EUA Council for Doctoral Education

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Doctoral education for the future — drivers and needs for development

28 August 2019 | Maija Urponen, University of Helsinki

The political, cultural and economic environment of universities is changing at an accelerated pace. These dynamics place new demands on higher education. Maija Urponen considers that the rapid technological and environmental changes, digital transformation, and changing structures of work have consequences for the skills needed for successful doctoral careers both inside and outside academia. Specifically, the challenges posed by AI for knowledge production, the adoption of the UN’s SDGs, and the reorganisation of research work will require upskilling and reskilling.

The political, cultural and economic environments of universities are changing at an accelerated pace. These dynamics are transforming the research landscape, and they place new demands on higher education.

Out of the megatrends currently affecting the operational context of universities, three seem to be expressly pertinent to doctoral training. The fast-paced technological and environmental changes, digital transformation of knowledge, and changing structures of work have consequences for doctoral careers outside academia, but they may affect researcher careers within universities as well. Most notably, they require skills that are now being included in Bachelor’s and Master’s degrees, and from doctoral candidates they require upskilling and reskilling during the course of their doctoral studies.

Some of the skills are truly new. However, other skills are not new as such, but the changing context of application alters their connotation and gives them new weight.

In the era of the internet, artificial intelligence (AI) and big data, the amount of information is increasing exponentially. Ownership of data and information is increasingly concentrated in large multinational data giants that collect information from their users and sell it forward for profit. The university's role as the supreme authority of knowledge is no longer self-evident.

At the same time, information and knowledge are becoming more and more fragmented. The individual person can now only master an ever smaller portion of the existing mass of information, and knowledge is dispersed across different knowledge communities with varying concepts of knowledge. Yet, in a knowledge-intensive society, the complexity of phenomena is growing, and high-level knowledge and knowledge professionals are needed to solve the wicked problems of humankind. The fragmentation of information and knowledge means that solutions provided by lone individuals or even single knowledge communities are rarely sufficient.

In the era of Google and Twitter, the quality of and quality assurance for knowledge are rising in importance. Factual knowledge remains important, but what really stand out are the command of the scientific method and the ability to interpret and critically evaluate the foundations of knowledge. Objective, open and ethically sustainable knowledge production is in great demand.

At the same time, the fragmentation of information and knowledge and the ability to provide research-based solutions to wicked problems require effective communication between different knowledge communities across multiple platforms – within academia, between academic and non-academic sectors, as well as with society at large. Citizen science, for example, rests on a command of participatory research methods and dialogue. Intervening in phenomena such as vaccine hesitancy, for instance, demands a thorough understanding of the sociocultural roots of our convictions and their relation to our identity, and the stakes that are involved in adjusting them. 

With digitalisation, automatisation and AI, the demand in the labour market shifts from more general, repetitive tasks towards contextual problem solving. However, AI is a game-changer that has the potential to revolutionise the way we work towards solving complex problems. It is already changing the scientific process and it is fast becoming a crucial element of innovation. Researchers and knowledge professionals will need to understand at least the basic principles behind AI and make use of it in their work.

What is more, AI itself cannot be seen only as a technical skill. AI applications have wide-ranging implications for issues concerning ethics, privacy, digital security and safety, as well as transparency and equality. For example, professionals in any field should not be naïve about how algorithmic bias embeds discrimination into AI-based decision-making.

Another game-changer are the social and global challenges that underpin the UN Agenda 2030 and its Sustainable Development Goals (SDGs). Universities have a decisive role in bringing into focus ways to achieve SDGs, but they will be increasingly hard to ignore by any organisation whether private or public. Climate and environmental change and sustainability by themselves are creating a whole new field of professional expertise, but as with AI, an elementary understanding of these issues, related risk management and knowledge of the legislative and policy framework, for example, is fast becoming a commonplace requirement for any knowledge professional. 

More importantly, however, solving the grand challenges to achieve the goals for sustainable development in the wider sense – including ending poverty, reducing inequality and improving health and education – requires scientific knowledge. But this scientific knowledge needs to be augmented by a heightened ability to turn complex problems into practical solutions in a concerted effort, both multidisciplinary and multi-professional, across different sectors in society.

It is predicted that the boundary between research and non-research jobs will become more permeable. Technological change and the demand for solutions to the grand challenges of global sustainability will result in new types of professional positions, inside and outside academia. Careers of researchers are likely to become more hybrid, comprising parallel and multiple career pathways and requiring lifelong learning.

Employment relationships in general are becoming increasingly unstable. New flexible or non-standard forms of employment are becoming more common. This is bound to have consequences for the organisation of research work and knowledge production as well, and people with doctoral training will increasingly be working as freelancers, micro-entrepreneurs and contractors. It is not unimaginable that the platform economy as a business model will be making its way into the transactions concerning research work.

These structural changes place new demands on individual skills, including entrepreneurial skills. Deep understanding of impact and how to produce it from research results, knowledge of commercialisation, pitching skills, and a basic grasp of how business works, are some of the necessary tools for career mobility in a shifting job market. It’s worth noting that the need for such tools is not restricted to non-academic jobs; they are becoming increasingly relevant for researchers working within academia.

The kind of upgrading of skills that we are talking about here is at the very core of the calls for life-long learning. It may not be reasonable to add all of this to the already heavy work-load of doctoral candidates. However, universities need to come up with means of raising their doctoral candidates’ awareness of the changing career landscape and providing them training in the appropriate skills.