Sociotechnical Dimensions of Computing
in the Age of Artificial Intelligence

CS110 -- Spring 2026

This course aims to provide students with the knowledge and skills
to become informed digital citizens in the age of AI,
ready to navigate the digital landscape.

How does AI work? What impact will AI have on individuals, communities, and our global society?

Through hands-on activities,
a semester-long collaborative project,
and engagement with guest lecturers,
students will gain fundamental technical understanding
of how computers, the Web, and AI work,
hone their ability to identify potential biases,
discuss societal and ethical issues with AI technologies,
and consider responsible use of AI.

About CS110 SocioTech_AI

Learning Goals

The course aims to engage students with the theory and practice of novel human-computer interfaces. Upon completing this course, students will gain basic skills in the following areas:

  • Computer Programming - Learn basic programming concepts (variables, functions, conditionals, etc.) and apply them to solve simple problems.
  • Information Representation - Understand how to use bits to represent different kinds of information including numbers, text, image, and video. Collect and prepare training data for machine learning models.
  • Internet and Web Technologies - Understand the underlying structure of how the Internet & the Web work.
  • Demystifying AI - Identify a variety of technologies that use AI, including technology spanning cognitive systems, robotics, and machine learning; Understand the big ideas of how AI works.
  • Societal and ethical issues surrounding AI - Identify and discuss different perspectives on key issues surrounding AI including privacy, employment, misinformation, the singularity, ethical decision making, diversity, bias, transparency, and accountability.
  • Critical Thinking - Consider and debate practices for responsible use of AI; Engage in a semester-long thorough investigation of the merits and risks of using AI to address global challenges.

Meet your instructors & tutors

Schedule

The full course schedule is in our Google drive folder and accessible from the following link. Please check the schedule regularly to keep track of due dates and alternate meeting locations.

Policies

Class Meetings

Our class will meet Mondays and Thursdays from 2:20-3:35 PM in Science Center Hub H401. Occasionally, we will meet in alternate locations as noted on the course schedule.

Office and Drop-in Hours

Brian's office hours and tutor/teaching fellow drop-in hours are posted in our office and drop-in hours document. The times/locations will be the same each week unless we decide to make a change to better accommodate student's schedules or space needs. Office and drop-in hours will be cancelled on any days when classes are cancelled. If hours need to be cancelled or temporarily moved for any other reason, student's will be notified by email through the course Google group.

Prerequisites

Fulfillment of the Quantitative Reasoning (QR) component of the Quantitative Reasoning & Data Literacy requirement. No prior background with computers is expected.

Computer Science Department Guidelines

As a part of this course, students and faculty are expected to adhere to the Computer Science Department Guidelines, which are designed to create a positive and productive learning environment. These guidelines emphasize respect, integrity, inclusivity, and transparency, ensuring that our department prepares students to lead in a world shaped by computation and data. For a detailed description of these principles and guidelines on attendance, timeliness, respect for others, exam policies, and how to share your thoughts with us, please refer to the Computer Science Department Guidelines Document.

Topics

Textbook, Readings, and Materials

We will be using a zyBook titled "Web Programming". ZyBooks is an interactive learning platform that integrates text and learning activities. To access the book:

  1. Sign in or create an account at zyBooks
  2. Enter zyBook code: Provided in course email
  3. Subscribe - there is NO cost for a subscription

In addition, we will read research and news articles as well as book chapters. Access to these readings will be available online or through the course Google Drive.

Course Requirements

The course requires active participation in class through discussion and hands-on lab activities. Students are required to prepare for class by completing the reading prior to lecture, arrive to class on time, actively participate in discussions, and submit all hands-on lab activities. Frequent questions and especially wrong answers are strongly encouraged.

There course has two different types of assignments: practice and assesment.


In addition, the course has a final project that requires working in teams to investigate a topic at the intersection of AI and society. As part of your team project, you will be expected to do the following:


The dates of the assignments and project milestones are listed on the course schedule.

Grading Policy

This course's grading is Mandatory Credit/Non-Credit (or Credit/Non), meaning that no letter grades are assigned.

To earn credit for this class, all criteria below must be met:


If you meet all of the above criteria, you will earn credit for this class. If you meet only two or three of the above criteria, you will not earn credit for this class.

Late Assignments

The late assignment policy differs by type of assignment:

  • Practice assignments: You are strongly encouraged to submit practice assignments by the deadline, but may submit up to 48 hours late.
  • Project milestones: Project milestone deadlines should be treated as hard deadlines and should not be submitted late. However, we understand that coordinating group schedules is difficult. Teams may request a 48-hour extension on a project milestone deadline by meeting with the instructor at the end of a lecture. However, some milestones (e.g., class presentations) cannot be submitted late.
  • Assessments: Assessments are conducted in class and you must be present on that date to take them. However, we will drop the two lowest scores. If you are not able to attend class when an assesment is given, the default policy will be to count that assessment as one of your two dropped assessments.

If you need extra time beyond the lateness policy outlined above, you should contact the class dean and discuss a plan for completing your assignment. We will work together to make sure that plan is a reasonable and effective so that it supports both your learning and your health.

Using Generative AI

Aligned with its learning goals, this course encourages students to explore responsible and critical engagement with generative artificial intelligence (GAI) tools such as ChatGPT, Gemini, DALL·E, Midjourney, Claude, and Bard. The use of GAI or AI powered tools is restricted and is allowed only when assignments specifically allow or require the use of such tools. Any use of AI powered tools in this course must follow the CS110 policy on using GAI.

We draw your attention to the fact that different classes at Wellesley implement different AI policies, and it is the student's responsibility to conform to expectations for each course.

Collaboration

The Wellesley College Honor Code applies to CS110. This course emphasizes collaboration, as working effectively within teams is an important part of the innovation culture. Working with a team provides you with opportunities to develop and improve interpersonal, communication, leadership, and follower-ship skills.

We strongly encourage you to get to know all of your classmates and collaborate extensively with them. Because of the interdisciplinary nature of this course, you may be strong in some areas, but weak in others. Please share your strengths and feel free to ask others for help.

Here is a summary of the collaboration policy:

  • In-class activities and discussion: This class requires your active contribution during our time together. Please come to class prepared and ready to contribute to our learning community. During class you will work in groups on various activities.
  • Practice assignments: You are encouraged to collaborate with others when completing your practice assignments, but this is not required.
  • Assessments: Collaboration on assessments is not allowed. Any form of collaboration with other students, people, or AI is a violation of the Honor Code.
  • Project: Teams of 3-4 students work together to complete and submit project milestones. You will be assigned to a team based on shared interests, preferred working styles, and schedule availability.

Electronic device policy

Despite being a tech-focused course, we will limit the use of technology during lecture time. The goal of this policy is to support a focused, engaged, present, and professional learning environment. We should all be together in one space, separate from the rest of the world, and concentrating on learning with each other. We will give reminders of this policy before and during class as needed.

  • Using tablets for note-taking with a stylus is allowed. All notifications should be turned off (not just silenced) and no unrelated apps should be open.
  • Laptop use in class is not allowed except when explicitly required for in-class activities or following along with a coding example.
  • If you need to use a laptop for note-taking, please reach out to me ASAP to discuss this accommodation. In this case, only your notes should be open, and all notifications should be turned off (not just silenced).
  • Phone use in class is not allowed. Phones should be silenced and put away/not visible unless they are explicitly required for an activity.
  • Recording of lectures or using automatic transcription is not allowed.
  • We recommend putting devices away even during the time before class begins and getting to know the people at your table who you will be working with on in-class activities, but this is not required.
  • Of course, all of these restrictions are subject to exceptions for individual needs/accommodations. Please reach out to me anytime to discuss this.

Attendance policy

Attendance on time is required.

  • You are allowed 4 unexcused absences except on our final presentation days. This is intended to account for illnesses and other unavoidable absences that occur during the semester. You do not have to tell me why, but should email by the start of class time to let me know. You are responsible for getting notes from another student to make up for missed content.
  • An excessive amount of unexcused absences may result in not receiving credit for the course.
  • If you need to miss more than 4 classes or need to miss a presentation day, please contact me to discuss a plan for keeping up with the content or taking an incomplete if necessary.
  • Attendance includes arriving on time to participate in lecture, discussion, and group activities. Every 2 late arrivals will be counted as an unexcused absence.

If you struggle with attendance or being on time, I’m happy to meet with you to discuss a plan for improvement. One simple strategy for being on time is to give yourself a reason to arrive early.

Health-related accomodations

If you are feeling ill or believe that your being in class might pose a risk to others, please do not come to class. Please be in touch with us at your convenience to let us know what is happening. We will then work with you to help you get back on track *after* you feel better. We also understand that illness may require you to seek an extension on a deadline – we can work with each of you individually.

Slides will always be available online after the lecture, as well as all the activities. Should you have to miss a class, your first resort is to get the notes from other students. After reviewing these notes and the materials on the course website, feel free to be in touch with us for additional help. We will not record in person lectures.

Disabilities

If you have a disability or condition, either long-term or temporary, and need reasonable academic adjustments in this course, please contact Accessibility and Disability Resources (ADR) to get a letter outlining your accommodation needs. You should request accommodations as early as possible in the semester, or before the semester begins, since some situations can require significant time for review and accommodation design. If you need immediate accommodations, during the semester, please arrange to meet with me as soon as possible. If you are unsure, but suspect you may have an undocumented need for accommodations, you are encouraged to contact ADR. They can provide assistance including screening and referral for assessments.

Computing and Laboratory Environment

We will primarily use the following platforms during the course: