A number of interactive technologies for learning integrate virtual agents—as expert or peer agents—in their interactions to support student learning across domains. However, these agents tend to be mainly designed to foster students’ cognitive and metacognitive aspects, without addressing a critical component of fostering trust with students. We conducted a co-design study with 11 students and one teacher on trustworthy virtual peer agents (VPAs) with Armenian middle school students for math learning. Through a three-phase design-based approach, students and their teacher collaborated to develop six VPA prototypes, differentiated by appearance (human, robot, animal) and communication style (trustworthy vs. untrustworthy). Results from post-design semi-structured interviews reveal that students’ trust in VPAs is shaped not only by visual appearance but also by empathetic and communication factors. These findings offer valuable insights for creating effective and trustworthy interactions within educational technologies through virtual agents.

AI-based educational technologies may be most welcome in classrooms when they align with teachers' goals, preferences, and instructional practices. Teachers, however, have scarce time to make such customizations themselves. How might the crowd be leveraged to help time-strapped teachers? Crowdsourcing pipelines have traditionally focused on content generation. It is an open question how a pipeline might be designed so the crowd can succeed in a revision/customization task. In this paper, we explore an initial version of a teacher-guided crowdsourcing pipeline designed to improve the adaptive math hints of an AI-based tutoring system so they fit teachers' preferences, while requiring minimal expert guidance. In two experiments involving 144 math teachers and 481 crowdworkers, we found that such an expert-guided revision pipeline could save experts' time and produce better crowd-revised hints (in terms of teacher satisfaction) than two comparison conditions. The revised hints however, did not improve on the existing hints in the AI tutor, which were carefully-written but still have room for improvement and customization. Further analysis revealed that the main challenge for crowdworkers may lie in understanding teachers' brief written comments and implementing them in the form of effective edits, without introducing new problems. We also found that teachers preferred their own revisions over other sources of hints, and exhibited varying preferences for hints. Overall, the results confirm that there is a clear need for customizing hints to individual teachers' preferences. They also highlight the need for more elaborate scaffolds so the crowd can have specific knowledge of the requirements that teachers have for hints. The study represents a first exploration in the literature of how to support crowds with minimal expert guidance in revising and customizing instructional materials.