Learning sciences emerged in the 1980s from roots in cognitive science and AI, as well as other social sciences such as linguistics and anthropology. LS distinctively investigates the process of learning, in contrast to research on teaching, instructional design, policies, and institutions. LS also focuses on research in authentic educational settings, which contrasts with psychology or brain science research conducted in a laboratory. Learning scientists use and develop a variety of methods and theories, leading to a large body of knowledge. The field also has blurry lines with neighboring fields such as AI in education.

Even staple figures in the field of science such as Stephen Hawking and Elon Musk have been vocal about technology's threat against humanity. The facts of the matter are that machines generally operate how they're programmed to and we are a long way from developing the ASI (artificial superintelligence) needed for this "takeover" to even be feasible. At present, most of the AI technology utilized by machines is considered "narrow" or "weak," meaning it can only apply its knowledge towards one or a few tasks. "Machine learning and AI systems are a long way from cracking the hard problem of consciousness and being able to generate their own goals contrary to their programming," George Montanez, a data scientist at Microsoft, wrote under the same Metafact thread. Some of these risks include overoptimization, weaponization, and ecological collapse, according to Ben Nye, the Director of Learning Sciences at the University of Southern California, Institute for Creative Technologies (USC-ICT).

Interest and investment in AI for Education is accelerating. So is concern about the issues that will arise when AI is widely implemented in educational technologies -- such as bias, fairness, and data security. With our team at the Center for Integrative Research in the Computing and Learning Sciences (CIRCLS), we see that organizations around the world--like UNESCO or the new EdSafe AI Alliance--are organizing people to tackle the issues. In the US, organizations like Stanford's HAI are addressing the issues of AI in healthcare, but not so much in education. Over the past year, my colleagues organized a working group in AI and education policy.

Amazon's Global Learning & Development (GLD) Learning Science and Engineering team is growing quickly and is looking for a skilled, driven, applied scientist to develop solutions and innovate at the intersection of human and machine learning. The Learning Science and Engineering org is reinventing workplace learning by building the programs, products, technologies and mechanisms that make learning effective and scalable for Amazon employees. We use machine learning to augment human decision-making to accelerate and personalize learning and skill development. We have a passion for raising the bar on learning and learning design at Amazon, and simultaneously contributing to the science of human and machine learning. We partner with multiple businesses across Amazon to explore ways to help Amazon employees grow their knowledge and capabilities.

Last week, I reviewed Thomas Siebel's surprisingly good Digital Transformation: Survive and Thrive in an Era of Mass Extinction. The components that makeup Siebel's digital transformation are: cloud computing, big data, IoT (internet of things), and AI (artificial intelligence). In that review, I asked if any books examine the impact of these technologies on the future of higher ed? But maybe that is the wrong question. A better approach might be to ask: what are the academic analogs of each of the components of digital transformation?

The organizations that will prevail in the current transformation are those whose employees can learn fastest and make the best decisions. At all ages, learning readiness is influenced by technological familiarity and fluency. Our hope for solving the seemingly intractable global problems includes an optimistic outlook on the partnership between artificial intelligence and human intelligence – person by person. You'll also hear from leading executives at Konica Minolta, Cigna, Cisco, Fujitsu and Xerox. A.I. Expert Neil Jacobstein and VR Expert Andrew Wasserman will speak on the importance of these technologies in this new frontier.

This article is aimed at helping AI researchers and practitioners imagine roles intelligent technologies might play in the many different and varied ecosystems in which people learn. My observations are based on learning sciences research of the past several decades, the possibilities of new technologies of the past few years, and my experience as program officer for the National Science Foundation’s Cyberlearning and Future Learning Technologies program. My thesis is that new technologies have potential to transform possibilities for fostering learning in both formal and informal learning environments by making it possible and manageable for learners to engage in the kinds of project work that professionals engage in and learn important content, skills, practices, habits, and dispositions from those experiences. The expertise of AI researchers and practitioners is critical to that vision, but it will require teaming up with others — for example, technology imagineers, educators, and learning scientists.