For example, in online learning, an AI social assistant may connect learners and thereby enhance social interaction. These social AI assistants too need to explain themselves in order to enhance transparency and trust with the learners. We present a method of self-explanation that uses introspection over a self-model of an AI social assistant. The self-model is captured as a functional model that specifies how the methods of the agent use knowledge to achieve its tasks. The process of generating self-explanations uses Chain of Thought to reflect on the self-model and ChatGPT to provide explanations about its functioning. We evaluate the self-explanation of the AI social assistant for completeness and correctness. We also report on its deployment in a live class.

A modern GITA guidance, one that uses artificial intelligence, can help in finding a solution to life problems. The AI stall, set up as part of the exhibition under the first digital economy working group meeting of G20 nations in Lucknow, gives a glimpse into this. GITA is an acronym that means guidance, inspiration, transformation and action. "The software has included all verses from the Bhagavad Gita that are used when someone asks a question. The answers are given using AI to help find solutions to problems in life," said Akash Goel of Tagbin, who had installed the AI technology.

Many AI courses include design and programming projects that provide students with opportunities for experiential learning. Design and programming projects in courses on knowledge-based AI typically explore topics in knowledge, memory, reasoning, and learning. Traditional AI curricula, however, seldom highlight issues of modality of representations, often focusing solely on propositional representations. In this paper, we report on an investigation into learning about representational modality through a series of projects based around geometric analogy problems similar to the Raven's Progressive Matrices test of intelligence. We conducted this experiment over three years, from Fall 2010 through Fall 2012, in a class on knowledge-based AI. We used the methodology of action research in which the teacher is also the researcher. We discovered that students found these projects motivating, engaging, and challenging, in several cases investing significant time and posting their work online. From our perspective, the projects accomplished the goal of learning about representational modality in addition to knowledge representation and reasoning.

We describe an experiment in using IBM's Watson cognitive system to teach about human-computer co-creativity in a Georgia Tech Spring 2015 class on computational creativity. The project-based class used Watson to support biologically inspired design, a design paradigm that uses biological systems as analogues for inventing technological systems. The twenty-four students in the class self-organized into six teams of four students each, and developed semester-long projects that built on Watson to support biologically inspired design. In this paper, we describe this experiment in using Watson to teach about human-computer co-creativity, present one project in detail, and summarize the remaining five projects. We also draw lessons on building on Watson for (i) supporting biologically inspired design, and (ii) enhancing human-computer co-creativity.

We present Octopus, an AI agent to jointly balance three conflicting task objectives on a micro-crowdsourcing marketplace – the quality of work, total cost incurred, and time to completion. Previous control agents have mostly focused on cost-quality, or cost-time tradeoffs, but not on directly controlling all three in concert. A naive formulation of three-objective optimization is intractable; Octopus takes a hierarchical POMDP approach, with three different components responsible for setting the pay per task, selecting the next task, and controlling task-level quality. We demonstrate that Octopus significantly outperforms existing state-of-the-art approaches on real experiments. We also deploy Octopus on Amazon Mechanical Turk, showing its ability to manage tasks in a real-world, dynamic setting.

Higher education already employs artificial intelligence in a number of effective ways--course and facilities scheduling, student recruitment campaign development, endowment investments and support, and many other operational activities are guided by AI at large institutions. The programs that run AI--algorithms--can use big data to project or predict outcomes based on machine learning, in which the computer "learns" to adapt to a myriad of changing elements, conditions and trends. Adaptive learning is one of the early applications of AI to the actual teaching and learning process. In this case AI is employed to orchestrate the interaction between the learner and instructional material. This enables the program to most efficiently guide the learner to meet desired outcomes based upon the unique needs and preferences of the learner. Using a series of assessments, the algorithm presents a customized selection of instructional materials adapted to what the learner has demonstrated mastery over and what the learner has yet to learn.

Imagine a classroom where student teams are learning with a computer simulation, planning a scientific expedition to Mars. They might be challenged to think about the tools they need or the clothing and food they will bring. As the students make decisions about their voyage to the red planet, the simulation changes until each group is following a storyline all their own. That level of personalized learning is just one vision of researchers who are harnessing artificial intelligence to improve education. They're getting a boost through 11 grants of $20 million each that the National Science Foundation has awarded to establish new AI research programs for education and other fields.

Georgia Tech is a major partner in a new National Science Foundation (NSF) Artificial Intelligence Research Institute focused on adult learning in online education, it was announced today. Led by the Georgia Research Alliance, the National AI Institute for Adult Learning in Online Education (ALOE) is one of 11 new NSF institutes created as part of an investment totaling $220 million. The ALOE Institute will develop new AI theories and techniques for enhancing the quality of online education for lifelong learning and workforce development. According to some projections, about 100 million American workers will need to be reskilled or upskilled over the next decade. With the increase of AI and automation, said Co-Principal Investigator and Georgia Tech lead Professor Ashok Goel, many jobs will be redefined. "There will be some loss of jobs, but mostly we will see individuals needing to learn a new skill to get a new job or to advance their career," said Goel, a professor of computer science and human-centered computing in Georgia Tech's School of Interactive Computing (IC) and the chief scientist with the Center for 21st Century Universities (C21U).

In 2015, Ashok Goel and his colleagues at the Georgia Institute of Technology informed a class of students that a new teaching assistant named Jill Watson would be joining their course on artificial intelligence. They left out an important detail, however: Jill Watson is, herself, an artificial intelligence agent. It wasn't until late in the term that students started to suspect that the answers to their online queries were not coming from a flesh-and-blood TA. Since then, Jill Watson has participated in 17 classes held both online and in person, at both undergraduate and graduate levels, in subjects ranging from biology to engineering and computer science. Meanwhile, Georgia Tech continues to explore the potential of AI in higher education. The academic landscape was already being transformed by economics and technology before the massive disruption of COVID-19.

Chatbots are being used increasingly to automate communication. For the most part, this has meant customer service chatbots which use advanced technology -- like AI-natural language processing -- to intelligently answer customer requests. More recently, businesses have started investigating how chatbots may be useful within an organization. Chatbots, which have access to vast knowledge stores and powerful language processing technology, may also be useful in training employees.. Here are four different ways that chatbots can help people build skills.