8 papers
Confirming Correct, Missing the Rest: LLM Tutoring Agents Struggle Where Feedback Matters Most
Tahreem Yasir, Wenbo Li, Sam Gilson +3
Effective tutoring requires distinguishing optimal, valid but suboptimal, and incorrect student solutions, a distinction central to intelligent tutoring systems (ITS) but untested…
When Verification Hurts: Asymmetric Effects of Multi-Agent Feedback in Logic Proof Tutoring
Tahreem Yasir, Sutapa Dey Tithi, Benyamin Tabarsi +7
Large language models (LLMs) are increasingly used for automated tutoring, but their reliability in structured symbolic domains remains unclear. We study step-level feedback for pr…
Data-Driven Hints in Intelligent Tutoring Systems
Sutapa Dey Tithi, Kimia Fazeli, Dmitri Droujkov +3
This chapter explores the evolution of data-driven hint generation for intelligent tutoring systems (ITS). The Hint Factory and Interaction Networks have enabled the generation of…
Exploring the Design and Impact of Interactive Worked Examples for Learners with Varying Prior Knowledge
Sutapa Dey Tithi, Xiaoyi Tian, Ally Limke +2
Tutoring systems improve learning through tailored interventions, such as worked examples, but often suffer from the aptitude-treatment interaction effect where low prior knowledge…
Adaptive Scaffolding for Cognitive Engagement in an Intelligent Tutoring System
Sutapa Dey Tithi, Nazia Alam, Tahreem Yasir +4
The ICAP framework defines four cognitive engagement levels: Passive, Active, Constructive, and Interactive, where increased cognitive engagement can yield improved learning. Howev…
The promise and limits of LLMs in constructing proofs and hints for logic problems in intelligent tutoring systems
Sutapa Dey Tithi, Arun Kumar Ramesh, Clara DiMarco +4
Intelligent tutoring systems have demonstrated effectiveness in teaching formal propositional logic proofs, but their reliance on template-based explanations limits their ability t…