相关论文: Advancing a Model of Students' Intentional Persist…
Machine learning systems are often deployed for making critical decisions like credit lending, hiring, etc. While making decisions, such systems often encode the user's demographic information (like gender, age) in their intermediate…
Machine learning (ML) algorithms are increasingly deployed to make critical decisions in socioeconomic applications such as finance, criminal justice, and autonomous driving. However, due to their data-driven and pattern-seeking nature, ML…
Generative AI (GenAI), which aims to synthesize realistic and diverse data samples from latent variables or other data modalities, has achieved remarkable results in various domains, such as natural language, images, audio, and graphs.…
Artificial intelligence (AI) is increasingly reshaping lifelong learning by introducing new possibilities for personalized, flexible, and data-informed educational practices. In the field of adult education, AI has gained particular…
Software engineering (SE) faces significant diversity challenges in both academia and industry, with underrepresented students encountering hostile environments, limited representation, and systemic biases that hinder their academic and…
The effective integration of generative artificial intelligence in education is a fundamental aspect to prepare future generations. The objective of this study is to analyze from a quantitative and qualitative point of view the perception…
The advent of Large Language Models (LLMs) started a serious discussion among educators on how LLMs would affect, e.g., curricula, assessments, and students' competencies. Generative AI and LLMs also raised ethical questions and concerns…
It is of critical importance to analyse the factors influencing college students' intention to use generative artificial intelligence (GenAI) to understand and predict learners' learning behaviours and academic outcomes. Nevertheless, a…
Machine learning (ML) techniques are increasingly prevalent in education, from their use in predicting student dropout, to assisting in university admissions, and facilitating the rise of MOOCs. Given the rapid growth of these novel uses,…
Privacy and fairness are two crucial pillars of responsible Artificial Intelligence (AI) and trustworthy Machine Learning (ML). Each objective has been independently studied in the literature with the aim of reducing utility loss in…
Humans are talented with the ability to perform diverse interactions in the teaching process. However, when humans want to teach AI, existing interactive systems only allow humans to perform repetitive labeling, causing an unsatisfactory…
While Machine Learning (ML) technologies are widely adopted in many mission critical fields to support intelligent decision-making, concerns remain about system resilience against ML-specific security attacks and privacy breaches as well as…
Many research fields are currently reckoning with issues of poor levels of reproducibility. Some label it a "crisis", and research employing or building Machine Learning (ML) models is no exception. Issues including lack of transparency,…
AI-supported tools can help learners overcome challenges in programming education by providing adaptive assistance. However, existing research often focuses on individual tools rather than deriving broader design recommendations. A key…
Rapid innovations in AI and large language models (LLMs) have accelerated the adoption of digital learning, particularly beyond formal education. What began as an emergency response during COVID-19 has shifted from a supplementary resource…
The influence of machine learning (ML) is quickly spreading, and a number of recent technological innovations have applied ML as a central technology. However, ML development still requires a substantial amount of human expertise to be…
Increasing student retention and persistence -- in particular classes or in their major area of study -- is a challenge for universities. Students' academic and social integration into an institution seems to be vital for student retention,…
Sustainability and efficiency have become essential considerations in the development and deployment of Artificial Intelligence systems, but existing regulatory practices for Green AI still lack standardized, model-agnostic evaluation…
Machine learning has proved useful in many software disciplines, including computer vision, speech and audio processing, natural language processing, robotics and some other fields. However, its applicability has been significantly hampered…
In response to the increasing mental health challenges faced by college students, we sought to understand their perspectives on how AI applications, particularly Large Language Models (LLMs), can be leveraged to enhance their mental…