UID:
almahu_9949772831802882
Umfang:
XXVII, 427 p. 123 illus., 80 illus. in color.
,
online resource.
Ausgabe:
1st ed. 2024.
ISBN:
9783031630286
Serie:
Lecture Notes in Computer Science, 14798
Inhalt:
This book constitutes the refereed proceedings of the 20th International Conference on Generative Intelligence and Intelligent Tutoring Systems, ITS 2024, held in Thessaloniki, Greece, during June 10-13, 2024. The 35 full papers and 28 short papers included in this book were carefully reviewed and selected from 88 submissions. This book also contains 2 invited talks. They were organized in topical sections as follows: Generative Intelligence and Tutoring Systems; Generative Intelligence and Healthcare Informatics; Human Interaction, Games and Virtual Reality; Neural Networks and Data Mining; Generative Intelligence and Metaverse; Security, Privacy and Ethics in Generative Intelligence; and Generative Intelligence for Applied Natural Language Processing.
Anmerkung:
-- Generative Intelligence and Tutoring Systems. -- Using Large Language Models to Support Teaching and Learning of Word Problem Solving in Tutoring Systems. -- A Generative Approach for Proactive Assistance Forecasting in Intelligent Tutoring Environments. -- Combined maps as a tool of concentration and visualization of knowledge in the logic of operation of the Intelligent Tutoring Systems. -- Fast Weakness Identification for Adaptive Feedback. -- QuizMaster: An Adaptive Formative Assessment System. -- Preliminary Systematic Review of Open-Source Large Language Models in Education. -- Jill Watson: Scaling and Deploying an AI Conversational Agent in Online Classrooms. -- Improving LLM Classification of Logical Errors by Integrating Error Relationship into Prompts. -- Enhancement of Knowledge Concept Maps Using Deductive Reasoning with Educational Data. -- Individualised Mathematical Task Recommendations through Intended Learning Outcomes and Reinforcement Learning. -- Developing Conversational Intelligent Tutoring for Speaking Skills in Second Language Learning. -- SAMI: An AI Actor for Fostering Social Interactions in Online Classrooms. -- Exploring the Methodological Contexts and Constraints of Research in Artificial Intelligence in Education. -- A Constructivist Framing of Wheel Spinning: Identifying Unproductive Behaviors with Sequence Analysis. -- Evaluating the ability of Large Language Models to generate motivational feedback. -- 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. -- Impact of Conversational Agent Language and Text Structure on Student Language. -- Analyzing the role of Generative AI in fostering self-directed learning through Structured prompt engineering. -- Detecting Function Inputs and Outputs for Learning-Problem Generation in Intelligent Tutoring Systems. -- Automated Analysis of Algorithm Descriptions Quality, through Large Language Models. -- An AI-Learner Shared Control Model Design for Adaptive Practicing. -- Early Math Skill as a Predictor for Foundational Literacy. -- Explaining Problem Recommendations in an Intelligent Tutoring System. -- Distributed Feedback in a Tool that Supports Peer-directed Simulation-based Training. -- Keeping Humans in the Loop: LLM supported Oral Examinations. -- Generating Learning Sequences Using Contextual Bandit Algorithms. -- A Generative Artificial Intelligence empowered chatbot: System usability and student teachers' experience. -- Predicting Rough Error Causes in Novice Programmers using Cognitive Level. -- Social AI Agents Too Need to Explain Themselves. -- Students' Perceptions of Adopting Learning Analytics. -- AI4LA: an Intelligent Chatbot for Supporting Dyslexic Students, Based on Generative AI. -- EvaAI: A Multi-Agent Framework Leveraging Large Language Models for Enhanced Automated Grading. -- Optimising a Peer based Learning Environment. -- Difficulty Estimation and Simplification of French Text Using Large Language Models. -- LLM-based Course Comprehension Evaluator. -- Exploring Item Difficulty Prediction: Data Driven Approach for Item Difficulty Estimation.
In:
Springer Nature eBook
Weitere Ausg.:
Printed edition: ISBN 9783031630279
Weitere Ausg.:
Printed edition: ISBN 9783031630293
Sprache:
Englisch
DOI:
10.1007/978-3-031-63028-6
URL:
https://doi.org/10.1007/978-3-031-63028-6
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