CS 263 – AI in the World

https://aaz-imran.github.io/teaching/2025-spring-cs263

Syllabus

Time: TR 2:00 pm – 3:15 pm
Location: Register course to know

Course Instructor: Dr. Abdullah-Al-Zubaer Imran
Office: 319 Marksbury
Office Hours: Wednesdays 10 am-12 pm

Teaching Assistant: TBD
Location: Engineering Annex 2nd Floor
Office Hours: TBD

Course Description:
This course is intended to be accessible to all first-year undergraduates and those in other years. It is not about the technical details of AI systems, but rather is about what AI is, what it does and doesn’t do, and what it should and shouldn’t do, what its role and impact are on society. The topics covered in this course will be: History of AI, historical AI Categories of current AI systems Practice in use of current AI systems Ethical considerations in the design and use of AI systems Understanding the social context of AI (socio-technical systems).

Course Outcomes
At the end of the course, the students will be able to:

• Define current categories of AI (e.g., expert systems, supervised vs. unsupervised machine learning, multi-agent systems) and their limitations and advantages
• Understand differences between human reasoning and AI
• Apply ethical frameworks or notions of bias and fairness in machine learning to reason about current uses of AI
• Analyze current AI in the language of sociotechnical systems
• Evaluate social and legal policies about AI in terms of implicit and explicit value systems, and in terms of effectiveness
• Accomplish creative work aided by appropriate AI tools

Course Schedule/Outline (Tentative):

Course Activities:

• Class participation (10%) – lecture quizzes, in-class and canvas activities
• Assignments (30%) – Three written assignments
• Project/Paper (30%) – Final group project (final presentation and report)
• Assessments (30%) – In-class Exams

Grading Scale:
After the final percentage grade is calculated, the following scale will be used to determine the final letter grade.

• 90–100% (A)
• 80–89% (B)
• 70–79% (C)
• 60–69% (D)
• 0–59% (E)

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Attendance Policy: Students are required to attend every lecture. Students only present in the class can take the associated quiz and get the available points. Missing a maximum of 2 lectures will be excused. Any student missing more than 2 lectures without any reasonable excuses will start losing 10 points for every miss.

Academic Integrity: Please strictly follow the Academic Offenses Rules (plagiarism, cheating, and falsification or misuse of academic records). Also, keep in mind that any copyrighted materials (e.g., images and other media), and published contents (e.g., academic papers, books, web sources, online tools) used in your submissions and project should be properly cited. Ideas from people other than your own (for the project—ideas from outside your group) should be acknowledged.

Late Policy: Late submissions (assignment, project proposal, code, project final report) will be subject to a 50% score penalty if you submit within 2 days after the deadline. A score of 0 will be given for any submissions after that. Late submissions will be accepted only for certain circumstances (e.g., medical) with proper evidence.
Exceptions to this rule may be requested by providing appropriate documentation (e.g., medical) which will be considered at the discretion of the instructor.

Generative AI Policy: GenAI tools such as ChatGPT may be used in this course for the purposes of pre-submission activities. However, students are responsible for submitting work that meets the assignment standards for quality and factual accuracy. Check the Canvas page for more detailed instructions on this. If you have any questions or concerns about the policy, contact the instructor before submitting any assignments.

Disability and Special Accommodation: Please let the instructor know of any needed accommodation in the first two weeks. Please also see Academic Accommodation for further assistance.

Academic Policy Statements, DEI, Resources Available to Students

Useful Resources:

• UK Libraries
• Free tutoring and coaching at UK
• Google Scholar