Intelligent tutoring systems (ITSs) that imitate human tutors and aim to provide immediate and customized instructions or feedback to learners have shown their effectiveness in education. With the emergence of generative artificial intelligence, large language models (LLMs) further entitle the systems to complex and coherent conversational interactions. These systems would be of great help in language education as it involves developing skills in communication, which, however, drew relatively less attention. Additionally, due to the complicated cognitive development at younger ages, more endeavors are needed for practical uses. Scaffolding refers to a teaching technique where teachers provide support and guidance to students for learning and developing new concepts or skills. It is an effective way to support diverse learning needs, goals, processes, and outcomes. In this work, we investigate how pedagogical instructions facilitate the scaffolding in ITSs, by conducting a case study on guiding children to describe images for language learning. We construct different types of scaffolding tutoring systems grounded in four fundamental learning theories: knowledge construction, inquiry-based learning, dialogic teaching, and zone of proximal development. For qualitative and quantitative analyses, we build and refine a seven-dimension rubric to evaluate the scaffolding process. In our experiment on GPT-4V, we observe that LLMs demonstrate strong potential to follow pedagogical instructions and achieve self-paced learning in different student groups. Moreover, we extend our evaluation framework from a manual to an automated approach, paving the way to benchmark various conversational tutoring systems.
SafeTutors: Benchmarking Pedagogical Safety in AI Tutoring Systems
Eindhoven University of Technology（埃因霍温理工大学）；Indian Institute of Technology Kharagpur（印度理工学院Khargpur分校）；Cisco Research（思科研究）；Taras Shevchenko National University of Kyiv（塔拉斯·谢甫琴科基辅国立大学）；National Technical University of Ukraine（乌克兰国家技术大学）；New York University（纽约大学）
MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems
ETH AI Center（苏黎世联邦理工学院人工智能中心）；ETH Zurich（苏黎世联邦理工学院）；TU Darmstadt（达姆施塔特工业大学）；Hessian Center for AI (hessian.AI)（黑森人工智能中心）；National Institute of Education, Nanyang Technological University（南洋理工大学国立教育学院）
Rewarding How Models Think Pedagogically: Integrating Pedagogical Reasoning and Thinking Rewards for LLMs in Education
Chosun University（昌原大学）；Korea University（韩国大学）；Seoul National University（首尔国立大学）；Korea Institute for Curriculum and Evaluation（韩国课程与评价研究院）；Upstage；Indiana University Bloomington（印第安纳大学布卢明顿分校）；Nanyang Technological University（南洋理工大学）
LLMs Are Already Good Tutors: Training-Free Prompt Optimization for Pedagogical Math Tutoring
Korea University Sejong Campus（韩国大学世宗校区）；Gyeonggi Institute of Education（京畿教育学院）；Indiana University Bloomington（印第安纳大学布卢明顿分校）；Opentutorials；Chosun University（全州大学）；Korea University Korean Studies Center（韩国大学韩学研究中心）
MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors
Department of Computer Science, ETH Zurich（苏黎世联邦理工学院计算机科学系）；ETH AI Center（ETH人工智能中心）；Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science, Technical University of Darmstadt and National Research Center for Applied Cybersecurity ATHENE, Germany（达姆施塔特技术大学计算机科学系）；Professorship for Learning Sciences and Higher Education, ETH Zurich（苏黎世联邦理工学院学习科学与高等教育教授职位）
SHAPE: Unifying Safety, Helpfulness and Pedagogy for Educational LLMs
Center for Data Science, New York University Shanghai（纽约大学上海数据科学中心）；Courant Institute of Mathematical Sciences, New York University（纽约大学Courant数学科学研究所）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）
Developing a Tutoring Dialog Dataset to Optimize LLMs for Educational Use
Graduate School of Information Science and Electrical Engineering（信息科学与电气工程研究生院）；Kyushu University（九州大学）；Faculty of Information Science and Electrical Engineering（信息科学与电气工程学部）
How Real Is AI Tutoring? Comparing Simulated and Human Dialogues in One-on-One Instruction
Shanghai Institute of Artificial Intelligence for Education, East China Normal University（华东师范大学上海人工智能教育研究院）；Lab of Artificial Intelligence for Education, East China Normal University（华东师范大学人工智能教育实验室）
ETH AI Center（苏黎世联邦理工学院人工智能中心）；The Chinese University of Hong Kong（香港中文大学）；ETH Zürich（苏黎世联邦理工学院）；TU Darmstadt（达姆施塔特工业大学）
Designing dialog tutors has been challenging as it involves modeling the diverse and complex pedagogical strategies employed by human tutors. Although there have been significant recent advances in neural conversational systems using large language models (LLMs) and growth in available dialog corpora, dialog tutoring has largely remained unaffected by these advances. In this paper, we rigorously analyze various generative language models on two dialog tutoring datasets for language learning using automatic and human evaluations to understand the new opportunities brought by these advances as w…
Machine learning plays an increasing role in intelligent tutoring systems as both the amount of data available and specialization among students grow. Nowadays, these systems are frequently deployed on mobile applications. Users on such mobile education platforms are dynamic, frequently being added, accessing the application with varying levels of focus, and changing while using the service. The education material itself, on the other hand, is often static and is an exhaustible resource whose use in tasks such as problem recommendation must be optimized. The ability to update user models with …
