Xinying Hou

We first proposed pedagogical prompting, a theoretically-grounded new concept to elicit learning-oriented responses from LLMs. For proof-of-concept learning intervention in a real educational setting, we selected early undergraduate CS education (CS1/CS2) as the example context. Based on instructor insights, we designed and developed a learning intervention as an interactive system with scenario-based instruction to train pedagogical prompting skills. Finally, we assessed its effectiveness with pre/post-tests in a user study of CS undergraduates.

We presented CodeTailor, a system that leverages a large language model (LLM) to provide personalized help to students while still encouraging cognitive engagement. CodeTailor provides a personalized Parsons puzzle to support struggling students. In a Parsons puzzle, students place mixed-up code blocks in the correct order to solve a problem. CodeTailor distinguishes itself from existing LLM-based products by providing an active learning opportunity where students are expected to "solve" the puzzle rather than simply acting as passive consumers by "read- ing" a direct solution.

We presented the results of a thematic analysis on a data set from 33 learners as they independently learned Python by working on 45 code-authoring tasks with access to an AI Code Generator based on OpenAI Codex. Our analysis reveals four distinct coding approaches when writing code with an AI code generator: AI Single Prompt; AI Step-by-Step; Hybrid; and Manual coding, where learners wrote the code themselves.

In this paper, we explore using Parsons problems to scaffold novice programmers who are struggling while solving write-code problems. Parsons problems, in which students put mixed-up code blocks in order, can be created quickly and already serve thousands of students while other types of programming support methods are expensive to develop or do not scale. We conducted studies in which novices were given equivalent Parsons problems as optional scaffolding while solving write-code problems. We investigated when, why, and how students used the Parsons problems as well as their perceptions of the benefits and challenges.

In this math digital learning game, one version encouraged playing and learning through an open learner model, while one encouraged playing for enjoyment through an analogous open enjoyment model. We compared these versions to a control version that is neutral with respect to learning and enjoyment. The learning-oriented group engaged more in re-practicing, while the enjoyment-oriented group demonstrated more exploration of different mini-games. In turn, our analyses have led to preliminary ideas about how to use AI to provide recommendations that are more aligned with students’ dynamic learning and enjoyment states and preferences.