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ORCIDHEXED/L3MNGET@EDM 2024: Atlanta, GA, USA
- Juan D. Pinto, Eamon Worden, Anthony Botelho, Lea Cohausz, Clayton Cohn, Mingyu Feng, Neil T. Heffernan, Arto Hellas, Lan Jiang, David Joyner, Tanja Käser, Juho Kim, Andrew Lan, Chenglu Li, Joshua Littenberg-Tobias, Qianhui Liu, Christopher MacLellan, Steven Moore, Maciej Pankiewicz, Luc Paquette, Zach A. Pardos, Anna N. Rafferty, Adish Singla, Shashank Sonkar, Vinitra Swamy, Rose E. Wang, Candace Walkington:
Joint Proceedings of the Human-Centric eXplainable AI in Education and the Leveraging Large Language Models for Next Generation Educational Technologies Workshops (HEXED-L3MNGET 2024) co-located with 17th International Conference on Educational Data Mining (EDM 2024), Atlanta, Georgia, USA, July 14, 2024. CEUR Workshop Proceedings 3840, CEUR-WS.org 2024
Human-Centric eXplainable AI in Education (HEXED 2024)
- Juan Pinto, Luc Paquette, Vinitra Swamy, Tanja Käser, Qianhui Liu, Lea Cohausz:
Preface. - Mustafa Cavus, Jakub Kuzilek:
The Actionable Explanations for Student Success Prediction Models: A Benchmark Study on the Quality of Counterfactual Methods. - Yuang Wei, Yizhou Zhou, Yuan-Hao Jiang, Bo Jiang:
Enhancing Explainability of Knowledge Learning Paths: Causal Knowledge Networks. - Shalini Sushri, Rahul K. Dass, Rhea Basappa, Hong Lu, Ashok K. Goel:
Combining Cognitive and Generative AI for Self-Explanation in Interactive AI Agents. - Juan Pinto, Luc Paquette:
Towards a Unified Framework for Evaluating Explanations.
Leveraging Large Language Models for Next Generation Educational Technologies (L3MNGET 2024)
- Mohammad Hassany, Peter Brusilovsky, Jiaze Ke, Kamil Akhuseyinoglu, Arun Balajiee Lekshmi Narayanan:
Engaging an LLM to Explain Worked Examples for Java Programming: Prompt Engineering and a Feasibility Study. - Wesley Morris, Joon Suh Choi, Langdon Holmes, Vaibhav Gupta, Scott A. Crossley:
Automatic Question Generation and Constructed Response Scoring in Intelligent Texts. - Diego Zapata-Rivera, Carol Forsyth, Edith Aurora Graf, Yang Jiang:
Designing and Evaluating Evidence-Centered Design based Conversations for Assessment with LLMs. - Shradha Sehgal, Bhavya, Krishna Phani Datta, Aditi Mallavarapu, ChengXiang Zhai:
Exploring AI-powered Multimodal Analogies for Science Education. - Changyoon Lee, Junho Myung, Jieun Han, Jiho Jin, Alice Oh:
Learning from Teaching Assistants to Formulate Subgoals for Programming Tasks: Exploring the Potential for AI Teaching Assistants. - Jungyoub Cha, Jieun Han, Haneul Yoo, Alice Oh:
CHOP: Integrating ChatGPT into EFL Oral Presentation Practice. - Maryam Alomair, Shimei Pan, Lujie Karen Chen:
Large Language Models for Intelligent Coaching in Data Science Problem Solving: A Preliminary Investigation. - Qiming Sun, Shih-Yi Chien, I-Han Hsiao:
Learning Waste Management from Interactive Quizzes and Adaptive GPT-guided Feedback. - Jiarui Rao, Jionghao Lin:
RAMO: Retrieval-Augmented Generation for Enhancing MOOCs Recommendations. - Zifeng Liu, Wanli Xing, Chenglu Li:
Explainable Analysis of AI-Generated Responses in Online Learning Discussions. - Zachary Levonian, Owen Henkel:
Safe Generative Chats in a WhatsApp Intelligent Tutoring System. - Md. Akib Zabed Khan, Agoritsa Polyzou, Neila Bennamane:
How Can We Use LLMs for EDM Tasks? The Case of Course Recommendation. - Liang Zhang, Jionghao Lin, Ziyi Kuang, Sheng Xu, Xiangen Hu:
SPL: A Socratic Playground for Learning Powered by Large Language Model.
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