AI promises personalized learning and scalable education. As AI agents increasingly permeate education in support of teaching and learning, there is a critical and urgent need for data architectures for collecting and analyzing data on learning, and feeding the results back to teachers, learners, and the AI agents for personalization of learning at scale. At the National AI Institute for Adult Learning and Online Education, we are developing an Architecture for AI-Augmented Learning (A4L) for supporting adult learning through online education. We present the motivations, goals, requirements of the A4L architecture. We describe preliminary applications of A4L and discuss how it advances the goals of making learning more personalized and scalable.

In Jill Watson has been equipped with OpenAI's GPT-this paper, we analyze student interactions with Jill across 3.5 Turbo model, accessed via the OpenAI API, and coupled multiple courses and colleges, focusing on the types and with several other technologies to facilitate more nuanced, complexity of student questions based on Bloom's Revised context-aware, and safe interactions with students. Jill has Taxonomy and tool usage patterns. We find that, by supporting been deployed in both online and offline classrooms[10] across a wide range of cognitive demands, Jill encourages different educational institutes and courses. This paper examines students to engage in sophisticated, higher-order cognitive student interactions with Jill Watson, to understand questions. However, the frequency of usage varies significantly how AI-based educational tools may engage students in meaningful across deployments, and the types of questions asked and deeper learning experiences.

Conversational AI agents often require extensive datasets for training that are not publicly released, are limited to social chit-chat or handling a specific domain, and may not be easily extended to accommodate the latest advances in AI technologies. This paper introduces Jill Watson, a conversational Virtual Teaching Assistant (VTA) leveraging the capabilities of ChatGPT. Jill Watson based on ChatGPT requires no prior training and uses a modular design to allow the integration of new APIs using a skill-based architecture inspired by XiaoIce. Jill Watson is also well-suited for intelligent textbooks as it can process and converse using multiple large documents. We exclusively utilize publicly available resources for reproducibility and extensibility. Comparative analysis shows that our system outperforms the legacy knowledge-based Jill Watson as well as the OpenAI Assistants service. We employ many safety measures that reduce instances of hallucinations and toxicity. The paper also includes real-world examples from a classroom setting that demonstrate different features of Jill Watson and its effectiveness.

With the rapid evolution of Natural Language Processing (NLP), Large Language Models (LLMs) like ChatGPT have emerged as powerful tools capable of transforming various sectors. Their vast knowledge base and dynamic interaction capabilities represent significant potential in improving education by operating as a personalized assistant. However, the possibility of generating incorrect, biased, or unhelpful answers are a key challenge to resolve when deploying LLMs in an education context. This work introduces an innovative architecture that combines the strengths of ChatGPT with a traditional information retrieval based chatbot framework to offer enhanced student support in higher education. Our empirical evaluations underscore the high promise of this approach.

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.

ProJo can also help students work together and assess their growth and weaknesses, in both robot form and on a computer screen. It is one of a variety of teaching aids in development, boosted by artificial intelligence, that scientists and educators say could support tomorrow's classrooms. Typically, AI education products serve one function, such as assessing a student's literacy, tailoring tools to individual learners or performing administrative functions such as grading. Next-generation tools may do all of this in a single platform, serving at times as a peer learning partner, a group facilitator and a monitor for educators--a sort of superpowered teacher's assistant personalized for each student. A look at how innovation and technology are transforming the way we live, work and play.

In this panel, AI faculty with experience teaching online and blended classes were asked to share their experiences teaching online classes. The panel was composed of Ashok Goel, Georgia Institute of Technology, Ansaf Salleb-Aouissi, Columbia University and Mehran Sahami, Stanford University. The panelists were asked to describe which tools and methods work well to help instructors engage and bond with students online. They were furthermore asked to share their insights into which components of a course can be done best online and which ones are best accomplished in person. The panel took place as part of the 2021 Symposium on Educational Advances of AI, which was collocated with AAAI-21.

Humans tutoring other humans works pretty well. The trouble is, it requires a lot of people. Artificially intelligent tools tutoring humans works pretty well, too--but building those digital systems takes time and expertise. So researchers hoping to engineer better teaching and learning systems are working to unlock a new level of education efficiency by creating AI tools that make it easier for almost anyone to build an AI tutor. "We are trying to leverage the joint power of human tutoring and computer tutoring," says Ken Koedinger, a professor of human-computer interaction and psychology at Carnegie Mellon University.

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.

We have developed an AI-powered socio-technical system for making online learning in higher education more accessible, affordable and achievable. In particular, we have developed four novel and intertwined AI technologies: (1) VERA, a virtual experimentation research assistant for supporting inquiry-based learning of scientific knowledge, (2) Jill Watson Q&A, a virtual teaching assistant for answering questions based on educational documents including the VERA user reference guide, (3) Jill Watson SA, a virtual social agent that promotes online interactions, and (4) Agent Smith, that helps generate a Jill Watson Q&A agent for new documents such as class syllabi. The results are positive: (i) VERA enhances ecological knowledge and is freely available online; (ii) Jill Watson Q&A has been used by >4,000 students in >12 online classes and saved teachers >500 hours of work; (iii) Jill Q&A and Jill Watson SA promote learner engagement, interaction, and community; and (iv). Agent Smith helps generate Jill Watson Q&A for a new syllabus within ~25 hours. Put together, these innovative technologies help make online learning simultaneously more accessible (by making materials available online), affordable (by saving teacher time), and achievable (by providing learning assistance and fostering student engagement).