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.
There are currently 28 cards
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.