2 citations · 3 across the 10 of their papers we have counts for
11 papers
If My Toy Could Talk: How Young Children Imagine, Design, and Test AI-Enabled Toys
Feiwen Xiao, Ruiyang Wu, Xinyue Cui +4
To investigate the design space where children might design AI chatbots for their own toys, we developed ToyTalk, a technology probe that positions children as designers of LLM-ena…
How Children Design and Reason about Trustworthy AI Chatbots
Deniz Ozturk, Jiayu Li, Daksh Pratap Singh +9
Children increasingly interact with AI chatbots, making trust calibration essential to AI literacy. Prior research has examined children's trust in AI mainly as users evaluating sy…
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 Teacher-Chatbot Interaction and Affect in Block-Based Programming
Bahare Riahi, Ally Limke, Xiaoyi Tian +8
AI-based chatbots have the potential to accelerate learning and teaching, but may also have counterproductive consequences without thoughtful design and scaffolding. To better unde…
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…
SnapClass: An AI-Enhanced Classroom Management System for Block-Based Programming
Bahare Riahi, Xiaoyi Tian, Ally Limke +5
Block-Based Programming (BBP) platforms, such as Snap!, have become increasingly prominent in K-12 computer science education due to their ability to simplify programming concepts…
Investigating the Impact and Student Perceptions of Guided Parsons Problems for Learning Logic with Subgoals
Sutapa Dey Tithi, Xiaoyi Tian, Min Chi +1
Parsons problems (PPs) have shown promise in structured problem solving by providing scaffolding that decomposes the problem and requires learners to reconstruct the solution. Howe…
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…