ASCI: AI-Smart Classroom Initiative

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.

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Publication Details

Published
2025-02-12
DOI
https://doi.org/10.1145/3641554.3701957
Citations
1
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
1.69
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ASCI: AI-Smart Classroom Initiative

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1 citations
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ASCI: AI-Smart Classroom Initiative

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article en
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Abstract

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.

University of Virginia (US)
Openalex Percentile: Top 15%
Online Learning and Analytics
1.69
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