With the rapidly growing recognition of the role that computer science is playing in every aspect of society, enrollments in introductory computer science courses are increasing at an unprecedented pace. As a result of this phenomenal growth, departments of computer science are seeing extraordinary demand for introductory computer science courses. The accelerating growth in enrollments poses significant challenges for introductory programming instructors, who must teach increasingly larger classes while providing effective, engaging learning experiences for students. The overarching objective of this project is to develop an introductory programming teaching support environment, INSIGHT, that will enable instructors to readily understand their students��� progress through introductory computer science coding activities. INSIGHT will fundamentally change classroom dynamics by supporting both students and instructors.

The overarching objective of this project is to investigate how explanation-based classroom response systems can significantly improve student learning in STEM undergraduate education. It has been widely demonstrated that students who engage in self-explanation learn much more effectively than students who do not engage in self-explanation. By explaining concepts and examples as they learn, students trigger the self-explanation effect, which causes them to actively probe their own understanding, to learn much more deeply. However, students in undergraduate STEM courses have limited opportunity to engage in self-explanation. Building on our prior NSF-supported research on natural language processing-based STEM learning environments, we will investigate student learning in undergraduate STEM classrooms with an explanation-based classroom response system. The system will fundamentally change classroom dynamics by supporting both students and instructors. It will support students by instantly providing realtime formative assessment of their explanations. It will support instructors by instantly providing a summary and analysis of students������������������ explanations in aggregate, which will enable instructors to make immediate adjustments to pedagogy. Together, these benefits will synergistically lead to improved student learning and stronger student engagement in STEM classrooms.

Artificial Intelligence (AI) has emerged as a foundational technology that is profoundly reshaping society. With accelerating advances in a wide array of capabilities including natural language processing, computer vision, and machine learning, AI is quickly finding broad applications in every sector of society. Critically, AI holds significant transformative potential for improving human learning. This National Artificial Intelligence (AI) Research Institutes proposal centers on the establishment of the Institute for an AI-Engaged Future of Learning. Driven by a learner-centered vision of the potential of AI-augmented learning, the ENGAGE AI Institute will conduct (1) foundational AI research on natural language technologies, computer vision, and machine learning and (2) use-inspired AI research on AI-augmented learning, thereby creating learning experiences specifically designed to promote student engagement in formal and informal learning settings. The ENGAGE AI Institute brings together an exceptional interdisciplinary team spanning five organizations with deep expertise in AI and education, including four universities (North Carolina State University, the University of North Carolina at Chapel Hill, Vanderbilt University, and Indiana University) and Digital Promise, which will serve a ����������������nexus��������������� role for the Institute. The Institute will create AI-augmented learning technologies with specific foci on supporting two forms of engaging collaborative inquiry learning experiences: collaborative learning (problem solving and learning that play out in groups) and embodied learning (learning processes that are grounded in the interplay between the body, movement, and senses). The Institute will focus on AI-driven narrative-centered learning environments that create engaging story-based problem-solving experiences to support collaborative inquiry learning. The Institute will explore AI-augmented learning that operates at three levels: individuals, small groups, and larger groups within a range of educational contexts (e.g., classrooms, museums).

It has long been recognized that drawing can be a powerful approach to learning. Learning-by-drawing activates a complex set of cognitive processes that requires students to deeply engage with a subject matter. The project centers on the design, development, iterative refinement, and investigation of a sketch-based science learning environment. Specifically, the project will focus on the development and piloting of a sketch-based science learning environment to support students������������������ conceptual understanding of science with an emphasis on modeling. The project will culminate in a pilot study to investigate the effectiveness of the sketch-based learning environment for improving students������������������ factual understanding, their inferential understanding, and their ability to engage in science modeling. By utilizing a mixed methods approach integrating quantitative and qualitative work with learning analytics, it is anticipated that the project will yield theoretically-driven, empirically-based advances in sketch-based science learning environments that significantly improve conceptual understanding of science in upper elementary students.

The objective of the proposed research is to design, implement, and investigate ChangeGradients, a clinically integrated health behavior change system for adolescents. In a partnership with the University of California, San Francisco School of Medicine, we will create a computational behavior change framework based on sample-efficient policy gradient methods for reinforcement learning. The project will investigate a critical research question in health behavior change: how can a computational framework produce dynamically tailored interactive narratives that promote health behavior change for adolescents? ChangeGradients will support behavior change by generating personalized interactive narratives and delivering analytics to healthcare providers in a data-driven clinical intervention. ChangeGradients������������������ impact on health behavior change will be evaluated in a clinical study at the UCSF Benioff Children's Hospital.

Developing adaptive instruction for teams requires a new generation of Adaptive Instructional Systems that can accurately assess team behaviors in real-time. To effectively adapt tutoring to the complex dynamics of teams calls for the creation of computational models that can operationalize and assess team performance and deliver coaching and feedback to team members as they complete simulated training events. Recent advancements in deep learning-driven natural language processing and reinforcement learning offer significant promise for achieving these capabilities. The goal of this project is to develop tools and methods that can be used by team training researchers to automatically analyzing team communication data and devise tutorial planners that can deliver run time feedback during team training tasks in synthetic environments. In particular, the project will (1) investigate how advances in deep learning-driven natural language processing can be leveraged to analyze team discourse in order to help researchers automatically assess team communication and team performance and (2) investigate how data-driven machine learning approaches can be leveraged to devise tutorial planning models that can automatically deliver run-time feedback during team training tasks in simulated environments.

Effective teaching is the cornerstone of K-12 education. However, effective teaching occurs in complex workplaces that require teachers to cope with the real-time demands of providing effective learning experiences for large classrooms of students by skillfully bringing to bear their expertise in pedagogy and classroom management. Although there is enormous potential for enhancing teaching with technology-rich support that leverages artificial intelligence (AI), limited work has been done to investigate how emerging AI technologies can bring about fundamental improvements to the teaching profession. With recent advances in AI technologies for natural language processing, machine learning, and user-adaptive support, the time is ripe for transforming the professional lives of teachers. The objective of the proposed research is to design, develop, and evaluate the Intelligent-Augmented Cognition for Teaching (I-ACT) framework featuring intelligent cognitive assistants for K-12 teachers. A unique feature of I-ACT afforded by recent advances in machine learning will be its ability to optimize teacher support for collaborative learning at the individual student, group, and classroom levels

Artificial intelligence has emerged as a technology that promises to have unprecedented societal impact. Integrating AI into the science curriculum holds significant potential for introducing students to deep science inquiry while simultaneously providing them with an experiential understanding of the role that AI can play in science problem solving. The proposed project will center on the design, development, and investigation of PrimaryAI, a curricular framework that integrates science and AI for upper elementary science education. Featuring an immersive game-based learning environment, PrimaryAI will use problem-based learning as the foundation for science inquiry in which students grades 3-5 will utilize AI tools to solve complex ecosystem problems within an immersive science adventure. Students will engage in scientific problem solving tightly integrating AI and science to learn about ecosystems phenomena, mechanisms, and components that comprise a system, and make inferences about change over time for biological systems. The project will use design-based research to understand how best to integrate AI and science in upper-elementary science classrooms.

The proposed project focuses on integrating models of game-based and problem-based learning in a computer-supported collaborative learning environment (CSCL). As groups of students solve problems in these environments, their actions generate rich and dynamic streams of fine-grained multi-channel data that can be instrumented for investigating students' learning processes and outcomes. Using the big data generated by small groups, we will leverage learning analytics to provide adaptive support for collaboration that will allow these models to be used at larger scales in real classrooms. The project will study CSCL in the context of an environmental-science-based digital game that will employ specific strategies to support the problem-based learning goals of helping students construct explanations, reason effectively, and become self-directed learners. In problem-based learning, students are active, intentional learners who collaboratively negotiate meaning. The project will embed models induced using learning analytic techniques inside of a digital game environment to enable students to cultivate collaborative learning competencies that translate to non-digital classroom settings.

The ability to plan is a key element of learning. With the objective of improving middle school students' science learning, the project will investigate open learner models to scaffold student planning. The project will see the design, development, and investigation of an open learner model for student goal setting and planning. In contrast to the "classic" student models of intelligent tutoring systems, which are opaque, open learner models are inspectable: they enable students to inspect a learning environment's representation of their knowledge and competencies. Using the Future Worlds learning environment, the project will feature classroom studies that will investigate the impact of open learner models on both problem solving and learning in middle grades science.