most citedExploring Teacher-Chatbot Interaction and Affect in Block-Based Programming

2 citations · 3 across the 9 of their papers we have counts for

collaborators

13 papers

cs.HC2026

From Instructional Intent to Classroom-Ready Lessons: Investigating Teacher-AI Collaboration in Lesson Authoring for Secondary Computing Education

Tahreem Yasir, Wenbo Li, Deniz Ozturk +4

Generative AI is increasingly studied as a tool for supporting lesson authoring; the creation and adaptation of classroom materials, yet little is known about how it should fit int…

cs.HC2026

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…

cs.HC2026

Adapting for AI: How elementary teachers adjust their practices for an AI-integrated curriculum

Fasika Melese, Ruiyang Wu, Xinyue Cui +4

Conversational AI tools are entering children's everyday experiences, and schools are interested in adopting them. However, successful classroom integration depends not only on the…

cs.HC2026

Will It Teach as Intended? How Teachers Configure Educational AI Chatbots

Bahare Riahi, Deniz Ozturk, Alice Guth +7

Teachers are increasingly using generative AI to support instruction, yet it remains unclear how pedagogical intentions are translated into chatbot configurations and reflected in…

cs.HC2026

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…

cs.AI2026

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…

cs.CY2026

Democratizing Foundations of Problem-Solving with AI: A Breadth-First Search Curriculum for Middle School Students

Griffin Pitts, Kimia Fazeli, Tirth Bhatt +5

As AI becomes more common in students' everyday experiences, a major challenge for K-12 AI education is designing learning experiences that can be meaningfully integrated into exis…

cs.AI2026

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…

cs.CY2026★ 2 cited

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…

cs.HC2026

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…

cs.AI2026★ 1 cited

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…

cs.HC2025

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…

cs.AI2025

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…