Instructional Scaffolding

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11 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Evaluating Inquiry Learning in LLM-Based Agents: Hypothesizing, Reasoning, and Exploration
Oct 15, 2026Marta Knežević, Seyed Parsa Neshaei, Jibril Frej +2Instructional Scaffolding Scientific Reasoning in Language Models
EPFL
Adaptive Scaffolding Needs Contingency: An AI Tutor That Escalates and Fades on What the Learner Does
Sep 19, 2026Xinmeng Hou, Yuxuan Weng, Chin Hsien Yeh +5Instructional Scaffolding Intelligent Tutoring Systems
National Institute of Education, Nanyang Technological University Singapore, Singapore · School of Computer Science and Engineering, Nanyang Technological University Singapore, Singapore · School of Art, Design and Media, Nanyang Technological University Singapore, Singapore +2
LLM Pedagogical Behavior in AI Tutoring Interactions
Aug 24, 2026Suhyeon Lee, Juneha Baek, Jaehyeong Park +1Instructional Scaffolding AI in Education
KAIST
Beyond Direct Answering: Aligning Educational LLMs as Socratic Guides via Heuristic Reinforcement Learning
Jul 25, 2026Xiaokun Wang, Siyu Song, Wentao Liu +1LLM Alignment Intelligent Tutoring Systems
East China Normal University, Shanghai, China · Shanghai Chuangjie Situo Information Technology Co., Ltd., Shanghai, China · Shanghai Normal University, Shanghai, China +1
Rethinking Scaffolding in LLM Tutors: The Interactional Mismatch Between Benchmarks and Real-World Deployments
Jun 14, 2026Alexandra Neagu, Jeffrey T. H. Wong, Marcus Messer +2Instructional Scaffolding Intelligent Tutoring Systems
Department of Mechanical Engineering, Imperial College London, UK · Department of Electrical Engineering, Imperial College London, UK · Department of Earth Science and Engineering, Imperial College London, UK
The Role of Instructional Guidance in Generative AI-Assisted Learning: Empirical Evidence from Construction Engineering Education
Jun 3, 2026Xiaoyu Hou, Bo Xiao, Hexu Liu +1Instructional Scaffolding Educational Technology
Dept. of Civil, Environmental, and Geospatial Engineering, Michigan Technological Univ., Houghton, MI. 49931, U.S.A. · Dept. of Civil, Environmental, and Geospatial Engineering, Michigan Technological Univ., Houghton, MI. 49931 · Dept. of Civil and Construction Engineering, Western Michigan Univ., Kalamazoo, MI. 49008 +1
A Theory-Guided LLM Pedagogical Agent for STEM+C Scaffolding Without Over-Reliance
May 28, 2026Clayton Cohn, Surya Rayala, Siyuan Guo +13Instructional Scaffolding Multi-Agent LLM Systems
College of Connected Computing, Vanderbilt University, Nashville, Tennessee 37240 · The School for Science and Math at Vanderbilt, Vanderbilt University, Nashville, Tennessee 37240
CourseBlueprint: A Structured Pipeline for Adaptive Pedagogical Video Generation Grounded in Course Corpora
May 22, 2026Md Zabirul Islam, Md Motaleb Hossen Manik, Ge WangInstructional Scaffolding Generative AI in Education
A Simulation-Based Method for Testing Collaborative Learning Scaffolds Using LLM-Based Multi-Agent Systems
Apr 13, 2026Han Wua, Lishan Zhang, Chunming LuInstructional Scaffolding Education
School of Education, Beijing Institute of Technology, Beijing, People’s Republic of China · State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, People’s Republic of China
Structuring versus Problematizing: How LLM-based Agents Scaffold Learning in Diagnostic Reasoning
Apr 10, 2026Fatma Betül Güreş, Tanya Nazaretsky, Seyed Parsa Neshaei +1Instructional Scaffolding AI in Education
ETH Zürich, EPFL Lausanne, Switzerland · EPFL Lausanne, Switzerland
Representation Learning to Study Temporal Dynamics in Tutorial Scaffolding
Mar 25, 2026Conrad Borchers, Jiayi Zhang, Ashish GurungInstructional Scaffolding Semantic Alignment
Carnegie Mellon University · Worcester Polytechnic Institute

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Evaluating Inquiry Learning in LLM-Based Agents: Hypothesizing, Reasoning, and Exploration

Marta Knežević, Seyed Parsa Neshaei, Jibril Frej, Vinitra Swamy+1

EPFL

Instructional Scaffolding Scientific Reasoning in Language Models LLM Agent Evaluation Causal Discovery

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Scientific inquiry involves iterative cycles of exploration, hypothesis generation, and reasoning over observations to uncover relationships in complex systems. Large language models (LLMs) have demonstrated strong capabilities in several components of this process, including inductive reasoning, hypothesis generation, and data analysis. However, prior work typically evaluates these abilities in isolation, leaving open how models integrate them across a full inquiry cycle. In this work, we introduce a framework for evaluating LLMs' inquiry capabilities in a unified, interactive setting. We extend the Countdown task to a mathematical environment where an agent probes the system by manipulating variables, collecting observations, and inferring relationships between them. This setup enables analysis of exploration strategies, causal discovery, and hypothesis refinement during inquiry. Across seven LLMs and multiple prompting strategies, our experiments reveal limited inherent inquiry capabilities and low success rates in discovering the underlying expressions. However, structured guidance inspired by educational scaffolding significantly improves performance. Our findings highlight current limitations of LLMs in strategic exploration and iterative hypothesis refinement, and show how simple guidance strategies developed for human learners in inquiry learning can support more effective inquiry behavior.