This project directly contributes to the main goals of the NSF DRK-12 program through the design, implementation, and evaluation of a project-based integrated science and AI curriculum and technology. The project has the overarching goal of preparing a diverse, computationally-competent next-generation STEM workforce through devising an effective, engaging, and inclusive learning environment. In particular, we will create a learning environment for computational modeling that features custom code blocks that facilitate the implementation of AI algorithms in science-related contexts while obfuscating unnecessary programming complications. Our curricular modules will integrate important AI concepts with the high school STEM curriculum following NGSS standards.

In this proposal, we aim to design, build, and integrate a novel student modeling engine to provide adaptive scaffolding to programming students. We bring in our expertise in student modeling coupled with LLM's capability in deep analysis of programming snippets to address challenges associated with the temporal ill-defined nature of programming education. This entails effective representation of students' programming processes and capturing their evolving competency in foundational programming knowledge and skills.

The ever-increasing surge in interest in computer science (CS) education, coupled with the unprecedented changes brought about by the emergence of generative programming tools, underscores the imperative for educational advancements in this field. These advancements may include understanding the most effective approaches to support students' learning integrated within state-of-the-art adaptive technologies that can provide CS students with effective, scalable, and individualized educational support. Our proposal centers on the creation of a personalized multi-level programming practice environment. This will be achieved by leveraging models of students' learning, a vast corpus of worked examples and practice problems, and the latest advancements in machine learning and generative AI. Through this proposal, we aim to conduct extensive studies to discern the most effective types of programming exercises for enhancing students' learning, taking into account their practice history and learning trajectories. The insights gained from these studies will be seamlessly integrated into our personalized CS education learning environment. This integration will result in tailored exercises that match each student's competency level, accompanied by individualized support to guide them through the exercises. To validate the effectiveness of our approach, we plan to evaluate it in several introductory programming classrooms across a variety of institutions. These institutions include large state universities and small HBCU colleges, ensuring that our solution addresses the diverse needs of students across different educational settings.

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 Early Research Scholars Program (ERSP) is a group-based, dual-mentored research structure designed to provide a supportive and inclusive research experience using equity-based practices to grow the confidence and foundational skills of early-career students, particularly African Americans, Hispanics, Native Americans and women. For this NSF subaward from UC San Diego, we plan to add ERSP to our course catalog and start implementing it in Fall 2021. As part of their full-year apprenticeship, teams of students will learn about graduate school, be matched to research mentors, observe the mentor's lab, participate in the ERSP course, and propose an independent research project.

Existing research suggests that institutions may be able to increase the persistence of women in STEM by increasing their self-assessed STEM ability. We propose conducting both a longitudinal field experiment (in Computer Science [CS] classes) and a lab experiment (with novice programmers) to assess the impact of unambiguous, direct performance feedback on women������������������s and men������������������s self-assessed CS ability and CS persistence. Beyond the support for our research provided by social-psychological theory, mediation analysis of pilot data from a field experiment found the predicted causal chain: the intervention increased women������������������s self-assessed CS ability, which then increased women������������������s CS persistence intentions.