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.

Oct 15, 2026

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

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.

Marta Knežević, Seyed Parsa Neshaei, Jibril Frej +2

EPFL

Instructional ScaffoldingScientific Reasoning in Language ModelsLLM Agent EvaluationCausal Discovery
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Sep 19, 2026

Adaptive Scaffolding Needs Contingency: An AI Tutor That Escalates and Fades on What the Learner Does

Coding assistants raise task performance, but learners plan and monitor less. Giving less away, the usual fix, conflates two things: how much work a system carries (cognitive load) and what the learner must decide before help arrives (metacognitive demand). Our principle, preserved metacognitive demand, holds the second constant and lets the first vary. CoMeT implements it: support rises when a learner fails at a decision point and fades on take-up. Within subjects, 131 adult learners used CoMeT, an unrestricted assistant and a question-only tutor on three Python tasks. CoMeT matched the question-only tutor's demand, delivered artifacts twice as often as the assistant, and frustrated learners less than the question-only tutor, with delegation and load unchanged. Learners often did not answer. Fading held when their turn addressed the decision under support, and CoMeT surrendered the full answer in one session in sixteen, against one in six for the question-only tutor.

Xinmeng Hou, Yuxuan Weng, Chin Hsien Yeh +5

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

Instructional ScaffoldingIntelligent Tutoring SystemsHuman-AI CollaborationProgramming EducationCognitive Offloading
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Aug 24, 2026

LLM Pedagogical Behavior in AI Tutoring Interactions

Students increasingly use LLMs as tutors for coursework and problem solving. Little is known about the level of assistance LLMs provide when students use them as tutors in authentic learning interactions. This matters because tutoring responses can differ substantially in how directly they help students complete a task. We operationalize this dimension as scaffolding level and develop a five-level scale, validated against human annotations, that characterizes responses according to the degree of direct assistance they provide. We apply the scale to 14,637 LLM responses from 203 students in a university AI course. Responses are overwhelmingly concentrated at high levels of assistance, with more than 95% classified as either Explaining or Solving. Scaffolding level is systematically associated with students' subsequent conversational behavior, but provides little additional predictive information about performance on three subsequent exams beyond prior achievement and dialogue behavior. These findings provide an empirical baseline for LLM assistance in tutoring interactions and a measurement framework for evaluating how alternative tutoring designs change that assistance.

Suhyeon Lee, Juneha Baek, Jaehyeong Park +1

KAIST

Instructional ScaffoldingAI in EducationIntelligent Tutoring Systems
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Jul 25, 2026

Beyond Direct Answering: Aligning Educational LLMs as Socratic Guides via Heuristic Reinforcement Learning

Large language models (LLMs) deployed in educational settings often behave as direct answerers: they disclose target concepts in the opening turn instead of guiding students through progressive inquiry, as Socratic pedagogy prescribes. We present HeuristicEdu, a two-phase pipeline that aligns Qwen2.5-7B toward Socratic tutoring via supervised warm-up and Group Relative Policy Optimization (GRPO). Training uses SocraticEdu, 797 multi-turn Chinese children's science dialogues reconstructed from a live platform, with a heuristic reward over cognitive depth (R_cog), curiosity engagement (R_eng), and directness (R_dir), together with a K_query correction for student-introduced terms. We introduce Scaffolding Effectiveness (SE) and Conversation Depth (CD) to evaluate outcomes beyond surface fluency. On 30 held-out questions, the best GRPO variant improves SE from 30.0% to 63.3% and lowers keyword leakage from 30.0% to 13.3%. Notably, this best variant omits the directness penalty during optimization, suggesting that explicit anti-leakage terms can conflict with gradient-based behavioral alignment. An unaligned Qwen-72B baseline reaches 0% SE and 96.7% leakage, showing that scale alone does not induce Socratic behavior.

Xiaokun Wang, Siyu Song, Wentao Liu +1

East China Normal University, Shanghai, China · Shanghai Chuangjie Situo Information Technology Co., Ltd., Shanghai, China · Shanghai Normal University, Shanghai, China +1

LLM AlignmentIntelligent Tutoring SystemsAI in EducationSocratic TutoringEducational Dialogue SystemsInstructional Scaffolding
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Jun 14, 2026

Rethinking Scaffolding in LLM Tutors: The Interactional Mismatch Between Benchmarks and Real-World Deployments

A central pedagogical value evaluated in AI tutor benchmarks is scaffolding: guiding students through graduated steps toward a solution. Alignment and evaluation methods for embedding scaffolding behaviour into chatbots, however, rest on an implicit assumption: that students will take up the scaffolding and engage in the conversation. To examine whether this assumption holds, we introduce an evaluation pipeline around two metrics - Chatbot Scaffolding and Student Uptake - and apply them across nine datasets of 9,490 chats, spanning AI tutor benchmarks and real-world deployments of educational chatbots. Our analysis reveals that while benchmarks assume a high-scaffolding, high-student-uptake environment, students in real-world settings exhibit lower levels of uptake overall - frequently bypassing the chatbot's pedagogical framing to drive the interaction toward their own learning goals at little interpersonal cost. We argue that bypassing scaffolding is not necessarily detrimental; rather, it frequently highlights a mismatch between a chatbot's pedagogical framing and the student's learning goals. To meaningfully evaluate the effectiveness of a chatbot's assistance, future benchmarks must move beyond the assumption that students will simply take up the scaffolding, and instead evaluate how these chatbots navigate diverse learning contexts and student-driven interaction patterns.

Alexandra Neagu, Jeffrey T. H. Wong, Marcus Messer +2

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

Instructional ScaffoldingIntelligent Tutoring SystemsAI in EducationHuman-AI InteractionGenerative AI Evaluation
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Jun 3, 2026

The Role of Instructional Guidance in Generative AI-Assisted Learning: Empirical Evidence from Construction Engineering Education

Generative artificial intelligence (AI) is increasingly used to support self-directed learning, yet student interaction with such systems often remains unstructured, limiting engagement in deeper cognitive processes. This study examines how instructional guidance shapes student and AI interaction in construction education. A five-step prompting framework grounded in Generative Learning Theory (GLT) is introduced to guide learner interaction during review activities. A controlled experiment compares three learning conditions: slide-based learning, unprompted AI-supported learning, and prompted AI-supported learning. Learning performance is assessed using multiple-choice and open-ended tasks, and user experience is measured using the User Experience Questionnaire (UEQ). Performance differences are concentrated on tasks requiring explanation and reasoning. The prompted condition achieves higher open-ended scores, with an improvement of approximately 2 or 3 points on a scale of 18 (p < 0.01), while no significant differences are observed in multiple-choice performance. The unprompted condition remains comparable to slide-based learning. These findings indicate that the effectiveness of AI-supported learning depends on how interaction is structured. The proposed framework provides a basis for integrating learning science principles into generative AI systems for construction education.

Xiaoyu Hou, Bo Xiao, Hexu Liu +1

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

Instructional ScaffoldingEducational TechnologyGenerative AI in EducationHuman-AI Interaction
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May 28, 2026

A Theory-Guided LLM Pedagogical Agent for STEM+C Scaffolding Without Over-Reliance

LLM pedagogical agents are proliferating, yet recent findings have raised questions about their adherence to established theories of learning and, by extension, their educational value. Concerns regarding cognitive offloading, over-reliance, and "gaming" behaviors persist and remain largely unaddressed. In response, we developed Copa, an agentic, multi-agent, multimodal Collaborative Peer Agent for STEM+C learning. Copa is built on top of the Evidence-Decision-Feedback (EDF) framework, grounding its interactions in Social Cognitive Theory and Social Constructivism and promoting sense-making through adaptive, dialogic support rather than answer-seeking. In an authentic high school computational-modeling study (n=33 dyads), we demonstrate that Copa (1) supports students' confidence building and ability to verbalize conceptual understanding without causing dependence; and (2) provides adaptive feedback personalized to learners that is interpretable with respect to students' multimodal input data. These findings position theory-guided, multimodal LLM agents as a promising path toward classroom AI integration that amplifies students' reasoning rather than replacing it.

Clayton Cohn, Surya Rayala, Siyuan Guo +13

College of Connected Computing, Vanderbilt University, Nashville, Tennessee 37240 · The School for Science and Math at Vanderbilt, Vanderbilt University, Nashville, Tennessee 37240

Instructional ScaffoldingMulti-Agent LLM SystemsAI in EducationMultimodal AgentsCognitive Offloading
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May 22, 2026

CourseBlueprint: A Structured Pipeline for Adaptive Pedagogical Video Generation Grounded in Course Corpora

Generative text-to-video systems can produce visually fluent educational clips, but they rarely encode the pedagogical content knowledge (PCK) needed for effective instruction, including prerequisite-aware sequencing, learner-adaptive depth, and sustained cognitive engagement. We present CourseBlueprint, a course-grounded pipeline for adaptive pedagogical video generation. Given a topic and learner persona, the system generates a structured teaching blueprint in a single forward pass over an undergraduate biomedical-imaging corpus (BMED 2300; twenty-three lectures, 1,116 slides). Instead of ad-hoc prompt chaining, the pipeline uses typed intermediate representations with validation: a scaffolding module builds a stage-labeled prerequisite concept graph with deterministic cycle removal, an adaptive controller assigns per-concept style specifications, and an engagement generator produces narration following a fixed hook->retrieval->core->analogy->forward contract. A deterministic slide-image override further grounds the rendered video by reusing instructor slides whenever retrieval confidence is high. We also release a reusable benchmark corpus and an evaluation harness combining repeated LLM-judge scoring with regex-grounded objective metrics. In a five-topic ablation, removing the engagement contract reduces the engagement score from 5.00 to 1.20, the adaptive score from 4.80 to 3.40, Flesch readability from 38.0 to 19.8, and analogy and retrieval-prompt counts to near zero. The slide-image override converts a 0/9 corpus-grounding failure into 9/10 successful slide matches on the same topic. These results show that pedagogical video quality depends less on surface fluency than on explicit, typed instructional contracts that make scaffolding, adaptation, engagement, and grounding auditable.

Md Zabirul Islam, Md Motaleb Hossen Manik, Ge Wang

Instructional ScaffoldingGenerative AI in EducationVideo GenerationAdaptive Learning
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Apr 13, 2026

A Simulation-Based Method for Testing Collaborative Learning Scaffolds Using LLM-Based Multi-Agent Systems

Background: Traditional research on collaborative learning scaffolding is often time-consuming and resource-heavy, which hinders the rapid iteration and optimization of instructional strategies. LLM-based multi-agent systems have recently emerged as a powerful tool to simulate complex social interactions and provide a novel paradigm for educational research. Objectives: This study proposes an LLM-based multi-agent simulation approach to investigate collaborative learning processes and the effectiveness of instructional scaffolds prior to actual classroom deployment. The research specifically examines the feasibility of simulating group discussions and the alignment of these simulations with established learning science theories. Methods: The simulation system was implemented using the MetaGPT framework and GPT-4o, comprising one teacher agent and five distinct student roles (Leader, Supporter, Expounder, Rebutter, and Summarizer). Two scaffolding strategies, "Deep Think before Speak" and "Direct Speak", were compared across ten classical Chinese poetry appreciation tasks. Evaluation was conducted through discourse analysis of quality and behavior. Results and Conclusions: The introduction of the "Deep Think before Speak" scaffold significantly improved the agents' discourse diversity and interaction depth while notably reducing content repetitiveness. Behavioral analysis showed that the scaffold encouraged more complex interaction patterns, such as reflecting, rebutting, and explaining. These findings align with the ICAP framework, as the scaffold prompted agents to move from simple "Active" participation to "Constructive" and "Interactive" knowledge co-construction. This study demonstrates the feasibility and ecological validity of using LLM-based multi-agent systems to simulate authentic collaborative learning dynamics.

Han Wua, Lishan Zhang, Chunming Lu

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

Instructional ScaffoldingEducationAI in EducationLarge Language Model-Based Social Simulation
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Apr 10, 2026

Structuring versus Problematizing: How LLM-based Agents Scaffold Learning in Diagnostic Reasoning

Supporting students in developing diagnostic reasoning is a key challenge across educational domains. Novices often face cognitive biases such as premature closure and over-reliance on heuristics, and they struggle to transfer diagnostic strategies to new cases. Scenario-based learning (SBL) enhanced by Learning Analytics (LA) and large language models (LLM) offers a promising approach by combining realistic case experiences with personalized scaffolding. Yet, how different scaffolding approaches shape reasoning processes remains insufficiently explored. This study introduces PharmaSim Switch, an SBL environment for pharmacy technician training, extended with an LA- and LLM-powered pharmacist agent that implements pedagogical conversations rooted in two theory-driven scaffolding approaches: structuring and problematizing, as well as a student learning trajectory. In a between-groups experiment, 63 vocational students completed a learning scenario, a near-transfer scenario, and a far-transfer scenario under one of the two scaffolding conditions. Results indicate that both scaffolding approaches were effective in supporting the use of diagnostic strategies. Performance outcomes were primarily influenced by scenario complexity rather than students' prior knowledge or the scaffolding approach used. The structuring approach was associated with more accurate Active and Interactive participation, whereas problematizing elicited more Constructive engagement. These findings underscore the value of combining scaffolding approaches when designing LA- and LLM-based systems to effectively foster diagnostic reasoning.

Fatma Betül Güreş, Tanya Nazaretsky, Seyed Parsa Neshaei +1

ETH Zürich, EPFL Lausanne, Switzerland · EPFL Lausanne, Switzerland

Instructional ScaffoldingAI in Education
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Mar 25, 2026

Representation Learning to Study Temporal Dynamics in Tutorial Scaffolding

Adaptive scaffolding enhances learning, yet the field lacks robust methods for measuring it within authentic tutoring dialogue. This gap has become more pressing with the rise of remote human tutoring and large language model-based systems. We introduce an embedding-based approach that analyzes scaffolding dynamics by aligning the semantics of dialogue turns, problem statements, and correct solutions. Specifically, we operationalize alignment by computing cosine similarity between tutor and student contributions and task-relevant content. We apply this framework to 1,576 real-world mathematics tutoring dialogues from the Eedi Question Anchored Tutoring Dialogues dataset. The analysis reveals systematic differences in task alignment and distinct temporal patterns in how participants ground their contributions in problem and solution content. Further, mixed-effects models show that role-specific semantic alignment predicts tutorial progression beyond baseline features such as message order and length. Tutor contributions exhibited stronger grounding in problem content early in interactions. In contrast, student solution alignment was modestly positively associated with progression. These findings support scaffolding as a continuous, role-sensitive process grounded in task semantics. By capturing role-specific alignment over time, this approach provides a principled method for analyzing instructional dialogue and evaluating conversational tutoring systems.

Conrad Borchers, Jiayi Zhang, Ashish Gurung

Carnegie Mellon University · Worcester Polytechnic Institute

Instructional ScaffoldingSemantic AlignmentIntelligent Tutoring SystemsEducational Data Mining
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