Search arXivSearch

arXiv · 2405.13701

Metabook: A Mobile-to-Headset Pipeline for 3D Story Book Creation in Augmented Reality

Abstract

The AR 3D book has shown significant potential in enhancing students' learning outcomes. However, the creation process of 3D books requires a significant investment of time, effort, and specialized skills. Thus, in this paper, we first conduct a three-day workshop investigating how AI can support the automated creation of 3D books. Informed by the design insights derived from the workshop, we developed Metabook, a system that enables even novice users to create 3D books from text automatically. To our knowledge, Metabook is the first system to offer end-to-end 3D book generation. A follow-up study with adult users indicates that Metabook enables inexperienced users to create 3D books, achieving reduced efforts and shortened preparation time. We subsequently recruited 22 children to examine the effects of AR 3D books on children's learning compared with paper-based books. The findings indicate that 3D books significantly enhance children's interest, improve memory retention, and reduce cognitive load, though no significant improvement was observed in comprehension. We conclude by discussing strategies for more effectively leveraging 3D books to support children's learning and offer practical recommendations for educators.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yibo Wang, Yuanyuan Mao, Lik-Hang Lee, Shi-ting Ni, Zeyu Wang, Xiaole Gu, Pan Hui. 2025-08-09. Metabook: A Mobile-to-Headset Pipeline for 3D Story Book Creation in Augmented Reality. https://arxiv.org/abs/2405.13701

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Describe Me Something You Do Not Remember - Challenges and Risks of Exposure Design Using Generative Artificial Intelligence for Therapy of Complex Post-traumatic Stress Disorder

Post-traumatic stress disorder (PTSD) is associated with sudden, uncontrollable, and intense flashbacks of traumatic memories. Trauma exposure psychotherapy has proven effective in reducing the severity of trauma-related symptoms. It involves controlled recall of traumatic memories to train coping mechanisms for flashbacks and enable autobiographical integration of distressing experiences. In particular, exposure to visualizations of these memories supports successful recall. Although this approach is effective for various trauma types, it remains available for only a few. This is due to the lack of cost-efficient solutions for creating individualized exposure visualizations. This issue is particularly relevant for the treatment of Complex PTSD (CPTSD), where traumatic memories are highly individual and generic visualizations do not meet therapeutic needs. Generative Artificial Intelligence (GAI) offers a flexible and cost-effective alternative. GAI enables the creation of individualized exposure visualizations during therapy and, for the first time, allows patients to actively participate in the visualization process. While GAI opens new therapeutic perspectives and may improve access to trauma therapy, especially for CPTSD, it also introduces significant challenges and risks. The extreme uncertainty and lack of control that define both CPTSD and GAI raise concerns about feasibility and safety. To support safe and effective three-way communication, it is essential to understand the roles of patient, system, and therapist in exposure visualization and how each can contribute to safety. This paper outlines perspectives, challenges, and risks associated with the use of GAI in trauma therapy, with a focus on CPTSD.

cs.HC

KnowTeX: Visualizing Mathematical Dependencies

Dependency graphs that show how definitions, theorems, and proofs relate to each other are valuable for understanding the structure of mathematical texts. Existing tools such as Lean Blueprint and plasTeXdepgraph generate such graphs within formal proof ecosystems, but they require familiarity with proof assistants or specific compilation pipelines. We present KnowTeX, a standalone Python tool that extracts dependency graphs directly from LaTeX sources without requiring any external framework. KnowTeX supports two complementary modes: a manual mode where authors annotate their source with lightweight commands compatible with Lean Blueprint, and an infer mode that automatically discovers dependencies through a layered system of deterministic and heuristic rules. The tool handles multi-file projects, detects cycles, applies transitive reduction, and exports graphs in DOT, TikZ, and PNG formats with an interactive preview. We evaluate KnowTeX on several mathematical texts and discuss how it complements recent tools such as LeanArchitect, which operates from the Lean side, while KnowTeX works entirely on the LaTeX side without requiring any formalization.

cs.HC

Helping Customers in Distress: An LLM-powered Agent that Converses, Probes, and Routes

Banks receive millions of reports of fraud, scams, and disputed transactions every year, making it challenging to accurately direct customers to the appropriate specialist teams for assistance. The existing manual process driven by humans is slow and stressful for both customers and staff. To address this, we develop a customer-facing AI powered triaging agent that leverages large language models (LLMs) to conduct multi-turn conversations, ask relevant questions, and classify cases for accurate, policy-guided routing, making it embedded in the customer journey. To evaluate and continuously improve the agent, synthetic digital twins of real customers were simulated, generating realistic, labelled dialogues based on historical data to test a wide range of real-world scenarios. This work details the triage agent's modelling approach, integration with policy, safety guardrails and reasoning frameworks, the use of the synthetic agent for scalable evaluation, and findings on the AI system's accuracy, robustness, and compliance. Results show that the agent successfully improves triaging of historical cases, achieving a 30.6% increase in classification accuracy, with high satisfaction levels reported by our subject-matter experts, highlighting how targeted probing can lead to more effective triage in banking operations at scale.

cs.HC