Lei Shi

1. Cultural heritage (CH) education confronts a unique pedagogical challenge, extending beyond the mere preservation and accessibility of artifacts; the deeper objective aims to foster an embodied ...

Rhythm and articulation are essential for expressive guitar performance. Existing tools provide basic beat cues, whereas beginners often struggle to align with these cues when playing complex techniques, such as strumming and muting. Informed by a formative study with five instructors and grounded in embodied learning theories, we present FretFlow, a haptic vest-based tool that simulates common instructional practices to guide learners through physical interactions like tapping. The key to FretFlow is its design space that maps rhythmic and articulation patterns in various playing techniques to distinct haptic patterns, enabling authoring of haptic scores. FretFlow further dynamically adapts haptic intensity based on learners’ real-time performance accuracy, accompanied by multimodal guidance across haptic, visual, and audio channels. We iteratively refined haptic designs across two rounds with 46 participants, followed by a two-week user study with 20 beginners. Results show that FretFlow improves learners’ rhythmic accuracy and expressive performance.

Live-streaming platforms have become an important medium for online learning, especially amid growing demand for interactive remote education. However, conventional text-based interactions on these platforms often perpetuate learner passivity and limit collaborative agency, hindering sustained engagement and knowledge retention. To address this gap, we present Quiz Traveller, an audience-participation game (APG) for world history learning that employs multi-player single-character control (MSCC), a crowdsourced interaction mechanism popularised by Twitch Plays Pokémon, as its core interaction paradigm. The game enables learners to collectively steer gameplay (e.g., navigating quizzes, solving historical challenges) via real-time chat commands, implementing two control modes: Anarchy (sequential command execution) and Democracy (voting-based aggregation). We evaluated this game through a single-group study (N = 25) using parallel-form pre- and post-tests (i.e., to measure short-term knowledge retention) and the Game Experience Questionnaire (GEQ) (i.e., to assess subjective engagement and experience). Results from the single-group pre-test and post-test evaluation reveal substantial improvement in short-term knowledge retention (i.e., enhanced quiz performance). GEQ responses indicate moderately positive ratings for competence, performance, and positive affect, alongside lower scores for flow and immersion, and minimal negative affect. Our work demonstrates that Quiz Traveller, an educational game employing MSCC mechanics, achieves substantial learning gains whilst maintaining positive player experience, providing proof of concept that MSCC-based games warrant further investigation in educational live-streaming. This study offers a scalable, chat-native initial design instantiation of MSCC for educational contexts for educators and platform designers, providing a concrete foundation for future mechanism-focused research whilst offering practical implications for game-based learning in remote and hybrid education settings.

This paper offers a comprehensive review of current developments in artificial intelligence (AI)-based 3D model creation, with an emphasis on techniques utilizing variational autoencoders (VAEs) and generative adversarial networks (GANs). 3DGAN, paired 3D model generation with GAN, conditional GAN, FaceVAE, voxel-based 3D object reconstruction, and 3D-VAE-SDFRaGAN are the six main techniques that are studied in this work. Each method is discussed, highlighting its architectural framework, data representation, and specific approach to generating 3D models. First, the paper introduces basic terms and classical 3D modeling techniques and provides a comparative analysis of them based on their workflow, purpose and field of application. In subsequent chapters, methods for generating 3D models based on the use of GANs and VAEs are reviewed, describing its methodology, experimentation technique, results, and comparison with other methods. The review outlines the strengths and limitations of each approach and their applications in object reconstruction, shape generation, and maintaining model consistency. It concludes by emphasizing how AI-driven methods can advance 3D modeling, underscoring the need for further research to enhance quality, control, and training reliability. The findings show AI’s significant impact on automating complex modeling tasks and enabling new creative opportunities in 3D content development.