Digital Futures: The Academic Staff Experience in 2020, 2025 and 2030

In 2020

• Social technologies are embedded as a global means of connecting niché specialties and academics around the world. Such systems begin to better connect and integrate, allowing for truly connected communities instead of isolated "islands" existing in different platforms.
• Student expectations and advances in classroom technologies drive the need for evolving use of technology in teaching and learning, with academic staff investing time in skills development.
• Research information systems, social technologies, and other administrative platforms that manage the various facets of academic information (such as publication and funding records) become better integrated, beginning to reduce the administrative burden on academic staff of updating the same or similar information in multiple locations.

In 2025

• Big data is used to inform future research interests and needs, as well as to feed data for research projects. Artificial intelligence combined with increased computing power significantly speeds up the ability to process, manipulate, interpret and present data in different ways.
• Virtual reality, artificial intelligence and advances in robotics provide reliable mechanisms for simulating experiments, reducing the need for expensive equipment, removing human error factors, and speeding up the pace of developments.
• The classroom is ever-more virtual, with students participating in modules from around the world in different institutions. Advances in natural language processing and translation technologies remove language barriers from participation and feedback.

In 2030

• As digital networks and the modularity of higher education has evolved, so too the importance of a specific university employer has lessened. Academics lead a more portfolio-career approach, affiliated with multiple universities and related institutions, with digital technologies co-ordinating the administrative complexity of such an approach.
• Big data is used to understand and assess performance metrics for academic outputs, in both teaching and research. Systems are in place to provide early-warning signals to academic staff when impact metrics are likely to be compromised and interventions are required. Such data is used to inform funding decisions, to identify potential global collaborators, and to "match" students to the best suited tutors for them and vice versa.
• Common global taxonomies in global higher education have developed, opening access to information, and advancing the way that information connects, is shared, is sourced or is archived.