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 controlled study to evaluate its impact on students' learning and perception of the ACPS hints. The study includes an analysis of how these outcomes are modulated by several user characteristics such as personality traits and cognitive abilities, to asses if explanations should be personalized to these characteristics. Our results indicate that providing explanations increase students' trust in the ACPS hints, perceived usefulness of the hints, and intention to use them again. In addition, we show that students' access of the explanation and learning gains are modulated by user characteristics, providing insights toward designing personalized Explainable AI (XAI) for ITS.
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…
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…
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…
On the Role of Domain Experts in Creating Effective Tutoring Systems
Department of Computer Science, Colorado State University, USA（科罗拉多州立大学计算机科学系）；Colorado State University Extension, USA（科罗拉多州立大学扩展服务）；School of Education, Colorado State University, USA（科罗拉多州立大学教育学院）
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 Hypermedia and Multimedia (AHAM) form the backbone of Adaptive eLearning Systems (AES) which provides personalized experiences to learners. This experience is important because it facilitates the accurate delivery of the learning modules in specific to the learner capacity and readiness. AES types vary, with Adaptive Web Based eLearning Systems (AWBES) being the popu…
Recently, we have seen a rapid rise in usage of online educational platforms. The personalized education became crucially important in future learning environments. Knowledge tracing (KT) refers to the detection of students' knowledge states and predict future performance given their past outcomes for providing adaptive solution to Intelligent Tutoring Systems (ITS). Bayesian Knowledge Tracing (BKT) is a model to capture mastery level of each skill with psychologically meaningful parameters and widely used in successful tutoring systems. However, it is unable to detect learning transfer across…
One Size doesn't Fit All: A Personalized Conversational Tutoring Agent for Mathematics Instruction
School of Computer Science, Wuhan University（武汉大学计算机学院）；DAMO Academy, Alibaba Group（阿里巴巴达摩院）；College of Computer and Cyber Security, Hebei Normal University（河北师范大学计算机与网络空间安全学院）
针对数学教学中学习者差异被忽视的问题，本文提出 PACE 个性化对话辅导智能体，结合学习风格画像、苏格拉底式反馈与个性化训练，提升教学适应性和学习效果。
Predicting student performance is a fundamental task in Intelligent Tutoring Systems (ITSs), by which we can learn about students' knowledge level and provide personalized teaching strategies for them. Researchers have made plenty of efforts on this task. They either leverage educational psychology methods to predict students' scores according to the learned knowledge proficiency, or make full use of Collaborative Filtering (CF) models to represent latent factors of students and exercises. However, most of these methods either neglect the exercise-specific characteristics (e.g., exercise mater…
RAG-PRISM: A Personalized, Rapid, and Immersive Skill Mastery Framework with Adaptive Retrieval-Augmented Tutoring
College of Information Science, University of Arizona, Tucson, AZ, USA（信息科学学院，亚利桑那大学）；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）；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）；Department of Industrial Engineering, University of Sonora, Hermosillo, Mexico（工业工程系，索诺拉大学）；Leonhard Center for Enhancement of Engineering Education, The Pennsylvania State University, University Park, PA, USA（工程教育增强中心，宾夕法尼亚州立大学）
University of Agder（阿格德大学）；Oslo and Akershus University College（奥斯陆与阿克什胡斯大学学院）
With the increasing popularity of online learning, intelligent tutoring systems are regaining increased attention. In this paper, we introduce adaptive algorithms for personalized assignment of learning tasks to student so that to improve his performance in online learning environments. As main contribution of this paper, we propose a a novel Skill-Based Task Selector (SBTS) algorithm which is able to approximate a student's skill level based on his performance and consequently suggest adequate assignments. The SBTS is inspired by the class of multi-armed bandit algorithms. However, in contras…
Should XAI Nudge Human Decisions with Explanation Biasing?
Tokyo Metropolitan University（东京都立大学）；National Institute of Informatics（信息学研究所）；The Graduate University for Advanced Studies (SOKENDAI)（高级研究大学院大学（SOKENDAI））
IntelliCode: A Multi-Agent LLM Tutoring System with Centralized Learner Modeling
School of Computer Science and Engineering, VIT-AP University（计算机科学与工程学院，VIT-AP大学）；School of Electrical and Computer Sciences, Indian Institute of Technology Bhubaneswar（电气与计算机科学学院，印度理工学院布巴内斯瓦尔学院）
IBM TJ Watson Research Center（IBM TJ沃森研究中心）；Sesame Workshop（芝麻街工作室）
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 pictur…
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）
Riiid! AI Research（Riiid! AI研究院）；University of Michigan（密歇根大学）；Yale University（耶鲁大学）
Intelligent Tutoring Systems (ITSs) have been developed to provide students with personalized learning experiences by adaptively generating learning paths optimized for each individual. Within the vast scope of ITS, score prediction stands out as an area of study that enables students to construct individually realistic goals based on their current position. Via the expected score provided by the ITS, a student can instantaneously compare one's expected score to one's actual score, which directly corresponds to the reliability that the ITS can instill. In other words, refining the precision of…
Research on intelligent tutoring systems has been exploring data-driven methods to deliver effective adaptive assistance. While much work has been done to provide adaptive assistance when students seek help, they may not seek help optimally. This had led to the growing interest in proactive adaptive assistance, where the tutor provides unsolicited assistance upon predictions of struggle or unproductivity. Determining when and whether to provide personalized support is a well-known challenge called the assistance dilemma. Addressing this dilemma is particularly challenging in open-ended domains…
Artificial Intelligence and Cost Reduction in Public Higher Education: A Scoping Review of Emerging Evidence
School of Science and Technology, Hellenic Open University（希腊开放大学科学与技术学院）；School of Humanities, Hellenic Open University（希腊开放大学人文学院）；School of Social Sciences, Hellenic Open University（希腊开放大学社会科学学院）
Michigan Technical University（密歇根理工大学）；Institute for Human and Machine Cognition（人类与机器认知研究所）；MacroCognition, LLC（MacroCognition有限责任公司）
This is an integrative review that address the question, "What makes for a good explanation?" with reference to AI systems. Pertinent literatures are vast. Thus, this review is necessarily selective. That said, most of the key concepts and issues are expressed in this Report. The Report encapsulates the history of computer science efforts to create systems that explain and instruct (intelligent tutoring systems and expert systems). The Report expresses the explainability issues and challenges in modern AI, and presents capsule views of the leading psychological theories of explanation. Certain…
Intelligent Tutoring Systems have become critically important in future learning environments. Knowledge Tracing (KT) is a crucial part of that system. It is about inferring the skill mastery of students and predicting their performance to adjust the curriculum accordingly. Deep Learning-based KT models have shown significant predictive performance compared with traditional models. However, it is difficult to extract psychologically meaningful explanations from the tens of thousands of parameters in neural networks, that would relate to cognitive theory. There are several ways to achieve high …
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）
A Scalable AI Driven, IoT Integrated Cognitive Digital Twin for Multi-Modal Neuro-Oncological Prognostics and Tumor Kinetics Prediction using Enhanced Vision Transformer and XAI
University of Calcutta（加尔各答大学）；Institute of Engineering and Management（工程与管理学院）；University of Engineering and Management（工程与管理大学）；San Francisco State University（旧金山州立大学）
