This paper presents our experiences in designing, implementing, and piloting an intelligent vocabulary learning tutor. The design builds on several intelligent tutoring design concepts, including graph-based knowledge representation, learner modeling, and adaptive learning content and assessment exposition. Specifically, we design a novel phased learner model approach to enable systematic exposure to words during vocabulary instruction. We also built an example application over the tutor platform that uses a learning activity involving videos and an assessment activity involving word to picture/image association. More importantly, the tutor adapts to the significant variation in children's knowledge at the beginning of kindergarten, and evolves the application at the speed of each individual learner. A pilot study with 180 kindergarten learners allowed the tutor to collect various kinds of activity information suitable for insights and interventions both at an individual- and class-level. The effort also demonstrates that we can do A/B testing for a variety of hypotheses at scale with such a framework.
The University of British Columbia（不列颠哥伦比亚大学）；Augsburg University（奥格斯堡大学）
Our research is a step toward ascertaining the need for personalization, in XAI, and we do so in the context of investigating the value of explanations of AI-driven hints and feedback are useful in Intelligent Tutoring Systems (ITS). We added an explanation functionality for the adaptive hints provided by the Adaptive CSP (ACSP) applet, an interactive simulation that helps students learn an algorithm for constraint satisfaction problems by providing AI-driven hints adapted to their predicted level of learning. We present the design of the explanation functionality and the results of a controll…
Korbit Technologies Inc.（科比科技公司）；University of Cambridge（剑桥大学）；École de Technologie Supérieure（高等技术学院）；McGill University（麦吉尔大学）；MILA (Quebec Artificial Intelligence Institute)（MILA（魁北克人工智能研究所））
We investigate how automated, data-driven, personalized feedback in a large-scale intelligent tutoring system (ITS) improves student learning outcomes. We propose a machine learning approach to generate personalized feedback, which takes individual needs of students into account. We utilize state-of-the-art machine learning and natural language processing techniques to provide the students with personalized hints, Wikipedia-based explanations, and mathematical hints. Our model is used in Korbit, a large-scale dialogue-based ITS with thousands of students launched in 2019, and we demonstrate th…
Open TutorAI: An Open-source Platform for Personalized and Immersive Learning with Generative AI
IRF-SIC Laboratory, Ibnou Zohr University（IRF-SIC实验室，伊本·扎赫尔大学）；Regional Center for Education（教育与培训专业地区中心）；Polydisciplinary Faculty of Taroudant, Ibnou Zohr University（塔鲁旦多学科学院，伊本·扎赫尔大学）；Higher School of Technology of Guelmim, Ibnou Zohr University（盖尔米姆技术高等学校，伊本·扎赫尔大学）；Paragraphe laboratory, Paris 8 and CY Cergy Paris Universities（Paragraphe实验室，巴黎8大学和CY塞克巴黎大学）
Despite artificial intelligence (AI) having transformed major aspects of our society, less than a fraction of its potential has been explored, let alone deployed, for education. AI-powered learning can provide millions of learners with a highly personalized, active and practical learning experience, which is key to successful learning. This is especially relevant in the context of online learning platforms. In this paper, we present the results of a comparative head-to-head study on learning outcomes for two popular online learning platforms (n=199 participants): A MOOC platform following a tr…
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）
TutorLLM: Customizing Learning Recommendations with Knowledge Tracing and Retrieval-Augmented Generation
University of Southampton（索姆塞特大学）；Durham University（杜 ham 大学）；Japan Advanced Institute of Science and Technology（日本先进科学和技术研究院）；Newcastle University（新卡斯尔大学）
Knowledge-Based Design Requirements for Generative Social Robots in Higher Education
Zurich University of Applied Sciences（应用科学大学苏黎世）；University of Applied Sciences and Arts Northwestern Switzerland（西北瑞士应用科学与艺术大学）；Bielefeld University（比勒菲尔德大学）
SingaKids: A Multilingual Multimodal Dialogic Tutor for Language Learning
Nanyang Technological University, Singapore（新加坡南洋理工大学）；National Institute of Education (NIE), Singapore（新加坡国家教育研究所）；Institute for Infocomm Research (I 2 R), A*STAR, Singapore（新加坡资讯与通信研究院（I2R））
E-Learning systems (ELS) and Intelligent Tutoring Systems (ITS) play a significant part in today's education programs. Sequencing questions is the art of generating a personalized quiz for a target learner. A personalized test will enrich the learner's experience and will contribute to a more effective and efficient learning process. In this paper, we used the Neural Collaborative Filtering (NCF) model to generate question sequencing and compare it to a pair-wise memory-based question sequencing algorithm - EduRank. The NCF model showed significantly better ranking results than the EduRank mod…
CoderAgent: Simulating Student Behavior for Personalized Programming Learning with Large Language Models
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China（认知智能国家重点实验室，中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（人工智能研究院，合肥综合性国家科学中心）
