Student Misconceptions

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The Scaling Paradox in Human-AI Collaboration
Aug 1, 2026Anyan Qi, Mengxin WangHuman-Ai Collaboration Scaling Laws
Naveen Jindal School of Management, University of Texas at Dallas, USA
Evaluating and Mitigating the Misguidance Effect of Buggy Code in LLM-Generated Unit Tests
Jul 24, 2026Junda Zhao, Shurui Zhou, Eldan CohenTest Generation Large Language Models Fail
University of Toronto, Department of Mechanical and Industrial Engineering, Toronto, Canada · University of Toronto, Department of Electrical and Computer Engineering, Toronto, Canada
AI advice suppresses people's willingness to say "I don't know", even when the advice is wrong and accuracy is incentivized
Jul 15, 2026Chiara Marcoccia, Walter Quattrociocchi, Valerio CapraroAdvice Student Misconceptions
Ecole Normale Supérieure, Paris, France · University of Rome “La Sapienza”, Rome, Italy · University of Milan-Bicocca, Milan, Italy
Evaluating Large Language Models on Misconceptions in Multi-Turn Medical Conversations
Jul 14, 2026Monica Munnangi, Saiph SavageStudent Misconceptions Large Language Models Fail
Khoury College of Computer Sciences Northeastern University
The Correct Answer Trap: Pedagogically-Grounded Detection and Feedback for Hidden Misconceptions
Jun 22, 2026Moiz Imran, Sahan BulathwelaStudent Misconceptions Tutors
Department of Computer Science, University College London, The United Kingdom · Centre for Artificial Intelligence, University College London, The United Kingdom
Security and Privacy Prompts in the Wild: What Users Ask LLMs and How LLMs Respond
Jun 16, 2026Hobin Kim, Xiaoyuan Wu, Omer Akgul +2Large Language Model Safety Prompt Engineering
Carnegie Mellon University · RSAC Labs
Crypto x AI, AI x Crypto: A Survey
Jun 11, 2026Sarah Allen, Pranay Anchuri, James Austgen +22Blockchain Artificial Intelligence Research
1Initiative for CryptoCurrencies and Contracts (IC3) · 5Flashbots · 6Offchain Labs +11
Recalling Too Well: Sycophancy Evaluation and Mitigation in Memory-Augmented Models
Jun 9, 2026Shelly Bensal, Axel Magnuson, Aparna Balagopalan +1Sycophancy Large Language Model Memory
Writer, Inc.
Fix the Mind, Not the Move: Interpretable AI Assistance via Knowledge-Gap Localization
Jun 4, 2026Ayano Hiranaka, Ya-Chuan Hsu, Stefanos Nikolaidis +2Student Misconceptions Trajectory-Level Credit
Thomas Lord Department of Computer Science, University of Southern California, Los Angeles, USA.
DECK: A Consistency x Confidence Taxonomy of LLM Hallucinations
Jun 1, 2026Mohit Singh ChauhanLarge Language Model Hallucination Citation Hallucination
Error as a Lens: Probing LLM Reasoning through Synthetic Misconception Generation
May 27, 2026Xinming Yang, Jun LiStudent Misconceptions Synthetic Data
CUNY Graduate Center · CUNY Queens College
Investigating Concept Alignment Using Implausible Category Members
May 20, 2026Sunayana Rane, Brenden M. Lake, Thomas L. GriffithsArtificial Intelligence Alignment Artificial Intelligence Safety
Department of Computer Science · Princeton University · Princeton, NJ 08540 +1
Cognitive-Uncertainty Guided Knowledge Distillation for Accurate Classification of Student Misconceptions
May 14, 2026Qirui Liu, Hao Chen, Weijie Shi +2Student Misconceptions Knowledge Distillation
South China University of Technology · Tencent Financial Technology · The Hong Kong University of Science and Technology +2
Sycophancy is an Educational Safety Risk: Why LLM Tutors Need Sycophancy Benchmarks
May 14, 2026Enkelejda Kasneci, Gjergji KasneciEpistemic Asymmetry Tutors
Technical University of Munich, Munich, Germany · Munich Center for Machine Learning, Munich, Germany
Simulating Students or Sycophantic Problem Solving? On Misconception Faithfulness of LLM Simulators
May 12, 2026Heejin Do, Shashank Sonkar, Mrinmaya SachanUser Simulation Student Misconceptions
ETH Zürich, ETH AI Center · University of Central Florida · ETH Zürich
What Do You Think I Think? Accounting for Human Beliefs Using Second-Order Theory of Mind
May 12, 2026Patrick Callaghan, Reid Simmons, Henny AdmoniTheory-Of-Mind Reasoning Student Misconceptions
Robotics Institute, Carnegie Mellon University
When Helpfulness Becomes Sycophancy: Sycophancy is a Boundary Failure Between Social Alignment and Epistemic Integrity in Large Language Models
May 6, 2026Jiechen Li, Catherine A. Barry, Rishika Randev +3Sycophancy Epistemic Integrity
Duke University Durham, NC 27708 · ∗These authors contributed equally.
What Don't You Understand? Using Large Language Models to Identify and Characterize Student Misconceptions About Challenging Topics
Apr 30, 2026Michael J. Parker, Maria G. Zavala-CernaStudent Misconceptions Student
associate dean for online learning research, faculty director of HMX, Office of External Education, and assistant professor of medicine, Harvard Medical School and Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts · Decanato Medicina, Universidad Autónoma de Guadalajara
Can LLMs Model Incorrect Student Reasoning? A Case Study on Distractor Generation
Mar 16, 2026Yanick Zengaffinen, Andreas Opedal, Donya Rooein +3Student Misconceptions Reasoning Errors
1ETH Zürich · 2Bocconi University · University of Central Florida
User Misconceptions of LLM-Based Conversational Programming Assistants
Oct 29, 2025Gabrielle O'Brien, Antonio Pedro Santos Alves, Sebastian Baltes +2Large Language Models Fail Student Misconceptions
University of Michigan Ann Arbor, Michigan, USA · Pontifical Catholic University of Rio de Janeiro Rio de Janeiro, Brazil · Heidelberg University Heidelberg, Germany +1
MisEdu-RAG: A Misconception-Aware Dual-Hypergraph RAG for Novice Math Teachers
Date pendingZhihan Guo, Yuting Lu, Jionghao LinStudent Misconceptions Math Problems
The University of Hong Kong, Hong Kong SAR, China · Carnegie Mellon University, Pittsburgh PA 15213, USA · Monash University, Clayton VIC 3800, Australia

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The Scaling Paradox in Human-AI Collaboration

Anyan Qi, Mengxin Wang

Naveen Jindal School of Management, University of Texas at Dallas, USA

Human-Ai Collaboration Scaling Laws Collaboration Student Misconceptions

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The discovery of scaling laws has highlighted the extraordinary potential of AI systems with a striking empirical pattern: as AI systems scale, their capabilities tend to improve predictably. Yet, in real-world applications, AI rarely operates in isolation; instead, it often works alongside humans, raising the question of whether these gains persist in human-AI collaboration. In this work, we develop an analytical model to examine when the empirical scaling benefits of AI translate into improved human-AI joint system performance. We demonstrate that the performance of a human-AI system can scale positively as the AI scales up-provided that humans have an accurate perception of the AI's capabilities. Human misperception, however, can fundamentally alter this relationship: i) when humans over-perceive the AI's capabilities, a scaling paradox may arise, in which greater AI scale reduces overall system performance and amplifies firm-level profit losses, and (ii) when humans under-perceive the AI's capabilities, performance still improves with scale but at a substantially slower rate. We further show that firms can actively manage these distortions through operational policies such as cost internalization and perception alignment, whose effectiveness depends on the economics of AI deployment and the direction of human misperception. These findings suggest that organizations may benefit more from managing the human-AI interface than from simply investing in larger, more expensive AI systems. More broadly, our results suggest that AI scaling should be viewed not only as a technological challenge, but also as a behavioral and operational one, and caution against the view that larger AI systems will automatically lead to better operational outcomes. Whether AI scaling creates value ultimately depends on how increased AI capabilities shape human beliefs and collaborative efforts.

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