Related papers: An Artificial Intelligence driven Learning Analyti…
Collaborative training can improve the accuracy of a model for a user by trading off the model's bias (introduced by using data from other users who are potentially different) against its variance (due to the limited amount of data on any…
This paper presents the design and refinement of automated Moodle-based Problem-Solving Assessments (PSAs) deployed across large-scale computing units. Developed to replace traditional exams, PSAs assess applied problem-solving skills…
Multi-modal learning aims to enhance performance by unifying models from various modalities but often faces the "modality imbalance" problem in real data, leading to a bias towards dominant modalities and neglecting others, thereby limiting…
AI-augmented classrooms generate rich teacher and student feedback before graded outcomes become available, yet these signals can be difficult to translate into timely instructional decisions. We propose an interpretable decision layer: a…
Collaborative bandit learning, i.e., bandit algorithms that utilize collaborative filtering techniques to improve sample efficiency in online interactive recommendation, has attracted much research attention as it enjoys the best of both…
Artificial Intelligence (AI) plays varying roles in supporting both existing and emerging technologies. In the area of Learning and Tutoring, it plays key role in Intelligent Tutoring Systems (ITS). The fusion of ITS with Adaptive…
The process of painting fosters creativity and rational planning. However, existing generative AI mostly focuses on producing visually pleasant artworks, without emphasizing the painting process. We introduce a novel task, Collaborative…
Sequential reasoning in agent systems has been significantly advanced by large language models (LLMs), yet existing approaches face limitations. Reflection-driven reasoning relies solely on knowledge in pretrained models, limiting…
As Large Language Models (LLMs) get integrated into diverse workflows, they are increasingly being regarded as "collaborators" with humans, and required to work in coordination with other AI systems. If such AI collaborators are to reliably…
This work describes our group's submission to the PROCESS Challenge 2024, with the goal of assessing cognitive decline through spontaneous speech, using three guided clinical tasks. This joint effort followed a holistic approach,…
There are various teaching methods developed in order to attain successful delivery of a subject without prior knowledge of the interaction among the students in a class. Social network analysis can be used to identify individual,…
Human collective intelligence has proved itself as an important factor in a society's ability to accomplish large-scale behavioral feats. As societies have grown in population-size, individuals have seen a decrease in their ability to…
The use of AI assistants, along with the challenges they present, has sparked significant debate within the community of computer science education. While these tools demonstrate the potential to support students' learning and instructors'…
In a conventional supervised learning setting, a machine learning model has access to examples of all object classes that are desired to be recognized during the inference stage. This results in a fixed model that lacks the flexibility to…
Collaborative AI systems (CAISs) aim at working together with humans in a shared space to achieve a common goal. This critical setting yields hazardous circumstances that could harm human beings. Thus, building such systems with strong…
Collaboration literacy requires adapting to the evolving demands of group work within complex discussions, making it difficult to develop and assess. Traditional analytics metrics capture behavioral signals while missing the semantic…
This work aims to propose a method to support students in finding appropriate peers in collaborative and blended learning settings. The main goal of this research is to bridge the gap between pedagogical theory and data driven practice to…
Automatically understanding the contents of an image is a highly relevant problem in practice. In e-commerce and social media settings, for example, a common problem is to automatically categorize user-provided pictures. Nowadays, a…
We study the multi-task learning problem that aims to simultaneously analyze multiple datasets collected from different sources and learn one model for each of them. We propose a family of adaptive methods that automatically utilize…
The implementation of teaching interventions in learning needs has received considerable attention, as the provision of the same educational conditions to all students, is pedagogically ineffective. In contrast, more effectively considered…