Generative Intelligence and Intelligent Tutoring Systems : 20th International Conference, ITS 2024, Thessaloniki, Greece, June 10-13, 2024, Proceedings, Part I

• Autore: Sifaleras Angelo
• Pubblicazione: Cham : , : Springer International Publishing AG, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (450 pages)
• Altri autori: LinFuhua
• Nota di contenuto: Intro -- Preface -- Organization -- Invited Talks -- Unleashing Potential: Harnessing the Power of Generative AI in Intelligent Tutoring Systems -- Sharing from Experience: Competencies for "Intelligent Dialogues" with Emerging Technologies -- Contents - Part I -- Contents - Part II -- Generative Intelligence and Tutoring Systems -- Using Large Language Models to Support Teaching and Learning of Word Problem Solving in Tutoring Systems -- 1 Introduction -- 2 Skill-Based Categorization -- 3 Methodology -- 3.1 Dataset -- 3.2 LLMs Considered -- 3.3 Experimental Setting -- 4 Results -- 5 Conclusions -- References -- A Generative Approach for Proactive Assistance Forecasting in Intelligent Tutoring Environments -- 1 Introduction -- 2 Related Work -- 2.1 Assistance Dilemma and Proactive Hints -- 2.2 Sequence Modelling -- 3 Proposed Method -- 4 Experimental Setting -- 4.1 Dataset -- 4.2 Model Training -- 4.3 Evaluation Metric -- 5 Results and Discussion -- 5.1 Comparison to Other Methods -- 5.2 Ablation Study -- 6 Conclusion -- References -- Combined Maps as a Tool of Concentration and Visualization of Knowledge in the Logic of Operation of the Intelligent Tutoring Systems -- 1 Introduction -- 2 Existing Solutions -- 3 Method -- 3.1 Methodological Approach to Data Generalization -- 3.2 CMKD Method -- 3.3 Combined Map as an Element of ITS Operation Logic -- 4 Experiment -- 5 Results and their Analysis -- 6 Conclusion -- References -- Fast Weakness Identification for Adaptive Feedback -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 3.1 Domain Model -- 3.2 Student Model -- 3.3 Transition Model -- 3.4 Bandit Model -- 3.5 Weak KC Identification Algorithms -- 4 Simulations -- 4.1 Simulations with Real-Student Dataset Using IRT -- 4.2 Simulations with Random Domain Graphs and Simulated Students -- 4.3 Experiments and Results -- 4.4 Discussion. ; 5 Conclusion and Future Work -- References -- QuizMaster: An Adaptive Formative Assessment System -- 1 Introduction -- 2 Background and Related Work -- 3 System Overview -- 3.1 System Architecture -- 3.2 Content Library -- 3.3 Course Model -- 3.4 Question Sequencing and Student Model -- 3.5 Formative Feedback Generation and Presentation -- 4 Evaluation of Methods and Future Testing -- 5 Conclusions and Future Work -- References -- Preliminary Systematic Review of Open-Source Large Language Models in Education -- 1 Introduction and Literature -- 2 Methods -- 2.1 Research Questions -- 2.2 Search Strategy -- 2.3 Screening and Selection -- 3 Results -- 3.1 Analysis of Current Open-Source LLMs in Educational and Industry Sectors -- 3.2 Suitability and Open-Source LLMs for Educational Integration -- 4 Discussion and Implications -- 5 Conclusion and Future Directions -- References -- Jill Watson: Scaling and Deploying an AI Conversational Agent in Online Classrooms -- 1 Introduction -- 2 Related Work -- 3 Overview of Jill Watson's Architecture -- 3.1 Knowledge Base -- 3.2 Agent Memory -- 3.3 Question Answering -- 4 Results and Discussion -- 5 Conclusion -- References -- Improving LLM Classification of Logical Errors by Integrating Error Relationship into Prompts -- 1 Introduction -- 2 Related Works -- 2.1 Automated Program Repair -- 2.2 Teaching Programming to Beginners: An Instructor's Perspective on Educational Environment -- 3 Definition of Logical Error Types -- 4 Classification and Augmentation Using LLMs -- 4.1 Logical Error Classification Prompt -- 4.2 Logical Error Augmentation Prompt -- 4.3 Experimental Results -- 5 Conclusion -- References -- Enhancement of Knowledge Concept Maps Using Deductive Reasoning with Educational Data -- 1 Introduction -- 2 Related Work -- 2.1 Bregman Iterative Approach -- 2.2 Deduction and Syllogism. ; 2.3 Expansion of Causality -- 2.4 Learning Path and Knowledge Concept Maps -- 3 Proposed Method -- 3.1 Data -- 3.2 Research Procedure -- 4 Results and Analysis -- 5 Conclusion and Further Study -- References -- Individualised Mathematical Task Recommendations Through Intended Learning Outcomes and Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Design and Implementation -- 3.1 Reinforcement Learning Environment -- 3.2 Assessing Student Progress -- 4 Experiments -- 4.1 Recommendation Pool -- 4.2 Experimental Setup -- 4.3 Results -- 5 Discussion -- 6 Conclusion -- References -- Developing Conversational Intelligent Tutoring for Speaking Skills in Second Language Learning -- 1 Introduction -- 2 Related Work -- 2.1 Conversational Intelligent Tutoring Systems -- 2.2 LLM-Integrated Second Language Learning -- 2.3 Dialogue Datasets for Deep Learning-Based Second Language Tutoring -- 3 KORLINGS Dataset Construction -- 3.1 Tutoring Dialogues with Annotations -- 3.2 Dataset Statistics -- 3.3 Dataset for Generation -- 4 Development -- 4.1 System Design -- 4.2 Implementation -- 5 Experiment -- 5.1 Evaluation Criteria -- 5.2 Evaluation Results -- 5.3 Human Evaluation -- 6 Conclusion -- Appendix -- References -- SAMI: An AI Actor for Fostering Social Interactions in Online Classrooms -- 1 Introduction -- 2 Related Work -- 3 SAMI System Design -- 3.1 Natural Language Processing Module -- 3.2 Knowledge Base Module -- 3.3 Matchmaking Module -- 3.4 Response Generation Module -- 3.5 SAMI on Slack -- 4 Results and Discussion -- 4.1 Early Results -- 4.2 Intermediate Results -- 4.3 Results from SAMI on Slack -- 4.4 Recent Deployments -- 5 Conclusion -- References -- Exploring the Methodological Contexts and Constraints of Research in Artificial Intelligence in Education -- 1 Introduction -- 2 Methodology -- 3 Results. ; 3.1 Educational Contexts and AI (RQ1) -- 3.2 Methodological and Study Designs in AIED (RQ2) -- 3.3 AI Algorithms and AI Technologies Used in Education (RQ3) -- 4 Discussion -- 4.1 In Which Educational Contexts is AI Used? (RQ1) -- 4.2 What are the Methodological and Study Designs Employed in AIED Research? (RQ2) -- 4.3 What AI Algorithms and Technologies are Used in Education? (RQ3) -- 4.4 Theoretical and Practical Implications -- 5 Conclusion -- References -- A Constructivist Framing of Wheel Spinning: Identifying Unproductive Behaviors with Sequence Analysis -- 1 Introduction -- 2 VERA: A Platform for Inquiry-Based Learning -- 3 Background -- 3.1 Inquiry-Based Learning -- 3.2 Wheel Spinning -- 3.3 End-of-Session Activities -- 3.4 Productive Persistence and Quitting Behavior -- 3.5 Sequential Pattern Mining -- 4 Methodology -- 4.1 Cohorts -- 4.2 Data Recording and Behavior Classification -- 5 Results -- 5.1 Actions Before Coach Request -- 5.2 Sequences of Note -- 5.3 Total User History and Patterns Across Sessions -- 5.4 Markov Diagram -- 6 Discussion -- 6.1 When to Offer Proactive Tutoring -- 7 Conclusion -- References -- Evaluating the Ability of Large Language Models to Generate Motivational Feedback -- 1 Introduction -- 2 Related Work -- 3 Overall Picture -- 4 Evaluation Framework -- 4.1 Component View -- 4.2 Quantitative Evaluation -- 4.3 Qualitative Evaluation -- 5 Experimentation Activities and Results -- 5.1 Settings and Execution -- 5.2 Results -- 6 Conclusions and Future Works -- References -- Towards Cognitive Coaching in Aircraft Piloting Tasks: Building an ACT-R Synthetic Pilot Integrating an Ontological Reference Model to Assist the Pilot and Manage Deviations -- 1 Introduction -- 2 Related Work -- 2.1 The Reference Model -- 2.2 Automatic Execution of Reference Model -- 2.3 Automatic Flight Deviation Detection. ; 2.4 Execution of a Piloting Task by an ACT-R Agent -- 3 The Cognitive Synthetic Pilot -- 4 Methodology and Results -- 4.1 Methodology -- 4.2 Results -- 5 Conclusion and Future Works -- References -- Impact of Conversational Agent Language and Text Structure on Student Language -- 1 Introduction -- 1.1 Theoretical Framework and Coh-Metrix -- 1.2 Summarization and Text Structures -- 2 Method -- 2.1 Participants and Procedures -- 2.2 Manipulation for Agents Language -- 3 Analyses, Findings, and Discussions -- 3.1 The Impact of Agent Language Within Comparison Texts -- 3.2 The Impact of Agent Language Within Causation Texts -- 4 Conclusions, Future Directions, and Implications -- References -- Analyzing the Role of Generative AI in Fostering Self-directed Learning Through Structured Prompt Engineering -- 1 Introduction -- 2 Backgrounds and Literature Review -- 2.1 Generative AI in Education -- 2.2 Prompt Engineering -- 2.3 Identification of Gaps in Existing Literature -- 3 Study Design -- 3.1 Selection of Concept of Data Analysis for the Task -- 3.2 Dataset for the Task and Problem Statement for the Task -- 3.3 Instrument Designed: Pre-post Test and Sus Survey -- 3.4 Design of Structured Prompt Training -- 3.5 Participant -- 3.6 Study Procedure -- 4 Results and Discussion -- 4.1 Data Analysis -- 4.2 Discussion -- 5 Conclusion and Future Work -- References -- Detecting Function Inputs and Outputs for Learning-Problem Generation in Intelligent Tutoring Systems -- 1 Introduction -- 2 Related Works -- 3 Method of Identifying Data Items in Function Description -- 4 Results -- 4.1 Test Sample -- 4.2 Evaluation -- 5 Discussion -- 6 Conclusion -- References -- Automated Analysis of Algorithm Descriptions Quality, Through Large Language Models*-1.0pc -- 1 Introduction -- 1.1 Research Questions -- 2 Related Work -- 3 Methods -- 4 Results. ; 5 Conclusions and Future Work.
• Titolo autorizzato: Generative Intelligence and Intelligent Tutoring Systems
• ISBN: 9783031630286; 9783031630279
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910865282703321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Computer Science Series