相关论文: Optimization- and AI-based approaches to academic …
Entity rankings (e.g., institutions, journals) are a core component of academia and related industries. Existing approaches to institutional rankings have relied on a variety of data sources, and approaches to computing outcomes, but remain…
As Artificial Intelligence (AI) increasingly impacts professional practice, there is a growing need to AI-related competencies into higher education curricula. However, research on the implementation of AI education within study programs…
Public AI benchmark results are widely broadcast by model developers as indicators of model quality within a growing and competitive market. However, these advertised scores do not necessarily reflect the traits of interest to those who…
Determining the reputation of academic journals is an crucial issue. The Author Affiliation Index (AAI) was proposed as a novel indicator for judging journal quality in many academic disciplines. Nevertheless, the original AAI has several…
The evaluation of academic theses is a cornerstone of higher education, ensuring rigor and integrity. Traditional methods, though effective, are time-consuming and subject to evaluator variability. This paper presents RubiSCoT, an…
Learning difficulties pose significant challenges for students, impacting their academic performance and overall educational experience. These difficulties could sometimes put students into a downward spiral that lack of educational…
We present a framework that utilizes quantum algorithms, an architecture aware quantum noise model and an ideal simulator to benchmark quantum computers. The benchmark metrics highlight the difference between the quantum computer evolution…
The recent developments in Artificial Intelligence (AI) technologies challenge educators and educational institutions to respond with curriculum and resources that prepare students of all ages with the foundational knowledge and skills for…
This work is a preliminary exploratory study of how we could progress a step towards an AI assisted article classification sys- tem in academia. The proposed system aims to aid the journal editors in their decisions by pinpointing the…
In the midst of the rapid integration of artificial intelligence (AI) into real world applications, one pressing challenge we confront is the phenomenon of model drift, wherein the performance of AI models gradually degrades over time,…
As Large Language Models (LLMs) advance, their potential for widespread societal impact grows simultaneously. Hence, rigorous LLM evaluations are both a technical necessity and social imperative. While numerous evaluation benchmarks have…
I propose a new model to measure simultaneously academic and financial performances of scientific activities quantitatively. The tool is very simple and can be applied to any branches of science, while it is also adjustable to varying…
Student engagement with large language models (LLMs) in academic writing is not a stable trait, an adoption decision, or a competency level; it is a continuously negotiated process that existing frameworks cannot adequately theorize.…
Modern artificial intelligence (AI) applications require large quantities of training and test data. This need creates critical challenges not only concerning the availability of such data, but also regarding its quality. For example,…
Quality and diversity are two critical metrics for the training data of large language models (LLMs), positively impacting performance. Existing studies often optimize these metrics separately, typically by first applying quality filtering…
The proponents of Aspect Oriented Software Development (AOSD) methodology have done a tremendous amount of work to bring out the positive effects of its adoption using quantitative assessment. A structured assessment of the methodology…
Agentic Artificial Intelligence (AI) represents a paradigm shift from reactive systems to proactive, autonomous decision making frameworks. Existing AI-based educational systems remain fragmented and lack multi-level integration across…
Generative adversarial networks (GANs) have achieved impressive results today, but not all generated images are perfect. A number of quantitative criteria have recently emerged for generative model, but none of them are designed for a…
Stochastic simulation has been widely used to analyze the performance of complex stochastic systems and facilitate decision making in those systems. Stochastic simulation is driven by the input model, which is a collection of probability…
Quantum computers have the potential to provide an advantage over classical computers in a number of areas. Numerous metrics to benchmark the performance of quantum computers, ranging from their individual hardware components to entire…