Created in 2012 at the Massachusetts Institute of Technology, the Comprehensive Initiative on Technology Evaluation (CITE) is the first-ever program dedicated to developing methods for product evaluation in global development. CITE produces technology evaluations that provide evidence for data-driven decision-making by development workers, donors, manufacturers, suppliers, and consumers themselves. In addition, CITE evaluations lead to significant developing insights, helping us better understand development challenges.

During Fall 2021, all MIT students and the general public are welcome to join Professors Richard Young and Facundo Batista as they discuss the science of the COVID-19 pandemic. The livestream of the lectures is available to the public, but only registered students are able to ask questions during the Q&A.
Lectures will be given by leading experts on the fundamentals of coronavirus and host cell biology, immunology, epidemiology, clinical disease, and vaccine and therapeutic development. Guest faculty include Amy Barczak, Dan Barouch, Arup Chakraborty, Victoria Clark, Shane Crotty, Anthony Fauci, Britt Glaunsinger, Salim Karim, Shiv Pillai, Rochelle Walensky, Bruce Walker, Laura Walker, and Andrew Ward.

During Fall 2020, all MIT students and the general public were welcomed to join Professors Richard Young and Facundo Batista as they discussed the science of the pandemic during this new class. The livestream of the lectures was available to the public, but only registered students were able to ask questions during the Q&A. 
Special guest speakers included: Drs. Anthony Fauci, David Baltimore, James Bradner, Victoria Clark, Kizzmekia Corbett, Britt Glaunsinger, Akiko Iwasaki, Eric Lander, Michael Mina, Michel Nussenzweig, Shiv Pillai, Arlene Sharpe, Skip Virgin, and Bruce Walker.
NOTE: This class ran from September 1, 2020 through December 8, 2020.

This video will help students, particularly those not in AP-level classes, have a practical application for knowing about the major divisions between plants, particularly about the details of plant anatomy and reproduction. Students will be able to :Identify the major evolutionary innovations that separate plant divisions, and classify plants as belonging to one of those divisions based on phenotypic differences in plants. Classify plants by their pollen dispersal methods using pollen dispersal mapping, and justify the location of a _„ƒcrime scene_„Ž using map analysis. Analyze and present their analysis of banding patterns from DNA fingerprinting done using plants in a forensic context.

Student projects in D-Lab classes are defined by community partners and social ventures around the world. We don't always know what is needed, but our community partners do, and our students have technical knowledge and skills to contribute to that work.
Each semester, through a selection of full-semester classes, our students form into teams to work on projects framed by community partners – NGOs, local nonprofits, and social entrepreneurs. At the end of each semester, students present their work to their peers, partners, and guests.

The MIT Libraries Data Management Group hosts a set of workshops during IAP and throughout the year to assist MIT faculty and researchers with data set control, maintenance, and sharing. This resource contains a selection of presentations from those workshops. Topics include an introduction to data management, details on data sharing and storage, data management using the DMPTool, file organization, version control, and an overview of the open data requirements of various funding sources.

Those workshops help to gain new skills in research data management. Created by MIT Libraries, under CC-BY license, others can adapt and utilize this resources to develop thier own slides in teaching data management.

This resource is to support teachers and educators to run Day of AI activities in their classrooms through curriculum packages and teacher training, all of which is available at no cost to participants.
Developed by leading faculty and educators from MIT RAISE, the curriculum features up to four hours of hands-on activities that engage kids in creative discovery, discussion, and play as they learn the fundamentals of AI, investigate the societal impact of these technologies, and bring artificial intelligence to life through lessons and activities that are accessible to all, even those with no computer science or technical background.

This climate simulation tool makes it intuitive to see how changes in energy, land use, agriculture, and other policies will affect Earth's projected temperature. Users move sliders on various policy actions and see immediate feedback on energy sources, emissions, and global temperature.

MIT App Inventor is an intuitive, visual programming environment that allows everyone – even children – to build fully functional apps for smartphones and tablets.

This learning video presents an introduction to the Flaws of Averages using three exciting examples: the ''crossing of the river'' example, the ''cookie'' example, and the ''dance class'' example. Averages are often worthwhile representations of a set of data by a single descriptive number. The objective of this module, however, is to simply point out a few pitfalls that could arise if one is not attentive to details when calculating and interpreting averages. The essential prerequisite knowledge for this video lesson is the ability to calculate an average from a set of numbers. During this video lesson, students will learn about three flaws of averages: (1) The average is not always a good description of the actual situation, (2) The function of the average is not always the same as the average of the function, and (3) The average depends on your perspective. To convey these concepts, the students are presented with the three real world examples mentioned above.

Learn how to read and interpret maps and data and use basic cartography principles to create maps that can be used in reports and presentations. After learning basic concepts, attendees will work through an exercise using ArcGIS Pro or QGIS.

Ever wondered why your temperature stays at 98.6 degrees? Learn about homeostasis and how your body maintains a stable temperature. Created by MIT+K12.

Helps young people learn to think creatively, reason systematically, and work collaboratively — essential skills for life in the 21st century.

This learning video addresses a particular problem of selection bias, a statistical bias in which there is an error in choosing the individuals or groups to make broader inferences. Rather than delve into this broad topic via formal statistics, we investigate how it may appear in our everyday lives, sometimes distorting our perceptions of people, places and events, unless we are careful. When people are picked at random from two groups of different sizes, most of those selected usually come from the bigger group. That means we will hear more about the experience of the bigger group than that of the smaller one. This isn't always a bad thing, but it isn't always a good thing either. Because big groups ''speak louder,'' we have to be careful when we write mathematical formulas about what happened in the two groups. We think about this issue in this video, with examples that involve theaters, buses, and lemons. The prerequisite for this video lesson is a familiarity with algebra. It will take about one hour to complete, and the only materials needed are a blackboard and chalk.

Everyone will be impacted by AI in daily life and in the workplaces of the future. It is critical for all students to have fundamental knowledge of AI and to understand AI’s potential for good and harm. The Daily-AI program will jumpstart your readiness for AI and give you the tools you need to prepare for the AI-enabled world.

The Daily-AI workshop, designed by MIT educators and experienced facilitators, features hands-on and computer-based activities on AI concepts, ethical issues in AI, creative expression using AI, and how AI relates to your future. You will experience training and using machine learning to make predictions, investigate bias in machine learning applications, use generative adversarial networks to create novel works of art, and learn to recognize the AI you interact with daily and in the world around you.

This curriculum is currently being piloted through NSF EAGER Grant 2022502. This is a joint venture between the Personal Robots Group at the MIT Media Lab, MIT STEP Lab, and Boston College.

Contents:
Unit 0: What is AI?
- What is AI
- Algorithms as Opinions
- Ethical Matrix
- Decision Trees
- Investigating Bias
Unit 1: Supervised Machine Learning
- Introduction to Supervised Machine Learning
- Neural Networks
- Classifying AI vs. Generating AI
Unit 2: GANs
- What are GANs?
- Generator vs. Discriminator
- Unanticipated Consequences of Technology
- AI Generated Art
- What are Deepfakes?
- Spread of Misinformation
- Generate a Story
Unit 3: AI + My Future
- Environmental Impact of AI
- Redesign YouTube
- Careers in AI

The MIT Election Data and Science Lab (MEDSL) supports advances in election science by collecting, analyzing, and sharing core data and findings. The lab also aims to build relationships with election officials and others to help apply new scientific research to the practice of democracy in the United States.
By applying scientific principles to how elections are studied and administered, MEDSL aims to improve the democratic experience for all U.S. voters.
The MIT Election Lab is a founding partner in the Stanford-MIT Healthy Elections Project, which was developed to ensure that the 2020 election can proceed with integrity, safety, and equal access. The project aims to do this by bringing together academics, civic organizations, election administrators, and election administration experts to assess and promote best practices.

The MIT Governance Lab (MIT GOV/LAB) is an applied research group and ideas incubator that aims to improve democracy and governance by changing practice around corruption, government accountability, and citizen voice. Our model combines behavioral political science, experimental social science, design thinking, and evaluation to iterate on governance solutions that support people’s ability to hold the government to account. 
We partner with in-country practitioners, including government, civil society, and social enterprises, at every stage of the research and learning process, from theory building to theory testing, to critical reflections and adaptations in real time, with the goal of contributing to a solid evidence base to strengthen the overall field of practice for participatory governance. 
To learn more about our work, check out our latest updates, tools, guides, and other resources, as well as published research, or be in touch mitgovlab@mit.edu.

The MIT Prison Education Initiative (PEI) hosted this non-credit January IAP course to give the MIT community an opportunity to engage in discussions about mass incarceration and criminal justice reform. The Independent Activities Period (IAP) is a special four week term at MIT that runs from the first week of January until the end of the month.

Sloan's Teaching Resources Library provides open access to case studies and management simulations for management educators and students worldwide. This collection of teaching materials and games focuses on areas in which Sloan's innovative research and teaching are on the cutting edge, including action learning, entrepreneurship, leadership and ethics, operations management, strategy, sustainability, and system dynamics.
Formerly known as LearningEdge, and MIT Sloan Teaching Innovation Resources (MSTIR).
Online Publication