Artificial Intelligence in Education

.- A first step to using machine learning methods to enhance interaction analysis in embodied learning environments.

.- "I am confused! How to differentiate between. . . ?" Adaptive Followup Questions Facilitate Tutor Learning with Effective Time-on-task.

.- An Automatic Question Usability Evaluation Toolkit.

.- Evaluating Behaviors of General Purpose Language Models in a Pedagogical Context.

.- Federated Learning Analytics: Investigating the Privacy-Performance Trade-off in Machine Learning for Educational Analytics.

.- Towards the automated generation of readily applicable personalised feedback in education.

.- Identifying and Mitigating Algorithmic Bias in Student Emotional Analysis.

.- Navigating Self-Regulated Learning Dimensions: Exploring Interactions Across Modalities.

.- Real-World Deployment and Evaluation of Kwame for Science, An AI Teaching Assistant for Science Education in West Africa.

.- ChatGPT for Education Research: Exploring the Potential of Large Language Models for Qualitative Codebook Development.

.- Aligning Tutor Discourse Supporting Rigorous Thinking with Tutee Content Mastery for Predicting Math Achievement.

.- Automated Educational Question Generation at Different Bloom's Skill Levels using Large Language Models: Strategies and Evaluation.

.- VizChat: Enhancing Learning Analytics Dashboards with Contextualised Explanations using Multimodal Generative AI Chatbots.

.- Affect Behavior Prediction: Using Transformers and Timing Information to Make Early Predictions of Student Exercise Outcome.

.- To read or not to read: Predicting student engagement in interactive reading.

.- How Well Can You Articulate that Idea? Insights from Automated Formative Assessment.

.- Educational Content Personalization for Neurodiversity: A Survey of Technologies Supporting Linguistic Development in Individuals with Autism Spectrum Disorder (ASD).

.- Beyond the Obvious Multi-Choice Options: Introducing a Toolkit for Distractor Generation Enhanced with NLI Filtering.

.- Exploring the Potential of Automated and Personalized Feedback to Support Science Teacher Learning.

.- Coding With AI: How Are Tools Like ChatGPT Being Used By Students In Foundational Programming Courses.

.- Identifying Gaps In Students' Explanations of Code Using LLMs.

.- A Multi-Task Automated Assessment System for Essay Scoring.

.- Content Knowledge Identification with Multi-Agent Large Language Models (LLMs).

.- Using knowledge graphs to improve question difficulty estimation from text.

.- The Role of First Language in Automated Essay Grading for Second Language Writing.

.- An Educational Psychology Inspired Approach to Student Interest Detection in Valence-Arousal Space.

.- Agency in AI and Education Policy: European Resolution Three on Harnessing the Potential for AI in and Through Education.

.- Nudging Adolescents Towards Recommended Maths Exercises With Gameful Rewards.

.- A Learning Approach for Increasing AI Literacy Via XAI in Informal Settings.

.- Aspect-based Semantic Textual Similarity for Educational Test Items.

.- Adapting Emotional Support in Teams: Quality of Contribution, Emotional Stability and Conscientiousness.

.- Predicting successful programming submissions based on critical logic blocks.

.- Student At-Risk Identification and Classification through Multitask Learning: A Case Study on the Moroccan Education System.

.- Investigating the Predictive Potential of Large Language Models in Student Dropout Prediction.

.- Towards Automated Multiple Choice Question Generation and Evaluation: Aligning with Bloom's Taxonomy.

.- Enhancing student dialogue productivity with learning analytics and fuzzy rules.

.- HiTA: a RAG-based Educational Platform that Centers Educators in the Instructional Loop.

.- Towards Convergence: Characterizing Students' Design Moves in Computational Modeling through Log Data with Video and Cluster Analysis.

.- Mars, Minecraft, and AI: A Deep Learning Approach to Improve Learning by Building.

.- Characterising Learning in Informal Settings using Deep Learning with Network Data.

.- Exploring Teachers' Perception of Artificial Intelligence: The Socio-emotional Deficiency as Opportunities and Challenges in Human-AI Complementarity in K-12 Education.

.- Who Pilots the Copilots? Mapping a generative AI's Actor-Network to assess its educational impacts.