Preparation for Introducing Generative AI into the Next-Generation Educational App “A-txt” for Smart Devices
This study explores the integration of generative AI (large language models) into the AR-based educational support application “A-txt” to develop a next-generation personalized learning support system utilizing learners’ behavioral and question histories. A-txt recognizes charts and texts on printed educational materials as AR markers and displays associated digital content. In this paper, we present a prototype system in which generative AI provides real-time hints and explanations based on user interactions and natural language queries. Through experiments involving image recognition and learning support responses, we verify the effectiveness of the system. We also discuss future developments, including automatic content recommendations, visualization of weak areas, and feedback functions for educators based on learner behavior logs. This research provides a foundational step toward building a context-responsive and self-regulated learning support environment by integrating AR and generative AI technologies.
Authors
- Tadashi Fukumoto
- K. Hirata (ORCID: https://orcid.org/0000-0001-8454-7852)
- Ryohei Kameyama
- Hideyuki Kanematsu (ORCID: https://orcid.org/0000-0002-9144-6539)
- Akira Okada (ORCID: https://orcid.org/0000-0001-5740-4346)
- Kuniaki Yajima
- Yutaro Akimoto (ORCID: https://orcid.org/0000-0002-0783-8039)
- Takeshi Matsumura (ORCID: https://orcid.org/0000-0001-6729-1103)
- Yasuhiro Kobayashi (ORCID: https://orcid.org/0000-0002-4857-1297)
- Nobuyuki Ogawa
- Takehito Kato (ORCID: https://orcid.org/0000-0003-2449-5645)
- Shin‐nosuke Suzuki
Institutions
- University of Tsukuba (JP)
- Sendai National College of Technology (JP)
- National Institute of Technology, Gifu College (JP)
- National Institute of Technology (JP)
- Museum of Japanese Art Yamato Bunkakan (JP)
- National Institute of Technology, Kagoshima College (JP)
- National Institute of Technology, Oyama College (JP)
- The University of Osaka (JP)
Publication Details
- Journal
- Procedia Computer Science
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.procs.2025.09.404
- Primary Topic
- Intelligent Tutoring Systems and Adaptive Learning
- Type
- article
- Field-Weighted Citation Impact
- 0.00