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Education
13 items tagged with “Education”
Publications
- NivTA: Towards a Naturally Interactable Edu-Metaverse Teaching Assistant for CAVEEdu-metaverse is a specialized metaverse dedicated for interactive education in an immersive environment. Its main purpose is to immerse the learners in a digital environment and conduct learning activities that could mirror reality. Not only does it enable activities that may be difficult to perform in the real world, but it also extends the interaction to personalized and CL. This is a more effective pedagogical approach as it tends to enhance the motivation and engagement of students and it increases their active participation in lessons delivered. To this extend, we propose to realize an interactive virtual teaching assistant called NivTA. To make NivTA easily accessible and engaging by multiple users simultaneously, we also propose to use a CAVE virtual environment (CAVE-VR) as a "metaverse window" into concepts, ideas, topics, and learning activities. The students simply need to step into the CAVE-VR and interact with a life-size teaching assistant that they can engage with naturally, as if they are approaching a real person. Instead of text-based interaction currently developed for large language models (LLM), NivTA is given additional cues regarding the users so it can react more naturally via a specific prompt design. For example, the user can simply point to an educational concept and ask NivTA to explain what it is. To guide NivTA onto the educational concept, the prompt is also designed to feed in an educational KG to provide NivTA with the context of the student’s question. The NivTA system is an integration of several components that are discussed in this paper. We further describe how the system is designed and implemented, along with potential applications and future work on interactive collaborative edu-metaverse environments dedicated for teaching and learning.
- Enhance Learner Engagement ThroughExperiential Learning in a Gamified Simulation:A Longitudinal StudyExperiential learning in virtual reality (VR) has gained con-siderable attention from both practitioners and researchers in recentyears. Despite its potential, there is still a gap in effectively engagingstudents through experiential learning. Gamification has emerged as apromising strategy to bridge this gap by incorporating interactive andmotivational elements into learning experiences. To explore the poten-tial of combining gamification with experiential learning in VR, we con-ducted a longitudinal study with 68 undergraduate students enrolled ina human-computer interaction (HCI) course. The study focused on agamified simulation of Fitts’ Law, a fundamental concept for modellinghuman movement in HCI, to evaluate its impact on learner engagementand the translation of this engagement into knowledge gains over boththe short and long term. Our findings indicated that the gamified ex-periential learning approach significantly enhanced learner engagement,especially affective engagement, compared to traditional methods thatrely on texts and figures. The longitudinal data also suggested that theapproachledtobetterknowledgeretention.However,norelationshipwasfound between the observed learner engagement and knowledge change.This highlights the need for further research to study how to effectivelyconvert learner engagement into measurable knowledge gains throughexperiential learning in gamified simulations.
Essays
- Students Want AI Companions — Not to Be WatchedA CHI 2026 study finds that university students recognize the learning value of AI prompts, yet strongly reject gaze and facial monitoring — prioritizing autonomy and privacy.
- Worked Examples Are Not Better Just Because They Are More InteractiveA CHI 2026 study shows that among interactive worked examples, novices benefit more from guided completion, while students with stronger foundations benefit more from finding and fixing errors.
- CHI 2026 | Add an AI Classmate — Small Classes of Two May Benefit MostThis CHI 2026 paper designs AI as a teacher-triggered virtual peer and observes how it reshapes student talk in Japanese elementary classrooms.
- CHB 2026 | Does a Better-Looking AI Teacher Make Students Trust It More?For GenAI-generated pedagogical agents, appearance shapes learner trust and motivation — but “looking good” does not automatically produce identification.
- CHI 2026 | High Schoolers Find Scratch Childish — What Blocks Them Is a Sense of GrowthHigh schoolers dislike block-based programming not because it is too simple, but because the curriculum never lets them see themselves growing.
- CHI 2026 | Helping Kids Reflect with AI Is Hardest Not in Follow-ups, but in Knowing When to StopRules keep the teaching path on track; the LLM handles flexible follow-ups. A robotics camp study finds that responding more does not mean understanding deeper.
- CHI 2026 | Everyone Has an LLM — Why Do Technical People Still Win?Even with the same LLM, technical experience still strongly predicts performance. What really widens the gap is planning, expression, understanding, and verification.
- IEEE TSE 2026 | Copilot Helps Students Get More Answers Right — and Pulls More Attention into DebuggingAn eye-tracking study with 50 students shows that Copilot raised task accuracy while also increasing completion time and visual cognitive effort.
- CHI 2026 | AI Can Save Teachers Time — But Not Classroom JudgmentInterviews with 22 teachers show that generative AI can ease repetitive planning work, yet what teachers guard most carefully is professional judgment, relationships, and responsibility.
- ICBL 2026 | A More Human AI Teacher May Not Teach Better — Don't Outsource ThinkingThe X-Verse team argues that LLM pedagogical agents should shift focus from persona cues to cooperative structure: keep productive friction, co-build artifacts, and keep learners accountable for thinking.
- CHI 2026 | AI Helps Teachers Write Feedback — Drafts Improve, But Do Students Learn?In a classroom trial with 354 students, after TAs reviewed AI suggestions, revision quality rose, but the post-test found no significant between-group difference, and grading was not faster. Which step did the comments concretely help?