The shortage of people trained in STEM fields is becoming acute, and universities and colleges are straining to satisfy this demand. In the case of computer science, for instance, the number of US students taking introductory courses has grown three-fold in the past decade. Recently, massive open online courses (MOOCs) have been promoted as a way to ease this strain. This at best provides access to education. The bigger challenge though is coping with heterogeneous backgrounds of different students, retention, providing feedback, and assessment. Personalized education relying on computational tools can address this challenge.
While automated tutoring has been studied at different times in different communities, recent advances in computing and education technology offer exciting opportunities to transform the manner in which students learn. In particular, at least three trends are significant. First, progress in logical reasoning, data analytics, and natural language processing has led to tutoring tools for automatic assessment, personalized instruction including targeted feedback, and adaptive content generation for a variety of subjects. Second, research in the science of learning and human-computer interaction is leading to a better understanding of how different students learn, when and what types of interventions are effective for different instructional goals, and how to measure the success of educational tools. Finally, the recent emergence of online education platforms, both in academia and industry, is leading to new opportunities for the development of a shared infrastructure. This CCC workshop brought together researchers developing educational tools based on technologies such as logical reasoning and machine learning with researchers in education, human-computer interaction, and cognitive psychology.
Given recent advances in information technology and artificial intelligence, web-based education systems have became complementary and, in some cases, viable alternatives to traditional classroom teaching. The popularity of these systems stems from their ability to make education available to a large demographics (see MOOCs). However, existing systems do not take advantage of the personalization which becomes possible when web-based education is offered: they continue to be one-size-fits-all. In this paper, we aim to provide a first systematic method for designing a personalized web-based educ…
Tailoring the presentation of information to the needs of individual students leads to massive gains in student outcomes~\cite{bloom19842}. This finding is likely due to the fact that different students learn differently, perhaps as a result of variation in ability, interest or other factors~\cite{schiefele1992interest}. Adapting presentations to the educational needs of an individual has traditionally been the domain of experts, making it expensive and logistically challenging to do at scale, and also leading to inequity in educational outcomes. Increased course sizes and large MOOC enrollmen…
University of Tübingen（图宾根大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；University of Miami（迈阿密大学）；University of Cambridge（剑桥大学）
The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to ultimately meet her desired goal. This concept emerged several years ago and is being adopted by a rapidly-growing number of educational institutions around the globe. In recent years, the boost of artificial intelligence (AI) and machine learning (ML), together with the advances in big data analysis, has unfolded novel perspectives to enhance personalized education in numerous dimensions. By taking advantage of AI/ML methods, the educ…
The Grainger College of Engineering, Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, 205 N Matthews, Urbana, IL 61801（伊利诺伊大学厄巴纳-香槟分校格拉inger工程学院，土木与环境工程系）
The modern educational ecosystem is not one-size fits all. Scholars are accustomed to personalization in their everyday life and expect the same from education systems. Additionally, the COVID-19 pandemic placed us all in an acute teaching and learning laboratory experimentation which now creates expectations of self-paced learning and interactions with focused educational materials. Consequently, we examine how learning objectives can be achieved through a learning platform that offers content choices and multiple modalities of engagement to support self-paced learning, and propose an approac…
Rice University（莱斯大学）；University of Cambridge（剑桥大学）；Eedi；Microsoft Research（微软研究院）；Alan Turing Institute（艾伦·图灵研究所）
Digital technologies are becoming increasingly prevalent in education, enabling personalized, high quality education resources to be accessible by students across the world. Importantly, among these resources are diagnostic questions: the answers that the students give to these questions reveal key information about the specific nature of misconceptions that the students may hold. Analyzing the massive quantities of data stemming from students' interactions with these diagnostic questions can help us more accurately understand the students' learning status and thus allow us to automate learnin…
This competition concerns educational diagnostic questions, which are pedagogically effective, multiple-choice questions (MCQs) whose distractors embody misconceptions. With a large and ever-increasing number of such questions, it becomes overwhelming for teachers to know which questions are the best ones to use for their students. We thus seek to answer the following question: how can we use data on hundreds of millions of answers to MCQs to drive automatic personalized learning in large-scale learning scenarios where manual personalization is infeasible? Success in using MCQ data at scale he…
Personalized Education with Generative AI and Digital Twins: VR, RAG, and Zero-Shot Sentiment Analysis for Industry 4.0 Workforce Development
Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, USA（电气与计算机工程系，亚利桑那大学，图森，亚利桑那州，美国）；Department of Systems and Industrial Engineering, University of Arizona, Tucson, AZ, USA（系统与工业工程系，亚利桑那大学，图森，亚利桑那州，美国）；Department of Industrial Engineering, University of Sonora, Hermosillo, Mexico（工业工程系，索诺拉大学，赫尔莫索，墨西哥）
Agent4Edu: Generating Learner Response Data by Generative Agents for Intelligent Education Systems
State Key Laboratory of Cognitive Intelligence（认知智能国家重点实验室）；University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence（人工智能研究院）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）
Online education platforms enable teachers to share a large number of educational resources such as questions to form exercises and quizzes for students. With large volumes of available questions, it is important to have an automated way to quantify their properties and intelligently select them for students, enabling effective and personalized learning experiences. In this work, we propose a framework for mining insights from educational questions at scale. We utilize the state-of-the-art Bayesian deep learning method, in particular partial variational auto-encoders (p-VAE), to analyze real s…
PRISM: A Personalized, Rapid, and Immersive Skill Mastery framework for personalizing experiential learning through Generative AI
Department of Electrical and Computer Engineering, University of Arizona（电气与计算机工程系，亚利桑那大学）；Department of Systems and Industrial Engineering, University of Arizona（系统与工业工程系，亚利桑那大学）；Department of Industrial Engineering, University of Sonora（工业工程系，索诺拉大学）；Leonhard Center for Enhancement of Engineering Education, The Pennsylvania State University（工程教育增强中心，宾夕法尼亚州立大学）
Beyond Flashcards: Designing an Intelligent Assistant for USMLE Mastery and Virtual Tutoring in Medical Education (A Study on Harnessing Chatbot Technology for Personalized Step 1 Prep)
Students in online courses generate large amounts of data that can be used to personalize the learning process and improve quality of education. In this paper, we present the Latent Skill Embedding (LSE), a probabilistic model of students and educational content that can be used to recommend personalized sequences of lessons with the goal of helping students prepare for specific assessments. Akin to collaborative filtering for recommender systems, the algorithm does not require students or content to be described by features, but it learns a representation using access traces. We formulate thi…
Educational Institutions have an essential role in promoting the teaching and learning process, within universities, colleges, and communities. Due to the recent coronavirus COVID 19 pandemic, many educational institutions adopted hybrid learning (HL), which is a combination of classic and online learning. It integrates the advantages of both, and it is a fundamental factor to ensure continued learning. Technological innovations such as HL are changing the teaching process, and how students, lecturers, and administrators interact. Based on this, the Community College of Qatar (CCQ) focused on …
Agentic Workflow for Education: Concepts and Applications
East China Normal University（华东师范大学）；Shanghai Institute of Artificial Intelligence for Education（上海人工智能教育研究院）；Nanyang Technological University（南洋理工大学）；National Institute of Education（国立教育学院）
e-Education has developed as one of the most encouraging territories. The Indian Government is investing all amounts of energy to improve education among the residents of the nation. School and graduate understudies are focused on, however the stage is being created for all the residents seeking to learn. Without a doubt, the objective is to build the quantity of literates with advanced education. To accomplish the equivalent, propels in Data and Correspondence innovation are being utilized in the education division, which has cleared route for e-Training in India as well. To help educators in…
