The Artificial Intelligence Smart Classroom Initiative (ASCI) presents a re-imagined set of online course tools, designed primarily to support growing computer science classes. The system has four primary tools: an office hours queue, an automatic student grouping algorithm, a course-specific local large-language model (LLM), and administration tools for detecting students and TAs that need support. These tools interoperate to improve the quality of one another (e.g., LLM conversations support students directly in the office hours queue) and are enhanced by synchronizing data from multiple external sources such as Piazza, Gradescope, and Canvas. The system has been deployed in multiple courses over the past three semesters: initially as a FIFO queue, then supporting manual grouping and smart grouping of office hour attendees, and recently including LLM support. Preliminary results indicate that students who were grouped using the tool were more likely to return to the queue more than twice as often (on average) than those who were not. However, while grouping in office hours has the potential to decrease student wait times, teaching assistants and students tend to favor one-on-one meetings over group meetings. This might be improved in the future with updates to the software, TA training, and incorporation of other supporting tools (e.g., LLM technology). The other, newer, tools will be more thoroughly evaluated in future semesters.

As undergraduate enrollment in computer science rises, instructors continue to investigate methods to improve the student experience at scale. One aspect commonly used in courses at scale is queue-driven office hours, in which students join an online queue and meet with teaching assistants on a first-come, first-serve basis (FIFO).

Program Visualizations (PVs) have been used as educational tools to allow students to visually inspect the runtime behavior of their code. However, many of these systems act as low-level visual debuggers not high-level abstractions of program behavior. Additionally, evaluations of these systems tend to focus more on student engagement or opinion in using the system and not on artifacts produced using the system. This paper discusses the effectiveness of a PV developed to aide students in an undergraduate Artificial Intelligence class on a pathfinding homework assignment. Students in 4 semesters of the course were tasked to develop pathfinding algorithms for an agent to navigate worlds in cases of both certain and uncertain world information. Students in 2 semesters of the course were given access to a PV that allowed them to see a visual representation of their agent navigating the world in either information condition. The final agents developed by these students were compared with those developed by students who never received the PV. Comparisons were made on the performance of these agents in both cases of uncertain and certain world information on several test worlds. Student written reports for the Experimental condition were also analyzed. The results showed significant differences in the performance of the algorithms developed in both certain and uncertain world information. Student reflections on using the PV within the written reports provide insight into how the PV informed the design and development of their submission.

We describe how web service architectures can provide better performance to applications by offering fine-grained services. We define web service granularity in terms of the amount of data that can be retrieved from a service in a single request on average. This is important because developers cannot predict if students will be using state of the art hardware. Thus, service-oriented architectures (SOA) with fine service granularity can minimize network communication and allow server machines to perform more work for applications. We present the Rashi Intelligent Tutoring System and describe how its architecture has been adapted into a web service with two competing application interfaces. We show how the interface that uses more fine-grained services leads to significant improvements in network message response time, message size, and response size, without a significant change in the number of requests.

We present an educational 3D game called Rashi Game, which instructs students via exploration using the inquiry teaching method. We first describe the Rashi Intelligent Tutoring System in its original form, and then describe the details and features of Rashi Game, the 3D game that we have developed. In particular, we argue that inquiry-learning environments are particularly viable for educational 3D games. This is because of the similarities between some common game mechanics and the inquiry-learning paradigm. These include freedom to explore open-ended environments, interaction with environments, and realistic scenarios. We briefly summarizing results that have been obtained via pilot studies of the efficacy of Rashi Game, and remark on what directions future work in educational 3D games might take.

We describe an educational 3D game called Rashi Game. Rashi Game features a fully functional, and open-ended, 3D environment for students backed by a domain-independent inquiry-learning tutor. We present pilot work that directly compares Rashi's classic 2D interface against the 3D game environment. We compare both the student's work within the system, as well as their reported sense of presence. Specifically, we notice some interesting patterns in student behavior within the game, dependent on the student's preference for games, and argue that there may be potential for modeling when and how to present a student with an educational game based on simple factors such as whether or not the student plays games regularly, or based on student affect (e.g. a lack of motivation).