Educational Futures of Intelligent Synergies between Humans, Digital Twins, Avatars, and Robots: The iSTAR Framework

As AI develops, collaboration between teachers and machines in educational tasks has become a research concern. The iSTAR paper, published in the Journal of Applied Learning and Teaching in 2023, argues for a framework aligned with contemporary technological capabilities to understand how human teachers and machines can team up and design safe, responsible and ethical learning opportunities.

iSTAR: an ecosystem of human–machine synergy

iSTAR stands for “Intelligent human-machine Synergy in collaborative teaching: utilizing the digital Twins, Avatars/Agents and Robots”. It treats collaboration as an ecosystem extending beyond a teacher working with a single machine. The framework places human teachers within collaborative relationships involving digital twins, avatars or agents, and physical robots.

The five DELTA dimensions

DELTA identifies five dimensions to consider when designing learning opportunities:

• Design
• Ethics
• Learning
• Teaching
• Assessment

Design considerations

The original DELTA figure identifies four design considerations:

• Data source, type and structure.
• AI technique, model and algorithm.
• Shape and appearance of robotic machines.
• Collaborative intelligence in education.

Ethical considerations

Its four ethical considerations emphasise responsible collaboration and inclusion:

• Responsible human–machine collaboration.
• Diversity, inclusion and fairness.
• Trustworthy relationships between AI and humans.
• Adequate AI literacy education.

Learning, teaching and assessment

The figure connects learning, teaching and assessment through two considerations:

• The role of teachers and learning scenarios.
• Assessment of learning and human–machine synergy.

Special education and inclusive design

In its ethical discussion, the paper argues that assistive technologies should improve the quality of life of learners with disabilities, and that diversity, inclusion and fairness should inform AI development and use. Representative data, bias and trustworthy relationships are also considered, offering a research perspective on technology design for special and inclusive education.

Publication dates

The publisher’s page is dated 17 October 2023, while the PDF front page states that it became available online on 18 October. The original site archive is dated 19 October and remains unchanged. These source records are distinguished here. This page introduces the abstract and frameworks; the full article is available from the journal.

Reference and licence

Reference: Ronghuai Huang; Ahmed Tlili; Lin Xu; Ying Chen; Lanqin Zheng; Ahmed Hosny Saleh Metwally; Ting Da; TingWen Chang; Huanhuan Wang; Jon Mason; Christian M. Stracke; Demetrios Sampson; Curtis J. Bonk. Educational futures of intelligent synergies between humans, digital twins, avatars, and robots – the iSTAR framework. Journal of Applied Learning & Teaching, 6(2), 28–43 (2023). DOI: 10.37074/jalt.2023.6.2.33. The paper declares a Creative Commons Attribution (CC BY) licence.

Further reading

Related research: educational applicability of generative AI; background: UNESCO guidance on generative AI; more resources: special and inclusive education resources.