Pair programming with AI often faces challenges in productive communication and engagement. Integrating embodiment offers a promising solution by making AI a more engaging and context-aware programming partner. To explore how embodied AI agent supports programming learning and affects user experiences, we designed a virtual reality (VR) programming environment with Wizard-of-Oz-controlled AI agents. Our study collected data from 18 participants through knowledge acquisition assessments and interviews. The results showed that embodiment improved engagement, enhanced communication efficiency, and offered emotional support. Specifically, the incorporation of embodied actions allows users to perceive the AI agent as a “programming partner” and introduces many interactions that resemble those shared with real-life partners. However, the effectiveness of embodied actions in supporting users with programming tasks depends on the timing and accuracy of those actions. This study reveals the potential of embodied AI agents in advancing programming education and provides valuable design insights for creating more intuitive and supportive AI programming partners.

The rapid evolution of artificial intelligence (AI), specifically large language models (LLMs), has opened opportunities for various educational applications. This paper explored the feasibility of utilizing ChatGPT, one of the most popular LLMs, for automating feedback for Java programming assignments in an introductory computer science (CS1) class. Specifically, this study focused on three questions: 1) To what extent do students view LLM-generated feedback as formative? 2) How do students see the comparative affordances of feedback prompts that include their code, vs. those that exclude it? 3) What enhancements do students suggest for improving LLM-generated feedback? To address these questions, we generated automated feedback using the ChatGPT API for four lab assignments in a CS1 class. The survey results revealed that students perceived the feedback as aligning well with formative feedback guidelines established by Shute. Additionally, students showed a clear preference for feedback generated by including the students' code as part of the LLM prompt, and our thematic study indicated that the preference was mainly attributed to the specificity, clarity, and corrective nature of the feedback. Moreover, this study found that students generally expected specific and corrective feedback with sufficient code examples, but had diverged opinions on the tone of the feedback. This study demonstrated that ChatGPT could generate Java programming assignment feedback that students perceived as formative. It also offered insights into the specific improvements that would make the ChatGPT-generated feedback useful for students.

Understanding how children perceive and interact with teachable agents (systems where children learn through teaching a synthetic character embedded in an intelligent tutoring system) can provide insight into the effects of so-cial interaction on learning with intelligent tutoring systems. We describe results from a think-aloud study where children were instructed to narrate their experience teaching Stacy, an agent who can learn to solve linear equations with the student's help. We found treating her as a partner, primarily through aligning oneself with Stacy using pronouns like you or we rather than she or it significantly correlates with student learning, as do playful face-threatening comments such as teasing, while elaborate explanations of Stacy's behavior in the third-person and formal tutoring statements reduce learning gains. Additionally, we found that the agent's mistakes were a significant predictor for students shifting away from alignment with the agent.