Future Practices
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.
World 1 Practice 7: Global Reach
- The University has allocated a large portion of its resources to online learning, with data-driven approaches to determining student pathways and student support assisted by proprietary AIs.
- In this way it draws very large numbers of international students into its community despite restrictions on immigration. Most of these students study STEM disciplines.
- Its global reach and technological capability support multiple strong collaborations with industry, a capability that becomes a core part of the University’s brand and USP.
World 4 Practice 1: Teaching in the gig economy
- Academics operate on a freelance basis and work across universities
Student-consumers hold the power in the learning relationship, and choose to contract in lecturers to help them work through blocks of content. They select teachers based on cost, reputation, and expertise. - Higher education is driven by student demand rather than university supply.
World 1 Practice 6: Personalisation
- Students are able to personalise learning content via more flexible curricular pathways. Expert recommender systems based on their personal profiles assist them in this, alongside intelligent artificial agents.
- Expert human academic input into this process is available to higher paying students.
World 4 Practice 2: Individual flexibility over community
- As the University structures its offering to enable individual flexibility for students and academics, co-present communities of scholarship become rare.
- With the return of the itinerant academic, global access and individual mobility become the defining feature of higher education.
World 2 Practice 1: The Un-Biased Machine
- Trust in the capacity of humans to make good decisions is reduced as they are increasingly seen as unreliable and open to corruption: automation and data-driven decision making are seen as more objective and unbiased.
- All research and teaching becomes highly dependent on the new field of ‘compassion analytics’.
Humans feel liberated, not oppressed, by advances in machine intelligence.
World 4 Practice 3: Unbundling
- The university is disaggregated into its component parts, with education split into small blocks. In some cases, these blocks are made up of learning content, and in others they are assessment-only courses used to evidence and accredit knowledge. Learners can choose how they assemble these blocks.
- Many universities market themselves on the basis of the quality of their proprietary algorithms which help students assemble viable learning pathways.
World 2 Practice 2: Bespoke Learning at Scale
- Bespoke learning experiences are curated by intelligent agents, combatting the risk of siloed thinking by matching people who will challenge each other, and partnering students with appropriate supervisors and programmes.
- At-scale teaching across the globe is enabled by an international teaching commons working in partnership with compassionate machine intelligences designed according to internationally-agreed ethical standards.
World 4 Practice 4: Academics on Demand
- The cost of education blocks varies widely, as does the time of the academics who support them.
- The pay of academics is generally determined by individuals on the basis of demand and reputation, though there are some federated agreements for key price points between universities. This system has allowed some superstar academics to rise to the top while others who have not built reputation or numbers of followers struggle to make a living.
World 2 Practice 3: Research through Action
- Almost all research and education is directed towards solving global crises, with adverse effects on disciplines where knowledge is not readily ‘applied’.
- Education has become to a large extent practice-based with all students actively involved in researching and designing solutions to global challenges.
- Time-intensive academic traditions such as publishing and peer review decline as research impact converges with openly-accessible outputs in multiple forms, algorithmically ranked for quality.
World 4 Practice 5: Age no object
- Lifelong education brings new kinds of diversity to the university. Higher education is no longer perceived as being for the young, and all ages and life-stages are educated together.
World 2 Practice 4: Guilds
- Communities of research and teaching are formed across federated university networks, defined by common missions.
World 4 Practice 6: Transparent pathways
- The algorithms that are used to mediate and verify learning pathways are open, transparent and editable. This helps teachers and learners understand how curricula are formed and how they can best be supported.
World 2 Practice 5: Experience over Accreditation
- Students are not routinely assessed: achievement and credit frameworks are tied to practical impact evaluated via impact metrics and portfolios informally assessed by students’ personal academic networks.
World 4 Practice 7: Prestige through teaching
- The university is no longer measured by its academic research: this is all now industry-funded and led, or conducted by networks of individual academics funded by wealthy charities and trusts.
- Universities no longer generate new knowledge, but build their reputations based on teaching.
World 2 Practice 6: Global and Open
- All academic resources, data and code is open, accessible and shared. Proprietary knowledge is not trusted: globally, the cultural imperative is toward open.
- With ‘sharing’ the new norm, diverse and constantly evolving educational material is available, presenting challenges to quality standards and stable disciplinary knowledge.
World 2 Practice 7: A New Diversity
- Local, economic diversity is embraced as international mobility for students and staff is tightly regulated in the interest of sustainability.
World 3 Practice 1: The Post-Work World
- With automation reducing much of the time humans commit to work, education has expanded to become a life project focused on creativity, self-development and understanding what it means to be human.
World 3 Practice 2: What it means to be human
- While machines keep the world going, there is greater emphasis in education on maintaining the human capacity to manage the boundaries between themselves and machines. The interests of human wellbeing are prioritised.
- Teaching has an increased focus on human interpretation of data, the ethical governance of AI, and how advanced, intelligent technologies work with humans to make sense of the world.
- Higher education is focused on collaborative and creative responses to challenges and questions raised by blurred human-machine boundaries.
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