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Future Practices

These future practices were crafted based largely in the representative activities emerging from the design workshops, which were largely value-led responses to the future worlds in which they were expressed.

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World 1 Practice 1: Teaching Machines
  • Interpretation of learner analytics becomes the core skill for teaching and support staff, as student data-profiling becomes highly advanced. Multiple student data-streams include facial and emotion recognition in class, linked public and health data and in some instances data drawn directly from new brain-computer interface technologies.
  • Generic, online learning support at point of need replaces subject expert-based pedagogies for all but the highest-paying students.
World 3 Practice 3: Teaching and learning converge
  • Students and mentors create learning experiences together: mentors are not necessarily human.
  • Academic hierarchies reduce as mentors are seen as peers and roles are continuously exchanged. Humans and agents teach and learn together.
World 1 Practice 2: Pay-as-you-go
  • The university has created several flexible pricing models to remain internationally competitive. Students can pay for differing levels of support, and are charged on a per-course, pay-as-you-go basis.
  • Self-driven learning is the cheapest option: the more mentor time students require, the more they pay.
World 3 Practice 4: Competency and Expertise
  • Competency in a field is no longer associated with the accumulation of knowledge, since machines now manage this much more efficiently. Instead, it is evidenced by the ability to synthesise, theorise and apply knowledge through experience and research-focused courses and portfolios.
  • Matchmaking algorithms help build collectives of shared interest by bringing together people with different types of expertise.
  • Meanwhile, bespoke ‘cocktail-style’ learning paths are argued by some to threaten the existence of expertise and specialisms altogether.
World 1 Practice 3: Accreditation Over a Lifetime
  • It is common for people to be in education across all stages of life, building portfolios of micro-accreditation over the lifecourse.
  • The University offers credit for its own courses, but also assigns credit earned from a wide range of other providers, including industry and for-profit platforms.
World 3 Practice 5: Learning through failure
  • There is no more assessment in its traditional form. Credit is given to those who complete content and reflect on what they have done.
  • Without formal assessment milestones, and with learning no longer time-contained by traditional programmes, confidence grows and failure is recognised as an opportunity to learn from experimentation, trial and error.
World 1 Practice 4: Measuring Experiences
  • While the University aims to enable experience-rich learning, particularly for the highest-paying students, it is under pressure to account for the quality of these experiences.
  • Compliance data requirements from government drive acceleration of the general culture of datafication and quantification.
World 3 Practice 6: Transparency
  • With data and code keeping society running, transparency becomes re-defined as the human capacity to understand how artificial agents are built and put to work.
  • Humans are educated accordingly, but within a recognition that the complexity of intelligent systems is beyond the capacity of individual or collective humanity to fully understand or control.
World 1 Practice 5: Programmed Diversity
  • Diversity is structured into the student experience, as smart algorithms ‘intelligently’ build cohorts, intentionally mixing up students from different economic, cultural and national backgrounds.
World 3 Practice 7: Off-grid counter culture
  • Human-machine interdependence drives a backlash among some groups resistant to hybridity: strong off-grid countercultures emerge.
  • Some universities define themselves as ‘human only’ spaces from which a platform for critique of the dominant collective mindset is made possible.