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6.S062/MAS.S10/MAS.s60 Generative Artificial Intelligence in K12 Education

Fall 2023 | MIT CSAIL x MIT Media Lab

Gen AI Poster

Instructors

Hal Abelson: Hal Abelson is a Professor at MIT’s Computer Science and Engineering in the Department of Electrical Engineering and Computer Science.
Safinah Ali: Safinah Ali is a PhD candidate at MIT’s Media Lab.
Prerna Ravi: Prerna Ravi is a PhD student at MIT’s Electrical Engineering and Computer Science Department.
Kate Moore: Kate Moore is a Research Scientist at MIT’s STEP Lab.
Cynthia Breazeal: Cynthia Breazeal is a Professor of Media Arts and Sciences at MIT’s Media Lab and the Director of Open Learning at MIT.
Randall Davis: Randy Davis is a Professor at MIT’s Computer Science and Engineering in the Department of Electrical Engineering and Computer Science.

Time and Dates

Fall 2023: Weekly on Tuesdays 1-3 pm

Location

34-303

Pre-requisites

6.100A or equivalent; permission of instructor required; enrollment may be limited The course is open to undergraduate and graduate students at MIT, Harvard and Wellesley College.

Units

2-0-10

Description

The emergence of transformer architectures in 2017 triggered a breakthrough in machine learning that today lets anyone create computer-generated essays, stories, pictures, music, videos and programs from high-level prompts in natural language, all without the need to code. That has stimulated fervent discussion among educators about the implications of generative AI systems for curricula and teaching methods across a broad range of subjects. It has also raised questions of how to understand both these systems and the at times overstated claims made for them. This class will introduce the foundations of generative AI technology and participants will explore new opportunities it enables for K-12 education. It will also describe and explore how an analytical frame of mind can help make clear the core issues underlying both the successes and failures of these systems. Much of the work will be project-based involving implementing innovative teaching and learning tools and testing these with K-12 students and teachers.

Schedule

Week Date Topic Assignments Slides/Links
W1 9/12 Introduction to Generative AI for K12 education. What the class is / what the class is not Assignment 1 released
Project details released		  
W2 9/19 Generative AI Tools in K12 Education Assignment 1 due
Assignment 2 released 		Slide Deck,
Other Links
W3 9/26 AI: Ethics and Equity Assignment 2 due
Project check-in 1 due
Assignment 3 released 		Slide Deck
Other Links
W4 10/3 Panel Discussion with K12 Stakeholders (educators, designers, coaches, students) Assignment 3 due
Assignment 4 released		 Slide Deck,
Other Links
W5 10/17 Introduction to Technical Foundations of Generative AI Assignment 4 due
Assignment 5 released 		Slide Deck
W6 10/24 Cont: Technical Foundations of Generative AI
Prompt Engineering		 Assignment 5 due
Assignment 6 Readings and project check-in instructions released
Project check-in 2 due		 Prompt Engineering Slide Deck
W7 10/31 Usability Testing Methods
Project prototypes feedback		 Readings and Project Check-in Instructions Usability Testing Slide Deck
W8 11/7 Teacher Professional Development (PD) and AI Literacy Assignment 7 released Slide Deck
Other Links
W9 11/14 Ethical implications of AI in education Assignment 7 due Ethical implications handout
W10 11/21 Project Check-In #3 Presentations
Presentation from the BU/MIT Student Innovations Law Clinic		 Project check-in 3 due
Assignment 8 released		  
W11 11/28 AI Education & Policy
AI hype		 Assignment 8 due  
W12 12/5 Evaluating Existing GenAI Tools and Curriculum N/A Slide Deck
Class Links
W13 12/12 Final Project Presentations Final Project Report and Presentation due Request staff for presentation recordings

Student presentations

Office Hours

Mondays 3.30 - 4.30pm - Safinah
Tuesdays 11-12pm - Kate
Wednesdays 12-2 - Hal | Sign up sheet
Wednesdays 3-4pm - Prerna
Fridays 2-3pm - Randy

Office Hours Zoom Link: https://mit.zoom.us/j/91849336452

Pre-semester Assignment

We have issued a pre-semester assignment (link) to be completed before our first class on September 12. There is nothing to hand in, but it is meant to prepare you to discuss the readings and get set up to work with GPT using Python and Colab.

Grading

Your final grade will be calculated based on the following breakdown:

Letter grades will be determined at the end of the semester. There are no grade quotas or targets, and there is no centering of the grade distribution at a particular boundary (i.e., we do not “curve” the class).

Final Project

Term projects are a major component of this course. Final projects are due on December 12, together with an oral presentation. Projects should be done by teams of 3, who should meet regularly to make sure project work is on track. Your project should be a tool (or a lesson or activity) that demonstrates how generative AI can empower students to address K12 educational topics that could not previously be addressed adequately, or that lets them address existing topics in new ways. The choice of implementation platform is up to you. You do not need to produce a finished, professional-level tool, but you should produce something that is usable and could plausibly be tested with the intended audience.

You can choose the age of the student your project is designed for (primary school through high school). Your tool should include an activity that students actually do, not just an exposition that they read or hear about. You should also test your work. The course staff will try to arrange a student workshop where your project can be tested, but this might not always be possible. At a minimum, you should present your project to a few friends and get feedback in advance of your oral presentation.

Important due dates for the final project:

Issued: September 12, 2023
Project Check-in #1: September 26, 2023
Project Check-in #2: October 24, 2023
Project Check-in #3: November 21, 2023
Project In-class Check-in and Feedback: December 5 and December 12, 2023
Final Presentation and Report: December 12, 2023

For more details, click here.

Due Dates and Extensions

All assignments/project check-ins are to be submitted by the due date noted on the assignment. They will usually be due by the beginning of class (1pm EST) on the dates mentioned. Deadlines are firm.

Every student has 3 late days that you can use, no questions asked, for additional flexibility during periods of heavy workload, minor illness, absences from campus (e.g., interviews, conference trips, sports meets, etc.), special occasions (e.g., religious holidays, family events, etc.), or unexpected problems. These slack days may be used in accordance with the following requirements:

  1. Course staff must be notified of a student’s wish to use a late day in advance of the assignment deadline. We prefer at least a 24 hour notice. Use this form to request late days.

  2. Late days may only be used for individual assignments ONLY. They cannot be used on the final team project, nor any of its milestones/check-ins.

  3. You may use at most 2 late days for any given deadline.

  4. Late days apply in 24-hour chunks. You cannot chop a late day up into late hours or minutes.

  5. Late submissions not covered by a late day will incur a penalty of 10% of the total available grade for each day of lateness. Note: while we will endeavor to return graded work to you as soon as possible, if you use slack days, this will likely delay receiving feedback on your work.

  6. Late days may not be reflected in initial grading, but will be factored in and distributed at the end of the semester. Assignments will not be accepted beyond one week late and any missing assignment will receive a zero (regardless of available late days left).

If you are taking this course, the expectation is that you have set aside the considerable amount of time needed to get your assignments and projects done in a timely manner. These late days are intended to cover unexpected clustering of due dates, travel commitments, interviews, hackathons, computer problems, extracurricular commitments, etc. Don’t ask for extensions to due dates because we are already giving you a pool of late days to manage yourself.

Additional Support for MIT Students

If you encounter an emergency or illness that cannot be managed through the allocated late days, we are more than willing to collaborate with you to arrange a suitable extension. However, to facilitate your request, we kindly request that you reach out to the specified on-campus offices and include their written support (including a clear outline of the recommended extension duration) as a component of your extension appeal to us.

For Undergraduate Students: Student Support Services (S3). In the event of personal or medical challenges affecting your class attendance, or coursework completion, it is advisable to communicate with a dean at Student Support Services (S3). S3 is dedicated to assisting you in such situations. The deans will verify your circumstances, offer assistance, and aid in coordinating with your respective professor or instructor to establish the subsequent steps. In most circumstances, you will not be excused from coursework without verification from a dean. For contact details and further avenues of support, please refer to the S3 website.

For Graduate Students: GradSupport. As a graduate student, a range of concerns such as faculty relationships, funding, and interpersonal matters might influence your academic journey. Within the Office of Graduate Education (OGE), GradSupport delivers guidance, mentorship, and representation to graduate students encountering challenges related to academics and personal life. If you are grappling with a situation impacting your class participation, or assignment completion, don’t hesitate to connect with GradSupport through email at gradsupport@mit.edu or by phone at (617) 253-4860.

The staff of this class is deeply committed to making your experience this semester valuable and, hopefully, joyful as well.

Collaboration and Reuse

Good design revolves around teamwork, drawing inspiration from various sources, integrating them into your unique approach, receiving and providing design evaluations. Hence, we promote collaboration within defined boundaries.

Individual assignments: The six individual assignments are solo assignments, and hence should be completed without any collaboration. While you can engage in broader conversations with fellow students, the distinct components of analysis, design, and execution for each assignment should be your individual effort.

Final Team Project: All members of the team must work together on all parts of the project, and each of you is expected to contribute a roughly equal share to these components. In most cases, all team members will be assigned the same grade for the work that is successfully completed, unless there are exceptional circumstances.

Unless otherwise stated in an assignment, you are free to use any third-party code, whether as libraries or code fragments, and to adopt any idea you find online or in a book as long as it is publicly available and appropriately cited (see the section on code in the MIT Handbook on Academic Integrity for details). Please include these citations directly in your code as comments as well as part of any required writeups/reports.

Academic Integrity

In this course, we will hold you to the high standard of academic integrity expected of all students at the Institute. We do this for two reasons. First, it is essential to the learning process that you are the one doing the work. We have structured the assignments in this course to enable you to gain a mastery of the course material. Failing to do the work yourself will result in a lesser understanding of the content, and therefore a less meaningful education for you. Second, it is important that there be a level playing field for all students in this course and at the Institute so that the rigor and integrity of the Institute’s educational program are maintained.

Violating the Academic Integrity policy in any way (e.g., plagiarism, unauthorized collaboration, cheating, etc.) will result in official Institute sanction. Possible sanctions include receiving a failing grade on the assignment or exam, being assigned a failing grade in the course, having a formal notation of disciplinary action placed on your MIT record, suspension from the Institute, and expulsion from the Institute for very serious cases.

Please review MIT’s Academic Integrity policy and related resources (e.g., working under pressure; how to paraphrase, summarize, and quote; etc.) and contact me if you have any questions about appropriate citation methods, the degree of collaboration that is permitted, or anything else related to the Academic Integrity of this course.

Inclusion and Respect

Fostering an Inclusive Classroom: MIT policy requires that courses welcome all students of all backgrounds, beliefs, ethnicities, national origins, gender identities, sexual orientations, religious and political affiliations—as well as other visible and nonvisible differences. You should expect and demand to be treated with respect by your classmates and the course staff and, reciprocally, treat your classmates and course staff with respect. Each of us is responsible for creating a safer, more inclusive environment. Unfortunately, incidents of bias or discrimination do occur, whether intentional or unintentional. They contribute to creating an unwelcoming environment for individuals and groups at the university. If any incident occurs that challenges this commitment to a supportive, diverse, inclusive, and equitable environment, please let the course staff know (6.s062-staff@mit.edu) so that the issue can be addressed.

Access and Disability Accommodations: MIT policy requires that we stay committed to the principle of equal access. If you are in need of disability accommodations, please speak with Disability and Access Services (DAS) prior to or early in the semester so that accommodation requests can be addressed in a timely fashion. If you have a disability and are not planning to use accommodations, we still recommend meeting with DAS staff to familiarize yourself with their services and resources.

If you have already been approved for accommodations, please get in touch with the course staff (6.s062-staff@mit.edu) who will be ready to assist you with implementing the accommodation.

Email Etiquette

Please give us at least 24 hours to reply to your emails, and we will do the same for you. Please put the course number in the Subject line and remember to sign your email with your name. We expect the language and structure of your emails to be professional.

More to come!

Contact

If you have any questions regarding this course, please email us using the course mailing list:

We have also provided individual instructor emails below (though the mailing list is preferred):