 1 assigned (Teachable Machine). |
| Jan 27 | T | The Rise of Deep Learning (2015-2017) | |
| Jan 29 | R | How LLMs Work | |
| Feb 3 | T | Effective Prompting | HW 2 assigned (Prompting Techniques). HW 1 Due. |
| Feb 5 | R | Human Learning | |
| Feb 10 | T | First Project Workshop | Mini-proposals due night before class. |
| Feb 12 | R | Trust and Verification | |
| Feb 17 | T | The AI Model Landscape | HW 3 assigned (Personal Benchmark). HW 2 Due. |
| Feb 19 | R | Reasoning: The Jagged Frontier | |
| Feb 24 | T | Agents and Tools | |
| Feb 26 | R | Multimodal AI: Images and Audio | |
| Mar 3 | T | Second Project Workshop | Proposals due night before class. HW 3 Due. |
| Mar 5 | R | Ethics and Impacts | |
| Mar 10 | T | No Class (Spring Break) | |
| Mar 12 | R | No Class (Spring Break) | |
| Mar 17 | T | Art and Creativity | |
| Mar 19 | R | Copyright, Fair-use, and Plagiarism | HW 4 assigned (AI Policy). |
| Mar 24 | T | Healthcare and Mental Health | |
| Mar 26 | R | Privacy, Security, and Scams | |
| Mar 31 | T | Misinformation and Persuasion | HW 5 — assigned (TBD). HW 4 Due. |
| Apr 2 | R | Infrastructure and Externalities | |
| Apr 7 | T | Third Project Workshop | Project check-ins due night before class. |
| Apr 9 | R | The Job Market | |
| Apr 14 | T | Law and Government | HW 6 — Law — assigned. HW 5 Due. |
| Apr 16 | R | Consciousness | |
| Apr 21 | T | (Humanoid) Robots | |
| Apr 23 | R | Predicting the Future | HW 6 Due. Project recordings/submissions due Friday. |
| Apr 28 | T | Symposium | |
| Apr 30 | R | Symposium |
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.