The emergence of generative AI, particularly large language models (LLMs), has opened the door for student-centered and active learning methods like project-based learning (PBL). However, PBL poses practical implementation challenges for educators around project design and management, assessment, and balancing student guidance with student autonomy. The following research documents a co-design process with interdisciplinary K-12 teachers to explore and address the current PBL challenges they face. Through teacher-driven interviews, collaborative workshops, and iterative design of wireframes, we gathered evidence for ways LLMs can support teachers in implementing high-quality PBL pedagogy by automating routine tasks and enhancing personalized learning. Teachers in the study advocated for supporting their professional growth and augmenting their current roles without replacing them. They also identified affordances and challenges around classroom integration, including resource requirements and constraints, ethical concerns, and potential immediate and long-term impacts. Drawing on these, we propose design guidelines for future deployment of LLM tools in PBL.
Classroom debates are a unique form of collaborative learning characterized by fast-paced, high-intensity interactions that foster critical thinking and teamwork. Despite the recognized importance of debates, the role of AI tools, particularly LLM-based systems, in supporting this dynamic learning environment has been under-explored in HCI. This study addresses this opportunity by investigating the integration of LLM-based AI into real-time classroom debates. Over four weeks, 22 students in a Design History course participated in three rounds of debates with support from ChatGPT. The findings reveal how learners prompted the AI to offer insights, collaboratively processed its outputs, and divided labor in team-AI interactions. The study also surfaces key advantages of AI usage—reducing social anxiety, breaking communication barriers, and providing scaffolding for novices—alongside risks, such as information overload and cognitive dependency, which could limit learners' autonomy. We thereby discuss a set of nuanced implications for future HCI exploration.
Educators and policymakers are increasingly trying to control youth access to technology in the classroom, while simultaneously working to deploy technology for purposes of surveillance and behavioral control. While many scholars have explored the implications of intensifying dataveillance and disciplinary practices deployed by teachers in K-12 schooling, few have investigated how students’ visions of technology deployment and use might align or diverge from those of designers and teachers. Using the resulting data from participatory design workshops and ethnographic research with students and staff in alternative hybrid schools, we explore students’ concepts of future technologies for the classroom and how these artifacts reflect student perceptions of safety and good behavior. Rather than simply accepting or resisting the role of technology in discipline and punishment as presented by technology creators, wherein disciplinary decisions are made by teachers using technology, students actively respond to these narratives to increase the objectivity and accuracy of punishment. The results of this work show how visions of future technology can sometimes reify new forms of power and other times respond to unmet student needs to exert control in the classroom.
Multimodal large language models (MLLMs) are Generative AI models that take different modalities such as text, audio, and video as input and generate appropriate multimodal output. Since such models will be integrated into future educational tools, a human-centered design approach that takes students’ perspectives into account is essential while designing such applications.
This paper describes two co-design workshops which were conducted with 79 student groups to examine how they design and prototype future educational tools integrated with MLLMs. Through various activities in the workshops, students discussed relevant educational problems, created journey maps, storyboards and low fidelity prototypes for their applications, and evaluated their applications based on relevant design principles. We found that students’ applications used MLLMs for important learning environment design features such as multimodal content creation, personalization, and feedback. Based on these findings, we discuss future research directions for the design of multimodality in generative AI educational applications.
Teaching scientific concepts is essential but challenging, and analogies help students connect new concepts to familiar ideas.
Advancements in large language models (LLMs) enable generating analogies, yet their effectiveness in education remains underexplored.
In this paper, we first conducted a two-stage study involving high school students and teachers to assess the effectiveness of LLM-generated analogies in biology and physics through a controlled in-class test and a classroom field study.
Test results suggested that LLM-generated analogies could enhance student understanding particularly in biology, but require teachers' guidance to prevent over-reliance and overconfidence.
Classroom experiments suggested that teachers could refine LLM-generated analogies to their satisfaction and inspire new analogies from generated ones, encouraged by positive classroom feedback and homework performance boosts.
Based on findings, we developed and evaluated a practical system to help teachers generate and refine teaching analogies.
We discussed future directions for developing and evaluating LLM-supported teaching and learning by analogy.
Virtual reality head-mounted displays (HMDs) offer unique and immersive opportunities for higher education. However, current research focuses on small-scale and infrequent use cases, raising questions about large-scale HMD integration into classrooms. We explored logistical and pedagogical challenges and opportunities when using 30 VR HMDs in a design class of 55 undergraduate students throughout a 12-week term. Each student shared an HMD with a partner, using it weekly in class and at home. We administered questionnaires and conducted observations and interviews. Our results reveal highly positive student engagement, but instructors and students must adapt to unique HMD characteristics and challenges, including in-VR lecturing practices, developing safety measures, and mitigating cybersickness. Although instructor-led VR tutorials were helpful, most learning occurred in individual, paired, and group activities, where screencasting and HMD sharing fostered collaborative learning. Free time during classes provided an opportunity for targeted instructor support while allowing students to explore emerging practices.
Students often take digital notes during live lectures, but current methods can be slow when capturing information from lecture slides or the instructor's speech, and require them to focus on their devices, leading to distractions and missing important details. This paper explores supporting live lecture note-taking with mixed reality (MR) to quickly capture lecture information and take notes while staying engaged with the lecture. A survey and interviews with university students revealed common note-taking behaviors and challenges to inform the design. We present MaRginalia to provide digital note-taking with a stylus tablet and MR headset. Students can take notes with an MR representation of the tablet, lecture slides, and audio transcript without looking down at their device. When preferred, students can also perform detailed interactions by looking at the physical tablet. We demonstrate the feasibility and usefulness of MaRginalia and MR-based note-taking in a user study with 12 students.