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

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.

Table of Contents:
.- 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.