Future Universities

Drivers

• Datafication
• Marketisation
• Tight borders
• Increased competition

In this world….

Accelerated datafication of everyday life and the normalisation of ubiquitous surveillance makes quantification, measurability and trackability the key markers of value. Data-driven decision making across all sectors positions STEM and data science at the top of the disciplinary hierarchy.

Higher education shifts toward a focus on provision at the point of need, with timely routes to accreditation in particular skills areas taking priority over extended periods of study within co-located communities of scholarship. The sector becomes diversified with online education, unbundled curricula, competency-based programmes, micro-learning and ‘stackable degrees’ – often offered by private universities, for-profit platforms and industry bodies – argued to bring increased affordability and accessibility.

Tighter borders and immigration controls in the UK, rising demand for higher education, high costs of living in elite university cities and increased competition for international students put pressure on universities to offer new ways of extending global reach, often through digital means. A divide has emerged between high-paying students who are largely based on-campus, and students on ‘tutor-light’ pathways delivered primarily to distance learners.

This future, this university

• Global
• Online
• Marketised
• Data-dependent
• Flexible
• Divided
• Compliant

This future expanded

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.

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.

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.

Measuring experience

• 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.

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.

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.

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.

Drivers

• Climate change
• Data-driven decision making
• Compulsory renewability
• Compassion and global justice

In this world…

The intensified effects of climate change result in mass movements of populations and increasing food and water insecurities, prompting global demand for action from governments, industry and society. Global crisis has shifted collective mindsets, with a strong emphasis across all areas of human activity on responsible and sustainable action. The goal of economic growth disappears as a key driver with all activity instead measured according to an ‘eco bottom line’ making sustainability and renewability the new indicators of human advancement.

Data analytics for compassion are funded globally to better understand and manage issues around environmental impact, equity and sustainability. Datafication and data-driven decision making become core to progressive reform, while citizenship practices are increasingly enabled through the data-driven activities of governance and social care. National borders and sovereignty become less important than global justice and equity.

Education and research become focused almost entirely on addressing global crises, with teaching in universities increasingly designed around action and practical solutions to ‘real world’ problems. Federations of global, elite universities drive research agendas each with tightly-defined niche areas of expertise, while teaching is conducted by networks of local universities designed to minimise the need for travel. Advanced technologies for telepresence lessen the need for international mobility.

This future, this university

• Sustainable
• Compassionate
• Algorithmically-determined
• Unbiased
• Global
• Sharing
• Practice-oriented
• Impact-obsessed

World 2 Future for Edinburgh

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.

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.

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.

Guilds

• Communities of research and teaching are formed across federated university networks, defined by common missions.

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.

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.

A new diversity

• Local, economic diversity is embraced as international mobility for students and staff is tightly regulated in the interest of sustainability.

Drivers

• Automation
• Human-machine hybridity
• Personal missions
• Leisure

In this world…

Automation has replaced much human work, resulting in growing demand for education focused on personal creativity, criticality and problem solving. Relations between humans and automated agents have become defined by co-dependence, with effortless access to the world’s information and relative freedom from work celebrated alongside a new valuing of the social and creative capacities of humans.

Easy access to information, and the automated synthesis of large, complex bodies of knowledge, have created a shift in education away from fixed curricula toward ‘experience’, with the most successful universities offering rich, time-intensive, student-led pathways extendable over the entire life course. Depth of scholarship and learning built and applied through life become more important than accreditation, qualifications and employability. Assessment is minimal as students are motivated primarily by developing their personal  capacity to fulfil missions and address intractable problems in loose collaboration with intense, digitally-maintained personal networks.

Discipline boundaries have largely disappeared as STEM and social science converges with the creative arts and humanities. Teaching is conducted for the most part by highly-effective, empathic automated agents, with access to human ‘navigators’ a premium model offered only by the most expensive universities.

This future, this university

• Time rich
• Posthuman
• Experience driven
• Lifelong provision
• Personal missions
• Challenged by ennui
• Postdisciplinary
• Non-hierarchical
• Troubled

World 3 Future for Edinburgh

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.

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.

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.

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.

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.

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.

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.