Founded in 2003, the Campus Consortium is a leading non-profit education association with thousands of higher education institutions and K-12 school district members. The Campus Consortium mission is to help members reduce the time, cost, and effort associated with implementing enterprise IT services by leveraging shared IT services, lessons learned, and best practices so that each member can avoid reinventing the wheel when adopting new education technologies.

The increase in online education has allowed a new type of teacher to emerge -- an artificial one. But just how accepting students are of an artificial instructor remains to be seen. That's why researchers at the University of Central Florida's Nicholson School of Communication and Media are working to examine student perceptions of artificial intelligence-based teachers. Some of their findings, published recently in the International Journal of Human-Computer Interaction, indicate that for students to accept an AI teaching assistant, it needs to be effective and easy to talk to. The hope is that by understanding how students relate to AI-teachers, engineers and computer scientists can design them to easily integrate into the education experience, says Jihyun Kim, an associate professor in the school and lead author of the study.

In less than a year, these same scholars will be writing about the "future of labor," and given the speed of innovation, by the time these articles are published, they will be made obsolete. Based on the research recently conducted by the newly formed Stanford Digital Economy Lab, before the pandemic, in early March, remote workers represented 15% of the workforce in enterprise-level organizations. Now that number is well over 50%, and the trajectory is steepening daily as we record higher levels of infections. In this article, we will attempt to build on our current understanding of the future of work and to identify potential challenges that could arise for different demographic groups. Dropbox was the first major digitized company to announce a remote-first work environment for all of its employees.

In June, the school board in Rio Rancho, New Mexico, was facing a series of votes on the budget for an elaborate and expensive reopening plan. Among the big-ticket items was a tablet designed to screen students and staff for fevers. The devices were sold by a company named OneScreen, which supplies schools with technology including "smart" whiteboards and attendance apps. But this spring, it had pivoted. Its new product, called GoSafe, could scan foreheads for elevated temperatures and detect when students aren't wearing masks.

Educational technology leadership is by no means uniform across institutions. The work is variously distributed among CIOs, CTOs, teaching and learning centers, academic administration, online learning outfits and sometimes even smaller-scale labs, institutes or departments. On most campuses there is not yet an ed tech center of gravity around which the others orbit. Institutions must empower chief educational technology leaders as true partners in developing the core university strategy for the next era of learning. The modern era of ed tech parallels the development of information technology in general.

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Carnegie Mellon's Robotics Academy studies how teachers use robots in classrooms to teach Computer Science, Science, Technology Engineering, and Mathematics (CS-STEM). Our mission is to use the educational affordances of robotics to excite students about science and technology. The Robotics Academy fulfills its mission by developing research-based solutions for teachers that are classroom-tested and foreground CS-STEM concepts. Visit our Research area to learn more about our projects, partners, and funding.

Motivated by applications such as college admission and insurance rate determination, we propose an evaluation problem where the inputs are controlled by strategic individuals who can modify their features at a cost. A learner can only partially observe the features, and aims to classify individuals with respect to a quality score. The goal is to design an evaluation mechanism that maximizes the overall quality score, i.e., welfare, in the population, taking any strategic updating into account. We further study the algorithmic aspect of finding the welfare maximizing evaluation mechanism under two specific settings in our model. When scores are linear and mechanisms use linear scoring rules on the observable features, we show that the optimal evaluation mechanism is an appropriate projection of the quality score. When mechanisms must use linear thresholds, we design a polynomial time algorithm with a (1/4)-approximation guarantee when the underlying feature distribution is sufficiently smooth and admits an oracle for finding dense regions. We extend our results to settings where the prior distribution is unknown and must be learned from samples.