Artificial Intelligence in Education as a Rawlsian Massively Multiplayer Game: A thought experiment on AI ethics

Benjamin Ultan Cowley, Darryl Charles, Gerit Pfuhl, Anna-Mari Rusanen

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


In this chapter, we reflect on the deployment of Artificial Intelligence (AI) as a pedagogical and educational instrument, and the challenges that arise to ensure transparency and fairness to staff and students . We describe a thought experiment: ``simulation of AI in education as a massively multiplayer social online game'' (AIEd-MMOG). Here, all actors (humans, institutions, AI agents and algorithms) are required to conform to the definition of a player. Models of player behaviour that `understand' the game space provide an application-programming interface for typical algorithms, e.g. deep learning neural nets or reinforcement learning agents, to interact with humans and the game space. The definition of `player' is a role designed to maximise protection and benefit for human players during interaction with AI. The concept of benefit maximisation is formally defined as a Rawlsian justice game, played within the AIEd-MMOG to facilitate transparency and trust of the algorithms involved, without requiring algorithm-specific technical solutions to, e.g. `peek inside the black box'. Our thought experiment for an AIEd-MMOG simulation suggests solutions for the well-known challenges of explainable AI and distributive justice.
Original languageEnglish
Title of host publicationAI in Learning – Designing the future
PublisherSpringer Nature
Number of pages19
ISBN (Electronic)9783031096891
ISBN (Print)978-3-031-09686-0
Publication statusPublished (in print/issue) - 6 Nov 2022


  • artificial intelligence
  • Learning Assistant
  • Learning Analytics
  • Massively-multiplayer Game
  • Thought experiment


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