Special Track 3

Special Track on “Preparing the Workforce for Industry 4.0: Robotics, Automation, and Ubiquitous Smart Technologies in Education”

The industry 4.0 is transforming the world with technologies like autonomous robots, artificial intelligence, and Internet of things. Technology literacy will be a key determiner in employability over the coming decade. The higher education sector must win the race between technology and education to prepare students for career readiness. Robotics, automation and ubiquitous smart technologies not only have become vital knowledge to learn for STEM students, but also can be leveraged to amplify and transform learning and teaching. Nevertheless, many challenges remain as to how these technologies can mend gaps in equity, engage students as unique individuals and prepare them for work and life in an uncertain future. This special track aims to provide a platform for teachers, educational researchers, and technology developers to discuss and share their insights on the trends and issues in the field of robotic, automatic and ubiquitous (RAU) learning technologies. We welcome a wide spectrum of papers including rigorous educational research, experience report as well as the development and evaluation of specific technological tool.


  1. Design principles, frameworks and standards for RAU learning technologies
  2. Learning design for integrating RAU learning technology (curriculum development, pedagogical practice, classroom management, reflected teaching and learning, etc.)
  3. Application of knowledge graph and ontology engineering in automated learning and teaching
  4. Integrating RAU learning technologies and student modelling for personalized education
  5. Human-computer interaction and user experience study of RAU learning technologies
  6. Application of RAU learning technologies in on-the-job training, casual learning and lifelong learning
  7. Application of RAU learning technologies in faculty development
  8. Models, strategies, and standards for evaluating educational benefits of RAU learning technologies
  9. Case studies and challenges of adopting RAU learning technologies at scale
  10. Ethical, social and policy considerations related to RAU learning technologies

Program Co-chairs

  • Akihiro Kashihara, University of Electro-Communications, Japan
  • Ian Piumarta, Kyoto University of Advanced Science, Japan
  • Osamu Tabata, Kyoto University of Advanced Science, Japan
  • Takuichi Nishimura, AIST, Japan
  • Usman Naeem, Queen Mary University of London, UK
  • Zilu Liang, Kyoto University of Advanced Science, Japan

Program Committee

  • Ana Amélia Amorim Carvalho, University of Coimbra, Portugal
  • Domenico Santaniello, University of Salerno, Italy
  • Irwan Kautsar, Universitas Muhammadiyah Sidoarjo, Indonesia
  • I Nyoman Darma Kotama, Udayana University, Indonesia
  • James Zheng Liu, South China University of Technology, China
  • Marina Freire-Gormaly, York University, Canada
  • Mark Guzdial, University of Michigan, US
  • Mendiola Maria, Instituto Politécnico Nacional (IPN), Mexico
  • Miguel Gomes da Costa Junior, Univ of Macau, China
  • Seong-Woo Kim, Seoul National University, Korea
  • Shinobu Hasegawa, JAIST, Japan
  • Tom Worthington, Australian National University, Australia
  • Yonghe Wu, East China Normal University, China


  • Paper Submission Deadline: 4 July 2020 (Hard Deadline)
  • Notification of Acceptance: 31 August 2020
  • Camera-ready submission: 21 September 2020
  • Early-bird and Presenter Registration: 30 September 2020
  • Conference: 8 – 11 December 2020


  • Full (6-8 pages) or short (4-6 pages) paper w/ oral presentation
  • Short (4-6 pages) or work-in-progress paper (2-4 pages) w/ poster presentation


Submissions will be accepted electronically via TALE conference online submission system. You will be able to select special track 3 during the submission process. Guidelines and templates are available on the conference website. All submission will be evaluated through a double-blind peer-review process.


All accepted and registered full, short, and work-in-progress papers that are presented at the special track will be published in the conference proceedings (USB with ISBN) and submitted to the IEEE Xplore® digital library. Content loaded into Xplore is made available by IEEE to its abstracting and indexing partners, including Elsevier (Scopus, Ei Compendex), Clarivate Analytics (CPCI—part of Web of Science) and others, for potential inclusion in their respective databases. In addition, authors of selected papers may be invited to submit expanded versions of their papers for consideration for special issues of a number of journals to be published in conjunction with the Special Track.



Organized in cooperation with Kyoto University of Advanced Science