AI-Driven Interface Design for Intelligent Tutoring System Improves Student Engagement

• URL: http://arxiv.org/abs/2009.08976v1
• Date: Fri, 18 Sep 2020 10:32:01 GMT
• Title: AI-Driven Interface Design for Intelligent Tutoring System Improves Student Engagement
• Authors: Byungsoo Kim, Hongseok Suh, Jaewe Heo, Youngduck Choi
• Abstract summary: We explore AI-driven design for the interface of Intelligent Tutoring System (ITS) describing diagnostic feedback for students' problem-solving process. We propose several interface designs powered by different AI components and empirically evaluate their impacts on student engagement through Santa. Controlled A/B tests conducted on more than 20K students in the wild show that AI-driven interface design improves the factors of engagement by up to 25.13%.
• Score: 2.083729551844793
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An Intelligent Tutoring System (ITS) has been shown to improve students' learning outcomes by providing a personalized curriculum that addresses individual needs of every student. However, despite the effectiveness and efficiency that ITS brings to students' learning process, most of the studies in ITS research have conducted less effort to design the interface of ITS that promotes students' interest in learning, motivation and engagement by making better use of AI features. In this paper, we explore AI-driven design for the interface of ITS describing diagnostic feedback for students' problem-solving process and investigate its impacts on their engagement. We propose several interface designs powered by different AI components and empirically evaluate their impacts on student engagement through Santa, an active mobile ITS. Controlled A/B tests conducted on more than 20K students in the wild show that AI-driven interface design improves the factors of engagement by up to 25.13%.

Related papers

• A Mixed User-Centered Approach to Enable Augmented Intelligence in Intelligent Tutoring Systems: The Case of MathAIde app [1.5547343675151382]
  This study focuses on designing, developing, and evaluating MathAIde, an ITS that corrects mathematics exercises using computer vision and AI.<n>Our research contributes to the literature by providing a usable, teacher-centered design approach that involves teachers in all design phases.<n>As a practical implication, we highlight that the user-centered design approach increases the usefulness and adoption potential of AIED systems.
  arXiv  Detail & Related papers  (2025-07-31T18:56:01Z)
• A Comprehensive Review of AI-based Intelligent Tutoring Systems: Applications and Challenges [0.4369550829556578]
  We use a systematic literature review method to analyze numerous qualified studies published from 2010 to 2025.<n>The results reveal a complex landscape regarding the effectiveness of ITS, highlighting both advancements and persistent challenges.
  arXiv  Detail & Related papers  (2025-07-25T01:43:07Z)
• Educational impacts of generative artificial intelligence on learning and performance of engineering students in China [1.5954224931801726]
  This study investigated how 148 students from diverse engineering disciplines and regions across China used generative AI.<n>More than half of the participants reported a positive impact of generative AI on their learning efficiency, initiative, and creativity.<n>However, many felt their actual academic performance remained largely unchanged and expressed concerns about the accuracy and domain-specific reliability of generative AI.
  arXiv  Detail & Related papers  (2025-05-14T07:52:54Z)
• Transforming Student Evaluation with Adaptive Intelligence and Performance Analytics [0.0]
  This paper creates a system for the evaluation of students performance using Artificial intelligence. There are formats of questions in the system which comprises multiple choice, short answers and descriptive questions. The platform has intelligent learning progressions where the user will be able to monitor his/her performances to be recommended a certain level of performance.
  arXiv  Detail & Related papers  (2025-02-07T18:57:51Z)
• Survey of User Interface Design and Interaction Techniques in Generative AI Applications [79.55963742878684]
  We aim to create a compendium of different user-interaction patterns that can be used as a reference for designers and developers alike. We also strive to lower the entry barrier for those attempting to learn more about the design of generative AI applications.
  arXiv  Detail & Related papers  (2024-10-28T23:10:06Z)
• Effects of a Prompt Engineering Intervention on Undergraduate Students' AI Self-Efficacy, AI Knowledge and Prompt Engineering Ability: A Mixed Methods Study [36.48421439947282]
  This study designed and implemented a prompt engineering intervention at a university in Hong Kong. It examined students' AI self-efficacy, AI knowledge, and proficiency in creating effective prompts.
  arXiv  Detail & Related papers  (2024-07-30T15:05:24Z)
• The Evolution of Learning: Assessing the Transformative Impact of Generative AI on Higher Education [0.0]
  Generative Artificial Intelligence models such as ChatGPT have experienced a surge in popularity. This research paper investigates the impact of GAI on university students and Higher Education Institutions.
  arXiv  Detail & Related papers  (2024-04-16T13:19:57Z)
• How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey [48.97104365617498]
  The emerging area of em Explainable Interfaces (EIs) focuses on the user interface and user experience design aspects of XAI. This paper presents a systematic survey of 53 publications to identify current trends in human-XAI interaction and promising directions for EI design and development.
  arXiv  Detail & Related papers  (2024-03-21T15:44:56Z)
• Enhancing Students' Learning Process Through Self-Generated Tests [0.0]
  This paper describes an educational experiment aimed at the promotion of students' autonomous learning. The main idea is to make the student feel part of the evaluation process by including students' questions in the evaluation exams. Questions uploaded by students are visible to every enrolled student as well as to each involved teacher.
  arXiv  Detail & Related papers  (2024-03-21T09:49:33Z)
• Towards Goal-oriented Intelligent Tutoring Systems in Online Education [69.06930979754627]
  We propose a new task, named Goal-oriented Intelligent Tutoring Systems (GITS) GITS aims to enable the student's mastery of a designated concept by strategically planning a customized sequence of exercises and assessment. We propose a novel graph-based reinforcement learning framework, named Planning-Assessment-Interaction (PAI)
  arXiv  Detail & Related papers  (2023-12-03T12:37:16Z)
• Empowering Private Tutoring by Chaining Large Language Models [87.76985829144834]
  This work explores the development of a full-fledged intelligent tutoring system powered by state-of-the-art large language models (LLMs) The system is into three inter-connected core processes-interaction, reflection, and reaction. Each process is implemented by chaining LLM-powered tools along with dynamically updated memory modules.
  arXiv  Detail & Related papers  (2023-09-15T02:42:03Z)
• Implementing Learning Principles with a Personal AI Tutor: A Case Study [2.94944680995069]
  This research demonstrates the ability of personal AI tutors to model human learning processes and effectively enhance academic performance. By integrating AI tutors into their programs, educators can offer students personalized learning experiences grounded in the principles of learning sciences.
  arXiv  Detail & Related papers  (2023-09-10T15:35:47Z)
• Towards a Success Model for Automated Programming Assessment Systems Used as a Formative Assessment Tool [42.03652286907358]
  The assessment of source code in university education is a central and important task for lecturers of programming courses. The use of automated programming assessment systems (APASs) is a promising solution. Measuring the effectiveness and success of APASs is crucial to understanding how such platforms should be designed, implemented, and used.
  arXiv  Detail & Related papers  (2023-06-08T06:19:15Z)
• AGI: Artificial General Intelligence for Education [41.45039606933712]
  This position paper reviews artificial general intelligence (AGI)'s key concepts, capabilities, scope, and potential within future education. It highlights that AGI can significantly improve intelligent tutoring systems, educational assessment, and evaluation procedures. The paper emphasizes that AGI's capabilities extend to understanding human emotions and social interactions.
  arXiv  Detail & Related papers  (2023-04-24T22:31:59Z)
• Disadvantaged students increase their academic performance through collective intelligence exposure in emergency remote learning due to COVID 19 [105.54048699217668]
  During the COVID-19 crisis, educational institutions worldwide shifted from face-to-face instruction to emergency remote teaching (ERT) modalities. We analyzed data on 7,528 undergraduate students and found that cooperative and consensus dynamics among students in discussion forums positively affect their final GPA. Using natural language processing, we show that first-year students with low academic performance during high school are exposed to more content-intensive posts in discussion forums.
  arXiv  Detail & Related papers  (2022-03-10T20:23:38Z)
• Peer-inspired Student Performance Prediction in Interactive Online Question Pools with Graph Neural Network [56.62345811216183]
  We propose a novel approach using Graph Neural Networks (GNNs) to achieve better student performance prediction in interactive online question pools. Specifically, we model the relationship between students and questions using student interactions to construct the student-interaction-question network. We evaluate the effectiveness of our approach on a real-world dataset consisting of 104,113 mouse trajectories generated in the problem-solving process of over 4000 students on 1631 questions.
  arXiv  Detail & Related papers  (2020-08-04T14:55:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.