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Found 8 papers, 4 papers with code
Improving Student-AI Interaction Through Pedagogical Prompting: An Example in Computer Science Education
Our contributions include (1) a theoretical framework of pedagogical prompting; (2) empirical insights into current instructor attitudes toward pedagogical prompting; and (3) a learning intervention design with an interactive learning tool and scenario-based instruction leading to promising results on teaching LLM-based help-seeking.
Small but Significant: On the Promise of Small Language Models for Accessible AIED
GPT has become nearly synonymous with large language models (LLMs), an increasingly popular term in AIED proceedings.
KCluster: An LLM-based Clustering Approach to Knowledge Component Discovery
Educators evaluate student knowledge using knowledge component (KC) models that map assessment questions to KCs.
An Automatic Question Usability Evaluation Toolkit
Evaluating multiple-choice questions (MCQs) involves either labor intensive human assessments or automated methods that prioritize readability, often overlooking deeper question design flaws.
Automated Generation and Tagging of Knowledge Components from Multiple-Choice Questions
This research advances the automation of KC generation and classification for assessment items, alleviating the need for student data or predefined KC labels.
Exploring How Multiple Levels of GPT-Generated Programming Hints Support or Disappoint Novices
Recent studies have integrated large language models (LLMs) into diverse educational contexts, including providing adaptive programming hints, a type of feedback focuses on helping students move forward during problem-solving.
Assessing the Quality of Multiple-Choice Questions Using GPT-4 and Rule-Based Methods
We demonstrated the effectiveness of the two methods in identifying common item-writing flaws present in the student-generated questions across different subject areas.
Learnersourcing in the Age of AI: Student, Educator and Machine Partnerships for Content Creation
Engaging students in creating novel content, also referred to as learnersourcing, is increasingly recognised as an effective approach to promoting higher-order learning, deeply engaging students with course material and developing large repositories of content suitable for personalized learning.
