Cognitive diagnosis aims to gauge students' mastery levels based on their response logs. Serving as a pivotal module in web-based online intelligent education systems (WOIESs), it plays an upstream and fundamental role in downstream tasks like learning item recommendation and computerized adaptive testing. WOIESs are open learning environment where numerous new students constantly register and complete exercises. In WOIESs, efficient cognitive diagnosis is crucial to fast feedback and accelerating student learning. However, the existing cognitive diagnosis methods always employ intrinsically transductive student-specific embeddings, which become slow and costly due to retraining when dealing with new students who are unseen during training. To this end, this paper proposes an inductive cognitive diagnosis model (ICDM) for fast new students' mastery levels inference in WOIESs. Specifically, in ICDM, we propose a novel student-centered graph (SCG). Rather than inferring mastery levels through updating student-specific embedding, we derive the inductive mastery levels as the aggregated outcomes of students' neighbors in SCG. Namely, SCG enables to shift the task from finding the most suitable student-specific embedding that fits the response logs to finding the most suitable representations for different node types in SCG, and the latter is more efficient since it no longer requires retraining. To obtain this representation, ICDM consists of a construction-aggregation-generation-transformation process to learn the final representation of students, exercises and concepts. Extensive experiments across real-world datasets show that, compared with the existing cognitive diagnosis methods that are always transductive, ICDM is much more faster while maintains the competitive inference performance for new students.

Online learning has soared in popularity in the educational landscape of COVID-19 and carries the benefits of increased flexibility and access to far-away training resources. However, it also restricts communication between peers and teachers, limits physical interactions and confines learning to the computer screen and keyboard. In this project, we designed a novel way to engage students in collaborative online learning by using haptic-enabled tangible robots, Cellulo. We built a library which connects two robots remotely for a learning activity based around the structure of a biological cell. To discover how separate modes of haptic feedback might differentially affect collaboration, two modes of haptic force-feedback were implemented (haptic co-location and haptic consensus). With a case study, we found that the haptic co-location mode seemed to stimulate collectivist behaviour to a greater extent than the haptic consensus mode, which was associated with individualism and less interaction. While the haptic co-location mode seemed to encourage information pooling, participants using the haptic consensus mode tended to focus more on technical co-ordination. This work introduces a novel system that can provide interesting insights on how to integrate haptic feedback into collaborative remote learning activities in future.

Generative Artificial Intelligence (GAI) models such as ChatGPT have experienced a surge in popularity, attracting 100 million active users in 2 months and generating an estimated 10 million daily queries. Despite this remarkable adoption, there remains a limited understanding to which extent this innovative technology influences higher education. This research paper investigates the impact of GAI on university students and Higher Education Institutions (HEIs). The study adopts a mixed-methods approach, combining a comprehensive survey with scenario analysis to explore potential benefits, drawbacks, and transformative changes the new technology brings. Using an online survey with 130 participants we assessed students' perspectives and attitudes concerning present ChatGPT usage in academics. Results show that students use the current technology for tasks like assignment writing and exam preparation and believe it to be a effective help in achieving academic goals. The scenario analysis afterwards projected potential future scenarios, providing valuable insights into the possibilities and challenges associated with incorporating GAI into higher education. The main motivation is to gain a tangible and precise understanding of the potential consequences for HEIs and to provide guidance responding to the evolving learning environment. The findings indicate that irresponsible and excessive use of the technology could result in significant challenges. Hence, HEIs must develop stringent policies, reevaluate learning objectives, upskill their lecturers, adjust the curriculum and reconsider examination approaches.

Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal that involves creating agents that can sense, perceive, reason about, learn from, and respond to affect, behavior, and cognition of other agents (human or artificial). Progress towards Social-AI has accelerated in the past decade across several computing communities, including natural language processing, machine learning, robotics, human-machine interaction, computer vision, and speech. Natural language processing, in particular, has been prominent in Social-AI research, as language plays a key role in constructing the social world. In this position paper, we identify a set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI. We anchor our discussion in the context of social intelligence concepts and prior progress in Social-AI research.

A teacher's knowledge base consists of knowledge of mathematics content, knowledge of student epistemology, and pedagogical knowledge. It has severe implications on the understanding of student's knowledge of content, and the learning context in general. The necessity to formalize the different content knowledge in approximate senses is recognized in the education research literature. A related problem is that of coherent formalizability. Existing responsive or smart AI-based software systems do not concern themselves with meaning, and trained ones are replete with their own issues. In the present research, many issues in modeling teachers' understanding of content are identified, and a two-tier rough set-based model is proposed by the present author for the purpose of developing software that can aid the varied tasks of a teacher. The main advantage of the proposed approach is in its ability to coherently handle vagueness, granularity and multi-modality. An extended example to equational reasoning is used to demonstrate these. The paper is meant for rough set researchers intending to build logical models or develop meaning-aware AI-software to aid teachers, and education research experts.

Generative AI (GenAI) marked a shift from AI being able to recognize to AI being able to generate solutions for a wide variety of tasks. As the generated solutions and applications become increasingly more complex and multi-faceted, novel needs, objectives, and possibilities have emerged for explainability (XAI). In this work, we elaborate on why XAI has gained importance with the rise of GenAI and its challenges for explainability research. We also unveil novel and emerging desiderata that explanations should fulfill, covering aspects such as verifiability, interactivity, security, and cost. To this end, we focus on surveying existing works. Furthermore, we provide a taxonomy of relevant dimensions that allows us to better characterize existing XAI mechanisms and methods for GenAI. We discuss different avenues to ensure XAI, from training data to prompting. Our paper offers a short but concise technical background of GenAI for non-technical readers, focusing on text and images to better understand novel or adapted XAI techniques for GenAI. However, due to the vast array of works on GenAI, we decided to forego detailed aspects of XAI related to evaluation and usage of explanations. As such, the manuscript interests both technically oriented people and other disciplines, such as social scientists and information systems researchers. Our research roadmap provides more than ten directions for future investigation.

Model-based reinforcement learning is an effective approach for controlling an unknown system. It is based on a longstanding pipeline familiar to the control community in which one performs experiments on the environment to collect a dataset, uses the resulting dataset to identify a model of the system, and finally performs control synthesis using the identified model. As interacting with the system may be costly and time consuming, targeted exploration is crucial for developing an effective control-oriented model with minimal experimentation. Motivated by this challenge, recent work has begun to study finite sample data requirements and sample efficient algorithms for the problem of optimal exploration in model-based reinforcement learning. However, existing theory and algorithms are limited to model classes which are linear in the parameters. Our work instead focuses on models with nonlinear parameter dependencies, and presents the first finite sample analysis of an active learning algorithm suitable for a general class of nonlinear dynamics. In certain settings, the excess control cost of our algorithm achieves the optimal rate, up to logarithmic factors. We validate our approach in simulation, showcasing the advantage of active, control-oriented exploration for controlling nonlinear systems.

The integration With the surge in data-centric AI and its increasing capabilities, AI of AI into everyday life will only increase as new applications applications have become a part of our everyday lives. However, are developed for use in our homes, schools, governments, social misunderstandings regarding their capabilities, limitations, and lives, and workplaces. But despite the progress made, we have associated advantages and disadvantages are widespread. Consequently, also seen how serious the consequences of misunderstanding or in the university setting, there is a crucial need to educate failing to question AI decisions can be - leading to issues such as not only computer science majors but also students from various disciplines viral misinformation [8], biased systems that disproportionately about AI. In this experience report, we present an overview impact marginalized communities [1], and serious concerns about of an introductory course that we offered to students coming from data privacy. This situation highlights the need to bridge the gap different majors. Moreover, we discuss the assignments and quizzes between AI's everyday presence and people's lack of knowledge, so of the course, which provided students with a firsthand experience we can clear up misconceptions, reduce fears, and embrace a more of AI processes and insights into their learning patterns. Additionally, informed relationship with the AI that is shaping our future [4].

As artificial intelligence (AI) continues to penetrate ever deeper into modern life, one particular family of machine learning algorithms--namely, deep neural networks--have come to be seen as the solution to many of the challenges that have stumped more classical algorithms in the past. Modeled loosely on the structure of biological neural networks, artificial neural networks consist of chains of simple mathematical transformations that can model complex non-linear decision boundaries in large problem spaces. In particular, deep neural networks--artificial neural networks that consist of multiple layers of transformations--allow for sufficient complexity to tackle tasks in a wide variety of fields. These models are collectively and more colloquially referred to as deep learning. A growing body of education researchers are now also turning their attention to leveraging the power of deep learning algorithms for the tasks of improving and understanding human learning. Researchers in educational data science, a field consisting of various interrelated research communities such as Educational Data Mining (EDM), Learning Analytics (LA), and AI in Education (AIED), have been involved in this endeavor.

This paper presents gnss lib py, a Python library used to parse, analyze, and visualize data from a variety of GNSS (Global Navigation Satellite Systems) data sources. The gnss lib py library's ease of use, modular capabilities, testing coverage, and extensive documentation make it an attractive tool not only for scientific and industry users wanting a quick, out-of-the-box solution but also for advanced GNSS users developing new GNSS algorithms. Metadata Metadata for the gnss lib py library is included in the ancillary data table 1. 1. Motivation and significance Global Navigation Satellite Systems (GNSS) are used globally for positioning, navigation, and timing across industries such as transportation, agriculture, power systems, and finance [1]. Several countries and political entities have developed global and regional satellite constellations such as GPS and WAAS (United States), GLONASS (Russia), BeiDou (China), Galileo (the European Union), QZSS (Japan), and IRNSS (India). GNSS technology, policy, and services are an active research area with established research journals and technical conferences.