相关论文: Tooling for digital accessibility in mathematics: …
This tutorial addresses the challenge of incorporating large language models (LLMs), such as ChatGPT, in a data analytics class. It details several new in-class and out-of-class teaching techniques enabled by AI. For example, instructors…
The rapidly growing demand for high-quality data in Large Language Models (LLMs) has intensified the need for scalable, reliable, and semantically rich data preparation pipelines. However, current practices remain dominated by ad-hoc…
Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing these datasets requires both sufficient compute power and…
Data assimilation (DA) enables hydrologic models to update their internal states using near-real-time observations for more accurate forecasts. With deep neural networks like long short-term memory (LSTM), using either lagged observations…
Analyzing and evaluating students' progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus…
Data storytelling workflows ask learners to integrate analytical, design, and narrative skills, but instructors rarely have the capacity to provide detailed feedback at each step. Computational and AI-assisted storytelling offers…
The Documentational Approach To Didactics (DAD) aims to study teachers' professional development through their interactions with their resources for/from teaching. It has been introduced in the French community of didactics of mathematics…
Large Language Models (LLMs) have the potential to transform the way a dynamic curriculum can be delivered. However, educators face significant challenges in interacting with these models, particularly due to complex prompt engineering and…
Context: Test Automation (TA) techniques are crucial for quality assurance in software engineering but face limitations such as high test suite maintenance costs and the need for extensive programming skills. Artificial Intelligence (AI)…
Modern software systems require various capabilities to meet architectural and operational demands, such as the ability to scale automatically and recover from sudden failures. Self-adaptive software systems have emerged as a critical focus…
Recent advances in collaborative knowledge distillation have demonstrated cutting-edge performance for resource-constrained distributed multimedia learning scenarios. However, achieving such competitiveness requires addressing a fundamental…
The Covid-19 pandemic outbreak has forced all courses to be carried out online, but only a few truly fulfill student expectations. This study aims to explain the characteristics of enjoyable online learning based on the platform, the…
In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users…
Instructors play a pivotal role in integrating AI into education, yet their adoption of AI-powered tools remains inconsistent. Despite this, limited research explores how to design AI tools that support broader instructor adoption. This…
Consider the following task taught in introductory optimization courses which addresses challenges articulated by the community at the intersection of (generative) AI and OR: generate the dual of a linear program. LLMs, being trained at…
Recent technological developments and advances in Artificial Intelligence (AI) have enabled sophisticated capabilities to be a part of Digital Twin (DT), virtually making it possible to introduce automation into all aspects of work…
Past research has defined a general process for the data-driven redesign of educational technologies and has shown that in carefully-selected instances, this process can help make systems more effective. In the current work, we test the…
We present a perspective on platforms for code submission and automated evaluation in the context of university teaching. Due to the COVID-19 pandemic, such platforms have become an essential asset for remote courses and a reasonable…
Data assimilation (DA) combines partial observations with dynamical models to improve state estimation. Filter-based DA uses only past and present data and is the prerequisite for real-time forecasts. Smoother-based DA exploits both past…
There is increasing interest in distilling task-specific knowledge from large language models (LLM) to smaller student models. Nonetheless, LLM distillation presents a dual challenge: 1) there is a high cost associated with querying the…