Universities & Emerging Technologies | July 2019 Issue

Universities & Emerging Technologies examines how institutions can make considered choices about blockchain, extended reality, artificial intelligence, data analytics, and the Internet of Things. Its enduring message is that emerging technologies in higher education create value when they address a defined need, support meaningful learning, and connect academic expertise with industry and society. The result is a practical lens on adoption, talent development, and responsible innovation.

Why emerging technologies in higher education demand strategic judgement

Universities face a double responsibility. They must decide which technologies can genuinely improve education and institutional operations, while also helping to research, shape, and govern those technologies. That makes technological change more than a procurement question. It affects curriculum, academic practice, digital infrastructure, identity, privacy, employability, and the university’s place within a wider innovation ecosystem.

The strongest starting point is the problem an institution needs to solve. Evidence on higher education technology priorities shows why this discipline matters: immediate pressures such as information security, student success, data-informed decisions, and operational complexity can outweigh the appeal of highly publicised tools. A credible strategy therefore asks how a technology fits institutional priorities, who benefits, what capabilities are required, and how success will be evaluated.

Four conversations shaping responsible adoption

Start with educational purpose, rather than technological novelty

Extended reality offers a useful illustration of purpose-led adoption. In anatomy education, three-dimensional visualisation, augmented reality, and virtual reality can expand access to scarce physical resources, support spatial understanding, and allow self-paced exploration. Their value becomes clearer when these tools complement disciplinary expertise, laboratory practice, and well-designed assessment.

The same principle appears in music production. An augmented reality application placed guidance directly over a recording console and patch bay, helping students revisit complex processes during independent practice. User feedback shaped the design, and early evaluation exposed technical limits in image tracking. This is a valuable pattern for universities: begin with a specific learning barrier, involve users, pilot in context, and refine the intervention through evidence.

Build authentic learning around real practice

Technology-rich education is most convincing when students create, test, and collaborate in conditions that resemble professional practice. A studio-based animation programme shows how industry experts, live production workflows, intensive coaching, and technologies such as real-time engines, virtual reality, and machine learning can be integrated into the learning environment. Students gain technical fluency alongside communication, teamwork, and judgement.

Immersive projects in the liberal arts extend this logic. Interdisciplinary teams can combine historical research, storytelling, software development, and three-dimensional design to explore difficult cultural questions. Such work strengthens digital capability without displacing the interpretive and ethical strengths of the humanities. It also reveals that future-ready learning depends on curriculum design, staff support, suitable funding, and room for experimentation.

Treat trust, identity, and ethics as design requirements

Digital credentials demonstrate both the promise and complexity of emerging technology. Verifiable records can give learners more control over their achievements, make qualifications easier to share across borders, and reduce slow or fraudulent verification. Yet their usefulness depends on interoperability, privacy, accessibility, issuer credibility, and the ability to manage revocation or expiry. The underlying question is how trust should work across institutions, platforms, employers, and learners.

Artificial intelligence raises a related challenge. Algorithms can support decisions and reveal patterns in operational data, but responsibility becomes harder to locate when automated systems influence access to finance, healthcare, education, or employment. Ethical advice cannot function as a final seal of approval. Universities and partners need continuing scrutiny of data, assumptions, ownership, explainability, and accountability as systems and social expectations evolve.

Make universities active partners in technology ecosystems

Universities contribute more than skilled graduates. They provide independent inquiry, specialist facilities, long-term research, and environments where theoretical and applied knowledge can meet. Joint laboratories, doctoral projects, internships, seminars, and fellowships can connect academic researchers with industry engineers while developing the next generation of talent. The most productive collaborations create shared agendas and reciprocal learning, rather than treating the university as a supplier.

Large-scale Internet of Things pilots show the same ecosystem principle at regional level. Universities and research organisations help test connected technologies in real settings, from mobility and agriculture to large public events. These pilots make interoperability, security, privacy, user needs, and replication visible early. They also turn research sites into learning environments where technical performance and public value can be assessed together.

Productive tensions leaders need to manage

Emerging technologies create choices that resist simple answers. Institutions must balance rapid experimentation with robust evidence, local needs with open standards, and personalised services with privacy. They also need to connect short-term operational gains with long-term educational purpose. A pilot may demonstrate technical feasibility while leaving questions about accessibility, staff workload, maintenance, procurement, or governance unresolved.

Another tension sits between adopting technology and helping to develop it. Universities can purchase a finished product, build internal capability, collaborate with a company, or participate in a wider research consortium. Each route distributes knowledge, risk, intellectual property, and decision-making differently. Leaders should make those trade-offs explicit and design partnerships around mutual benefit, transparent responsibility, and the capacity to learn from failure.

Practical priorities for university and industry leaders

• Define the challenge first. Express the educational, research, or operational need in terms that users recognise before selecting a platform or tool.
• Design with the people affected. Involve students, educators, professional staff, partners, and communities in shaping use cases, testing prototypes, and identifying barriers.
• Invest in complementary capability. Technology delivers little without data literacy, disciplinary knowledge, ethical judgement, technical support, and confident teaching practice.
• Pilot in authentic settings. Test solutions through real courses, studios, laboratories, workplaces, or public environments, and evaluate learning, usability, inclusion, and sustainability together.
• Build governance into the work. Establish responsibility for data, privacy, security, accessibility, procurement, intellectual property, and algorithmic decisions from the outset.
• Share what can travel. Open standards, reusable learning resources, and transparent findings help successful approaches move across institutions and contexts.

Details

Title: University Industry Interaction Magazine – Universities & Emerging Technologies (July 2019 Issue)
Year: 2019
Pages: 60
Language: English

Frequently asked questions

What are emerging technologies in higher education?

Emerging technologies in higher education include developing tools and systems whose educational and institutional uses are still evolving. Examples include artificial intelligence, extended reality, blockchain-based credentials, data analytics, and the Internet of Things. Their relevance depends on the problem being addressed, the maturity of the technology, and the capabilities available to implement it responsibly.

How can universities evaluate a new technology?

Universities can begin with a clearly defined user need, then assess pedagogical fit, evidence, accessibility, security, privacy, interoperability, cost, staff workload, and long-term maintenance. Small pilots in authentic settings help reveal benefits and limitations. Evaluation should combine technical performance with learning outcomes, user experience, inclusion, and alignment with institutional priorities.

How does extended reality support university learning?

Extended reality can help students explore spatial, practical, or historically situated subjects through interactive three-dimensional experiences. It can provide additional opportunities to rehearse complex processes, visualise inaccessible material, and learn independently. Effective use still requires disciplinary expertise, inclusive design, reliable technical support, and integration with wider teaching and assessment.

What role should universities play in artificial intelligence ethics?

Universities can contribute independent research, interdisciplinary scrutiny, professional education, and public debate. They can help partners examine data quality, bias, explainability, accountability, and the distribution of benefits and risks. Because artificial intelligence systems and social expectations change over time, ethical governance should be continuous and connected to real decisions throughout design and deployment.

Why are university–industry partnerships important for emerging technology?

University–industry partnerships connect long-term research and specialist talent with practical problems, engineering expertise, and routes to application. Joint laboratories, doctoral projects, internships, and real-world pilots can accelerate learning for both sides. Strong partnerships use shared objectives, clear responsibilities, reciprocal knowledge exchange, and transparent arrangements for data, intellectual property, and public value.