Building the future intelligent campus

Using data to make smarter use of your university estate.



Many colleges and universities are working on ways to improve their students’ experience, business efficiencies and environmental performance by better utilising data. This data can be directly related to learning and part of the overall campus experience. 

The COVID-19 crisis has brought much of this development into sharp focus and, in some cases, accelerated.

The current landscape  

UK universities and colleges with sophisticated management of data and analytics are enjoying better use of resources and facilities, delivering more personalised services to students (often in real time) and enhancing life on campus. 

The use of sensors and data collection is becoming commonplace, as are university mobile apps as a way for students to access services.  

The types of use typically include buildings and energy management, management of student learning, navigating the campus and access to services and facilities. Increasingly, health, wellbeing and safety also feature in mobile apps, together with opportunities for events and activities. The COVID-19 pandemic led to a shift in behaviour with a rapid increase in online access to services, contactless transactions and payments, remote learning and meetings. Aspects of these are likely to remain as long-term changes, along with a renewed awareness of health and hygiene focused on no-touch facilities, air quality and flow and density of people traffic, including one-way routing and use of outdoor spaces.  

Many organisations are also integrating more closely with their local area on ‘smart city’ initiatives, often including transport services, energy, health and the environment. From finding the next bus to saving water in toilets, communities are embracing the combination of a sustainability agenda, the need for cost and resource efficient solutions, and demand for online and mobile personalised services. 

Exploiting existing digital systems and tools 

Many existing technologies are already being harnessed to improve the student experience. For example:


Airports often use wayfinding to help passengers reach their gate in time. There are educational versions of wayfinding that help students get to the right room at the right time and find an available PC.

Accessible routes

Tools such as Google Maps give students a better experience as they navigate their campuses when learning and socialising and, increasingly, can adapt to individual need. Google Maps now includes basic routing for wheelchair users, for example. The availability of information and services at the touch of a button can make what could be a daunting experience easier and friendlier.  


As wifi becomes ubiquitous across university and college campuses, organisational IT teams are using wireless technologies to track devices and find out which areas of the campus are busy, troublesome for wireless devices, or have too many devices for the network routers to cope with.  

Space usage

Space within educational institutions is always at a premium. Having a clear understanding of how space is used will not only enable more efficient use of existing spaces but also determine which spaces are fit for purpose, healthy environments. Many estates departments are fitting sensors measuring CO2 levels, movement, temperature and light levels and using the resulting data to make changes to those spaces; some use smart technologies to do this automatically. 


Some universities and colleges use chatbots to facilitate student support and general queries, even though integrating a chatbot interface with university and college systems is a real challenge.  

Facial recognition

Experimental use of facial recognition for attendance monitoring, as well as early work on emotional recognition for engagement, has demonstrated the potential of such technologies. There have been ethical concerns, though, over the algorithms and whether they really do tell us a true picture. 

Innovative approaches 

Across the UK, universities and colleges are researching, developing, designing, and delivering their own tools and services in the intelligent campus space. Some of these are narrowly focused and closed systems. Combining data from these various sources could offer new insights and opportunities. 

The sector has a need for effective systems that enable data to be gathered from physical places and from systems that record and monitor space and equipment use, timetabling and other activities. We know that making timely interventions will enable students to learn more effectively and in a personalised, adaptive manner, and ensure that the organisation is running efficiently.

Leda's day: travelling to campus

It was raining, and Leda was off to her university for the day. Her phone had already sent her notification to leave for campus early as there was a lot of traffic and the buses were delayed. She got to the bus stop earlier than usual and within a few minutes the bus arrived.  On the bus, on her phone using the university app, she looked over her schedule for the day. There were lectures, a seminar and she also had time to get to the library to find books for the essay she needed to hand in next month. There were some notifications: the seminar room had been changed and there was a high chance the library would be busy. Leda looked out of the window at the rain. With her day planned effectively she had time to catch up with friends over coffee later. Today was going to be a good day.

What is an intelligent campus?

The term ”intelligent campus” is becoming more popular, but what does it mean? How can a campus be intelligent, and is it achievable or desirable?


The field of artificial intelligence (AI) aims to study the extent to which machines and computers can be developed with aspects of intelligence. As general intelligence is a rather complex concept to tie down, AI helpfully breaks it into a series of central problems (or goals) – including reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.

In understanding what an intelligent campus is - or could be - these AI topics are a useful starting point to describe, evaluate or even design 'intelligent' actions or devices.

Perception and action

Consider the use of sensors for measuring changes such as temperature or motion and more complex devices such as cameras or GPS-enabled equipment.

Data from sensors could be combined with knowledge about the environment and used to make logical deductions. In order to use this data more effectively, we could set goals and take action to adapt the environment, in turn using this learning to help devices improve.

This cycle of perception, reasoning and action is a simplified version of what humans do and AI attempts to replicate this, or parts of it, with computers and machines.

Several things are central to this discussion, including data, devices and connectivity. Data collected by sensors can be more valuable when devices are connected and the data is processed effectively.

The Internet of Things (IoT)

Current everyday devices like mobile phones can collect data on various aspects of activity, including location. Mobile phones also have connectivity through the telecommunications network and wifi to the internet.  

So do many other common devices, from webcams and printers to central heating systems and baby monitors. Out on the street we can see connected vehicles, ticket machines and lighting, as well as engine maintenance and healthcare tools in industry and public services.

An interesting extension to this is the concept of wearable devices, for example used for health monitoring or fitness applications.

This has become known as the Internet of Things – a wide variety of devices connected to the internet with the ability to collect and transmit data. IoT provides the potential to integrate all manner of data and use it in aspects of the intelligence concept, for example reasoning or adaptation of the environment.  

Data and analytics  

Data is all around us and it’s the subject of much topical debate, including work on open data, big data and analytics, not to mention ethical issues including privacy and security.  

In some ways the physical collection of data is easy. It is when we try to interpret and make sense of it that we hit many of the challenges. 'Analytics' has become a common term, referring to the identification of patterns and interpretation of data. Its sophistication varies from presenting and describing data right through to developing insights and making predictions.  

Notifications and alerts are known as 'push' data, meaning they are initiated from the system or source rather than requested. 'Pushing' data to an individual or group of individuals may have limited value without understanding what the data is and what it might mean. Equally, it is possible to misinterpret data and reach conclusions that don’t represent the whole picture. 

Data analysis is critical in providing meaningful information and services.  

Becoming smart  

The concept of an intelligent campus hinges on several key points:

  • The availability of connected devices and sensors 
  • The ability to collect, store and process data (including combining it with other data) 
  • An understanding of what the data is and how it can be used  
  • A set of goals to benefit campus users  

The last of these is crucial in making this a useful topic to explore. It is also important to consider the combination of these aspects – for example, having high-quality network infrastructure is only part of the jigsaw.  

Sometimes the term 'smart' is used in relation to using smart devices on campus. In many ways this is synonymous with intelligent campus, although there are subtle differences in meaning between the two terms.

Some technological definitions, for example, refer to smart sensors that can collect and transmit data autonomously but lack the analysis and reasoning aspects of intelligence. Perhaps the important distinction here is the ability to see beyond the data and the technical capability to understand the purpose and benefits fully. The combination of different datasets adds another dimension to the perception of intelligence, where cross-referencing between datasets can lead to reasoned conclusions. 

As an example, data from weather sensors could combine with building occupancy data and historical patterns of room temperature to predict the necessary level of heating.  

Leda's day: arriving on campus

The bus arrived at the campus and Leda got off. She checked her app and started to walk to her first lecture. As she passed one of the campus coffee shops she received a notification that three of her friends from the course were in there, so she checked that she had time and popped in. Her app let her know that she had enough loyalty points for a free coffee. “Well, why not?” Leda thought to herself, she could take the opportunity to check if there were any additional resources for the lectures today.

Why would we want an intelligent campus?

What is driving the agenda to incorporate some of these technical capabilities into the educational setting. Who benefits? 

Institutional drivers

If the campus is more responsive, able to react to environmental changes and to behaviour, what are the implications for universities and colleges?

Potential applications exist in several key areas:

  • Enriching the student experience – responding to student needs, providing timely and relevant personalised information, offering new learning and career opportunities
  • Supporting students’ health and welfare, helping them navigate student life
  • Mitigating the impact of crises including natural disasters, safety and security incidents and health crises such as epidemics
  • Creating new opportunities for research including cross-disciplinary areas, societal challenges and management of the research lifecycle
  • Reducing environmental impact – monitoring energy use and waste and adjusting energy to meet needs in real time
  • Improving the physical environment – making it more comfortable or conducive to learning
  • Maximising use of valuable resources – including rooms and equipment
  • Enhancing the campus estate and user experience of it – using space, finding your way, managing flow and congestion

Several factors may influence initiatives to make campuses more intelligent:

  • The economic context
  • Legal and ethical concerns – eg carbon zero goals
  • Institutional reputation, competition, educational principles and student expectations
  • The challenges of increasing amounts of data used in research and how to collect, organise, report on it or integrate it
  • The need to support flexible working and new ways of working
  • The ready availability of the technology

Ensuring that technology is used to support strategic aims (rather than as an end in itself) is important. The intelligent campus agenda can be complex ethically and practically. Approaching projects from a purely technical point of view without clear end user and/or organisational benefits is likely to lead to problems.

The concepts are also frequently explored in collaboration with the local area and services. Often, campuses are linked closely with their surrounding environment and communities and staff and students are significant users of local services, notably transport. Similar agendas are being pursued in other contexts, in particular the development of 'smart cities'.

Some universities are working with citywide developments that include transport, energy, health, urban informatics, collaboration with other sectors and the environment.

Specific benefits

Whether you are a student, teacher, researcher, manager or a provider of services to others, the intelligent campus offers the potential to improve effectiveness, optimise use of resources, deliver real-time personalised services and improve the quality of the campus environment.

But realising that potential is complex. Here are a few possible scenarios:

  • Student recruitment and retention – ensuring new students have the best possible experience when they arrive with timely, relevant information pushed to them to support them as they settle in
  • The learner environment, experience and voice – monitoring environmental conditions and feedback from students, creating an environment conducive to learning and health with personalised services and choice
  • Life on campus – helping navigation with easy access to facilities and support
  • Life as a student – linking data to enhance the academic and social experience, connecting with friends, engaging in activities, supporting health and wellbeing, interacting with the community and local services
  • Smart research – creating, structuring and publishing data for the research community, access to open datasets and living laboratories
  • Campus management and cost saving – particularly in efficient use of space and facilities, ensuring facilities are as fully available as possible and used appropriately, optimising energy and resources sustainably
  • Anytime, anywhere learning – using smartphones to provide flexible learning opportunities off-campus and contextual learning, delivering timely resources and progress updates

Campuses already have a wealth of connected devices, both user-owned and organisational, reliable and fast networks, established systems and experience in collecting and organising data. Intelligent campus projects aim to bring these existing systems and infrastructure together with new skills in data sciences and analytics to provide innovative applications that benefit campus users.

What are some of the concerns?

There are several main challenges to the effective implementation of intelligent campus projects. They centre around:

  • Understanding the potential of what is possible and how to take the first steps
  • Setting relevant goals – understanding what is useful and appropriate, achievable but innovative
  • The logistics of collecting and processing data – managing the huge volumes that can be generated
  • Interpreting large datasets to inform decisions – bias or misinterpretation in algorithms could lead to inappropriate responses
  • The complexity of combining datasets to draw accurate inferences and provide meaningful services 
  • Safety, security and privacy – such as the appropriateness of monitoring individuals’ locations and sharing data, the need for transparency and consent, ensuring data and devices are secure
  • Over-reliance on technology – lack of user skills, the need for network resilience, maintenance of devices and infrastructure  
  • The impact on people – including unexpected consequences of attempts to influence behaviour  
  • The need for joined-up thinking and action from different departments and services 

Learning analytics

We’ve identified data analysis as crucial to the intelligent campus. Learning analytics is the focus of other work using data about students to make informed decisions, particularly in the areas of student satisfaction, retention and attainment. 

Analytics can be used in other contexts, for example applying the same analysis of data to teaching processes to support the work of staff. This could be in improving administrative efficiency or supporting and enhancing teaching methods.  

Where intelligent campus fits with existing analytics work is in considering the integration of different types of data and more joined-up analysis and knowledge. For example, learner data and decisions combined with the wider context of the environment, community and services. This includes data on buildings and facilities, learning spaces and location data to create a more efficient campus delivering more effective personalised services, and potentially taking this further to improve teaching and curriculum design and support more personalised and adaptive learning.  

With more joined-up thinking and collaboration between learning and teaching, IT and estates the benefits to the individual and organisation are potentially much greater. They may also be more complex to deliver, needing cross-functional collaboration and innovative thinking.

Leda's day: reflection time

As Leda drank her coffee, she reflected on why she had chosen this university. One of the things that had attracted her was the positive reviews and feedback on the whole student experience. She was reminded, though, of one of the induction sessions where the university had discussed the concept of data gathering and processing, what interventions were possible and the importance of consent at all three stages. She worried about this and wondered if all appropriate mechanisms and security were in place to protect her personal data. As she finished her coffee, she did wonder whether all this data gathering was really necessary?

The learner and student perspective

What do learners want that an intelligent campus could help with?

Students care about several issues including the quality of teaching, feedback and, increasingly, value for money. The perception of teaching quality can be related to the number of contact hours, although students also recognise that independent learning is a crucial skill.

Learning and teaching have evolved, particularly through the experience of remote learning and virtual teaching during the COVID-19 pandemic. Expectations increasingly include some form of blended learning and students understand the importance of social learning.  

Students’ other concerns centre around anxiety about leaving home, learning in new ways, managing workloads, building new networks of friends and finding jobs.  

Social media and other communication technologies already play a part in connecting students and they support collaboration on social and academic topics with fellow students, staff and others. So what more can an intelligent campus do to help? 

A personalised learning experience 

The intelligent campus has potential to give each student more personalised real-time services and greater control over how and when they access them. It could make constructive suggestions and help people make informed choices. Even more significantly, it can integrate data from different contexts and tools in such a way that the educational environment can respond, perhaps in real time, to issues as they arise rather than waiting for those in difficulties to seek help. In particular, the following characteristics are ones that smart technologies could support: 

  • Moving around the physical environment and accessing facilities easily, including accessible routes, avoiding congested areas and being aware of temporary diversions and obstacles 
  • Making the physical environment more comfortable and healthy, providing real-time information on, and individual control over, environmental conditions 
  • Providing flexibility in learning  
  • Connecting with others socially and for academic collaboration 
  • Identifying and sharing events and activities, matching shared interests and providing coordination support such as transport or food options 
  • Providing real-time contextual information that improves decision-making, from healthier eating options to managing finances 
  • Raising issues as they arise and linking to support services, including wellbeing  

The intelligent campus has the potential to make life easier for the student, improve their academic progress, enhance their emotional wellbeing and make the environment more comfortable and attractive. 


Students have expectations about using technology and how and when their university or college provides services. These are partly based on their familiarity with tablets and smartphones and their experience of other organisations and tools that offer sophisticated services. These might be music or shopping services suggesting what they might like next, or knowing which of their friends is attending an event nearby. The COVID-19 pandemic also brought a societal shift towards more contactless and cashless transactions and payments, online bookings and virtual meetings and consultations. 

An intelligent campus offers more than existing applications on smart handheld devices can because it supports the collation of student body data, including potential segmentation by academic or social group, and integration of this with data about the physical environment and academic context.  

Sometimes institutions assume students don’t have concerns about technology, data-gathering and processing. These assumptions need to be tested and addressed, to ensure students are fully aware of the reasons for collection and comfortable with data processing and use. 


Questions students may ask include:

  • “Where is my next lesson?”
  • “What books would be useful for this topic?”
  • “When is my tutor free for a chat?”

While this data may already exist in timetabling apps, shared diaries or reading lists, it is typically not ‘live’. Perhaps the tutor isn’t in their office due to a delay, yet the student isn't aware they are unavailable. Or perhaps the tutor is working from a remote location but is still available for an online meeting.  

Equally, the seminar may be allocated a room in the timetabling process, but how appropriate is it for the specific learning activities? Perhaps this week there are presentations or a group exercise, or even an impromptu guest speaker looking for an interactive discussion, and the room booking assumes a standard classroom. The room next door may be better suited but fully booked, although not actually in use. If the class moves next door, how easy is it to notify students who are late? 

There is growing interest in flexible learning spaces and in blended learning, possibly with both remote and local students. The data collection for room allocation would need to be quite detailed to enable this, including the range of activities the room is suitable for, how the class size varies in relation to the activity, the precise facilities and how to arrange them.

Some questions you may seek to answer include:

  • Can relevant materials be signposted as new topics emerge during the session and automatically added to the references for the module, along with their location in the library?
  • Are there resources in different formats that match different learning style preferences?
  • Could references be differentiated by difficulty relating to an individual student’s understanding of the topics?  

Improving the social experience

Arriving at a new institution can be disorienting and confusing. Virtual campus tours and satnav-guided walks can help, but can these be integrated with personal timetables and interests? What if a personal digital assistant could tell you this is where your first lecture is, which societies are meeting today or what’s being served for lunch in the canteen?

Connecting with people, interests and activities in a more dynamic, responsive and personalised way could enable the student to integrate into the student community more easily, identify others with shared interests and highlight opportunities to meet and join in. 

Equally importantly, data about the student might suggest difficulties or anxieties and lead to suggestions about helpful services such as counselling - even making a live chat available instantly. 

Consider the wider context

Looking beyond the campus, how do students find out about areas to live, and how the facilities match their interests? Information on a wide variety of factors including transport links, air quality, crime rates and nearby student cafes could be made available to inform their decisions.  

Information on how to travel to and from campus, the time of the next bus and what the traffic is like might already be available online. However, what if we combine this with where good cycle lanes are, and where the spaces in the cycle racks are this morning? Another application could be demand-responsive transport, where a bus is dispatched only when several students express interest in catching it.

Intelligent campus initiatives are often closely linked with services in the wider community including transport and leisure, and other ‘smart city’ projects as described in a later part of this guide. And while the focus of this guide is on the campus context, there is much to learn from smart cities and the wider world, and integration with campus data can help smooth the boundaries between life on- and off-campus and the local community.

University campuses are often seen as a mini-city and used as a ‘living laboratory’ for research into smart city initiatives.  

Leda's day: getting to lectures

Leda’s phone buzzed; she needed to be at her lecture in ten minutes. However, the room was different to the one she was usually in. Leda didn’t worry as she knew the phone would direct her to the room quickly and efficiently. What was so great, Leda thought, was that the sessions she attended were always in the right kind of space. Sometimes her lecturer wanted to do group work and the usual lecture theatre wasn’t appropriate, so having a more suitable room allowed her to focus better on her learning.

Making the campus smart

What can the intelligent campus offer to its users?

For those managing the campus, the intelligent campus has much to offer all its users, including visitors and professional services staff. In fact, the latter are key to supporting and implementing some of the applications of the intelligent campus. Examples include:

  • Efficient use of energy and resources, managing waste and optimising recycling
  • Effective use of facilities, monitoring use and influencing it in off-peak times
  • Managing the movement and interaction of resources and people, identifying congestion, optimal routes and flow, adjusting routes to account for obstacles
  • Finding cost-effective ways to deliver campus services and facilities, eg minimising waste in the cafe via dynamic pricing and real-time offers, or using surplus energy from electric vehicle batteries to power lighting

There is growing interest in the concept of 'digital twins' – development of a virtual 3D model of a building or campus that monitors and manages the collected data from sensors. Reports and dashboards allow interpretation of the combined datasets, show the impact of adjustments and can be used to model and forecast future scenarios.


Energy has been the focus of a number of initiatives, driven by environmental and cost concerns but also by the need to enhance the campus user’s experience. Georgia Tech for example, runs the Georgia Smart Program with a range of initiatives that include digital twins helping to manage energy and water, informed by the movement of people across the city. This type of initiative involves collecting data from energy and utility systems all over campus, analysing consumption trends and looking for opportunities to become more efficient.

Developing a predictive model of campus energy use is complex but examples using thermal networks and electric grid modelling aspire to systems that can self-correct without human intervention.

It isn’t just about saving money or reducing consumption. These systems can monitor and adjust the working environment to make it more comfortable for working and learning. A room that is too hot could open a window automatically for example, and what about adjusting noise levels or air quality or improving safety?


At the other end of the consumption lifecycle is waste. Waste management and recycling are particular challenges for any large organisation with a highly mobile, varied user base. Already, facilities management services can prioritise bin collection by weight, but combining such data with mapping information on facilities (for example, toilets and social spaces) and routing collection services effectively could streamline the waste management process.

Location and movement

Many opportunities exist for improving the flow of vehicles, supplies and people. Ideas include reducing queueing time at registration, ensuring food supplies meet demand and synchronising public transport to special events.

Location data is currently widely used through smartphone apps and contributes to analysis – for example of traffic flow in Google Maps. A combination of mapping and location-tracking within campus could provide a range of interesting applications including:

  • Finding a workstation or a seat in the library
  • Providing real-time routing for the most direct journey, wheelchair access or combining with activity monitors such as Fitbit to hit exercise targets
  • Avoiding congested areas, managing people-flow more smoothly by suggesting alternative routes or implementing one-way systems
  • Better use of parking facilities
  • A better experience for visitors through improving signage

A smart campus becomes an intelligent campus when the data from these kinds of activities is connected and combined with data from other sources to create and analyse a larger dataset.

Internal tracking and wayfinding

Colleges and universities can provide on-campus navigation to students, staff and visitors through online maps, effective external signage and technologies such as wayfinding.

Wayfinding involves four stages:

  • Orientation – determining the individual’s current location in relation to where they want to go
  • Routing decision – selecting the best route, taking account of individual mobility or other needs. It could also help with the optimal flow of people
  • Routing monitoring – checking the individual is heading the right way and deciding an alternative route if there are problems
  • Destination recognition – informing the person when they reach the right place

GPS doesn’t work well internally, so other technologies must be implemented to allow for internal geo-positioning, for example using geo-locating through wireless routers on the network or the use of RFID and motion trackers.

Use of space

Universities and colleges have a complex portfolio of buildings, equipment and spaces designed either for specific purposes or for flexible use. Room utilisation is a challenge for timetabling and resource management. We have already noted some learning-specific opportunities such as live information on availability of lecture rooms and alternative, flexible spaces. Equally, use of research equipment could be monitored and displayed.

Open-plan offices, hot-desking and working from home are common for staff, but many of these processes rely on accurate planning in advance rather than responsiveness and flexibility in real time. They may be constrained by specific processes. Examples of more adaptive facilities use include:

  • Interchangeable spaces between teaching, research and public events
  • Use of space according to time of day, weather, noise levels or numbers of people – avoiding crowding and smoothing out peaks to enable equitable access to equipment

Additional benefits could be realised by combining different data sets and sending out, for example, contextual notifications to get information to students and staff at the right time. You could look up where there is a free workstation and your smartphone could indicate the nearest one. It could also assess how many other people are heading that way, check the noise levels and find the quickest route there.

Leda's day: attending her lecture

As Leda walked around the campus she noticed a lot of devices attached to ceilings and walls. She recognised CCTV-style cameras - though some looked more like speed cameras with some kind of sensor - as well as devices with lights in lecture theatres. Leda made her way to her next session using the wayfinding app on her phone, as she knew her usual route was closed due to building works. The app would show her the fastest route. As she walked into her seminar room, she touched her radio frequency identification (RFID) enabled smartphone to the touchpad by the door. This registered her attendance and the app recognised her location so it started to download the resources for the seminar to her phone and registered her device for the polling and audience response system. Leda found the process much more transparent than being given a clicker. She liked being able to use a single device for all her smart campus interactions.


Ethics (along with security) are perhaps the biggest concerns that campus users have when considering aspects of the intelligent campus.

Fears and concerns

Collecting and interpreting data from multiple sources understandably raises concerns.

Some campus users will doubt the possible benefits and applications of the intelligent campus. They will quite reasonably be protective of their personal data, conscious of security and wary of misinterpretation. Even now, when sharing data on apps is widely accepted, these fears and concerns should not be underestimated or dismissed.  

It’s important to consider several aspects:

  • Awareness and control of one’s own data and its use  
  • Respecting individual privacy  
  • Appropriate interpretation and decision-making
  • Clear and transparent processes and policies

Using data and analytics is likely to require new devices and systems, as well as changes to policies and processes.

Different types of data may involve individuals, groups and their activities. As analysis is done, decisions are reached and interventions made, there is scope for misinterpretation and misuse. So those who design and implement applications within the intelligent campus have a responsibility to reassure people and ensure effective management.

Questions that campus users may have include:

  • What data is being collected about me?  
  • Why is it being collected?  
  • What will it be used for?
  • How is it being interpreted?
  • What actions will be taken as a result?  
  • Who will see the data?  
  • Can I control what data is collected and shared?

Specific concerns include the notion of being tracked – that a person’s location is being monitored for surveillance or to check up on them. The original intention might be justified, such as logging attendance or clocking in for work, but other interpretations might be made – how many breaks you have, or how often you go to the toilet! Whether these are intended uses or not, collecting the data raises concerns about how it might be used.  

To explore this further, read our code of practice for learning analytics.

Personal data and privacy  

Personal data is defined by the 2018 General Data Protection Regulation (GDPR) as data relating to a living individual who can be identified. This isn’t just identification from the data itself, but from other data or information that could be in the possession of the 'data controller'. Individuals have the right to correct inaccurate personal data.  

The GDPR supersedes the Data Protection Act 1998 and all universities and colleges should have policies that demonstrate compliance

Institutions will need to reflect on the following key areas that changed under the GDPR legislation when implementing or enhancing an existing intelligent campus: 

  • Accountability measures  
  • Privacy by default or design  
  • Data protection impact assessments  
  • Higher standards for valid consent  
  • Statutory liability for processors   
  • Mandatory breach notification within 72 hours of becoming aware of it (where feasible) 
  • Increased data subject rights  
  • Greater transparency around data processing  
  • Profiling  
  • Minimum mandatory contractual provisions in data processing clauses/ contracts  
  • Tighter rules on international transfers 

Some of the data used in intelligent campus activities might not be thought of as personal. However, combined with data from other sources (for example, precise location and user behaviour) it can become possible to identify individuals. Anonymised data, once aggregated, can lead to better understanding of user behaviour and facilities management, but also potentially reduce privacy. 

For example, when a room is booked by a student society, the membership of that society is known. The movement of anonymous individuals shows a group congregating in that room, attendance records show who is present at lectures that day and slowly a picture builds of who is doing what, where and when. 

One key point is that access to data and to the analysis of it should be limited to people who have a legitimate need for it. This leads to a number of other policy and procedural issues that must be addressed.


Universities and colleges generally have policies relating to the use of data. But are they sufficient to cover the increasing complexity of different data types, sources and integration?  

This includes the notion of responsibility across legal and ethical concerns. The different elements of collection, anonymisation, analysis and decision-making need clear and specific responsibility assigned to them, and the policy must set out the objectives and intentions, define interventions to be carried out and be specific about retention and stewardship of data. This could involve staff and services from different parts of the institution, including IT, student services, legal and policy representatives. In addition, it’s essential to consult with those who will potentially be affected at all stages of the design and implementation.  

Transparency, consent and sharing  

The objectives and processes involved in collecting and analysing data should be made clear. Obtaining individuals’ consent to use their data is critical. There are three aspects of consent to consider:  

  • Gathering – how the data is collected or recorded, and for what purpose  
  • Processing – concerning the interpretation of the data  
  • Actioning – making interventions on the basis of the decisions reached  

Having appropriate policies and effective implementation in these three aspects is important. This ensures individuals are fully aware of what data is being collected and used, and they have made informed decisions about it.  

As an example, if anonymised data is collected about the movement of people this might fall under consent for 'gathering'. If the location of a specific individual is being collected and this is used to determine behaviour, then this would require consent under the 'processing' aspect. If the result of the interpretation is that contextual notifications or other information are sent to an individual, then consent would be expected under 'actioning'.  

Another aspect of sharing and consent relates to current approaches to apps on devices such as smartphones. Device users readily accept sharing requirements when they install apps. This may include the sharing of that data with third parties, for example for advertising.  

Typically, this includes location but can also be your email address, contacts, search terms or even access to your camera. Specific examples include health apps collecting sensitive information about health, diet and activities and social media apps holding information on interactions and social groups. 

Understanding the wider impact

Why are users relaxed about accepting various sharing conditions for apps but concerned about them with their university? One reason could be a lack of awareness about what is being shared, and why. Another could be the perceived impact of sharing. The university might be seen to play a more significant part in their life than a faceless company collecting information for advertising. For example, being at university can be a life-defining period for students, with an impact on future career and social groups. Equally, for staff, being tracked or monitored by your employer might have perceived consequences for career prospects, performance reviews or compliance with policies and procedures.  

Universities could choose to add clauses to their terms and conditions, such that users accept these terms as part of wider acceptance of usage policies. However, users may tick the box to agree without really being aware of the implications. Having technically received their consent doesn’t alleviate fears and concerns or avoid problems later.  

Educating users to be more aware of what they are sharing, or to switch permissions off when not needed, is important. Alongside this is transparency from apps and services in how they promote their facilities. Educating students to be more aware of security and privacy is helpful regardless of whether the data is collected by the university itself or other parties. This is an aspect of digital literacy – the competencies needed to participate effectively in a digital knowledge society.  

Interpretation and validity  

There are reasonable concerns over how appropriate it is to link data together and draw conclusions. For example, consider a future scenario where an institution combines attendance and progress records with personal finance data in order to identify struggling students and suggest services such as counselling. This could be seen as a valuable intervention or a flawed interpretation and misuse of sensitive data.

Take particular care when designing algorithms that make interpretations, so that the decisions are free from bias or assumptions and are reliable and appropriate. As we move further into 'intelligence' and algorithms that can learn and adapt we need to make sure data-driven algorithms don’t learn our prejudices. This could lead to undesirable and even illegal outcomes such as discrimination.  

Other examples include the use of facial recognition to assess emotions and link this to understanding or anxieties. However, reading facial expressions is complex. A frown could mean confusion or concentration, and we would need to be confident that such an application was based on reliable evidence of success.

Defining a process

Data collection and processing should be subject to the same measures of quality, validity and robustness that might be applied to research. This includes identifying inaccuracies, awareness of incomplete data, care with the choice of data sources and appropriate correlations of data sets. Considerations of validity, usefulness and appropriateness also apply to the algorithms and interventions.

Having rigorous processes across the three phases of gathering, processing and actioning, combined with careful consideration of the concerns of users, will help to deliver benefits to intelligent campus users.

Leda's day: using her data

When Leda started her degree programme she had been concerned about how her data was being gathered, processed and acted upon. It was apparent that her journey through the university, both academically and physically, would be tracked. She was happy that the university had published a guide for students on the ethical use of data. She knew what data she had to provide and understood she had a choice about whether or not other kinds of data were collected. Leda and her friends had been looking at the open algorithms the university used and playing with some of them to see if there were any interesting insights into the way they interacted with the university systems and the campus.


Features of the intelligent campus could enhance security for users and for the campus’s physical assets.


Features of the intelligent campus could enhance security for users and for the campus’s physical assets. CCTV is nothing new but remote control of cameras, analysed by artificial intelligence and linked with real-time notifications to security staff, availability of digital floor plans and control of alarms, locks and access systems, could open up many benefits for campus users. These could include facilities such as mobile panic buttons and alerting systems. Issues such as bullying (including cyber bullying) and harassment, criminal activity and emergencies like natural disasters, medical and health emergencies and violent attacks could all potentially be dealt with by harnessing such technology. Monitoring people’s location and movement and informing the emergency services has been valuabe in several incidents in the US. However, ethical issues including privacy and consent are also critical.

Device security

The proliferation of devices connected through the internet, including embedded and wearable devices, is often referred to as the internet of things (IoT). It has led to concerns about vulnerability to hacking and other attacks, and the safety of data collected.

The devices are typically specialised for a particular function (eg a webcam). Often, they are produced with the minimum functionality to perform their task and few security features, leaving them vulnerable to attack. Furthermore, they may run only with the factory-installed software and be difficult to 'patch' with updated features. This problem may be corrected over time as device manufacturers and their users become more aware of the dangers and willing to pay for added safety. Until then, device users can check more carefully what the device can and can’t do, what can be accessed and who by, and whether updates are available. The local IT department may offer guidance and support on how to integrate devices with the campus infrastructure.

The new Product Security and Telecommunications Infrastructure (PSTI) Bill aims to enhance security of connected devices. The bill doesn’t specify security requirements but gives power to the UK Secretary of State to introduce them. Examples could include banning default passwords and greater transparency on security updates.

It may be appropriate to consider what level of security is needed, depending on the type of data and the criticality of the device. For example, what consequences are there if the temperature sensor of a building is hacked? It may reveal information about the actual temperature but it is probably more serious if the controls of the heating system were compromised. It might be more important to secure the processing and integration of the data and the decisions and actions that follow.


Jisc provides guidance and services related to cyber security and managing IT networks safely, including key issues relevant to the intelligent campus. These include:

  • Viruses and hacking – of particular concern with the proliferation of devices and IoT
  • Authentication – ensuring the right people have access to the right data in line with appropriate transparency and consent
  • Encryption – making transmission of data between devices and systems safe from unauthorised access and supporting the integrity of that data
  • Secure storage – protecting gathered data in a safe location
  • Back-ups and data loss prevention – systems and procedures to guard against data loss or damage

All these need careful consideration when implementing systems and applications to support the intelligent campus features. In addition, the wider IT and network infrastructure must be fit for purpose and  able to support the intended applications. This may include having a reliable network of sufficient bandwidth, including wifi coverage, to allow intelligent solutions and interoperability of systems and services that support data integration and exchange.

Security and ethics are perhaps campus users’ biggest concerns about the intelligent campus. It is important to consider the limitations and opportunities of interconnected smart devices. And it is crucial to appreciate fully the concerns of those who may be affected so that new applications can have maximum impact.

Leda's day: personal privacy

Although Leda had concerns about her personal privacy with all the data gathering happening on campus, she and her friends had noticed a reduction in crime and vandalism. When incidents happened, the reaction time from the campus security officers was really fast because they could get to the right place quicker. Leda felt it was all a bit 'Big Brother' but she did feel safer.

Smart city

The concept of the intelligent campus doesn’t exist in isolation and parallels can be seen with the various initiatives around 'smart cities'. Integrating smart cities with the intelligent campus offers many interesting opportunities.

The smart city may focus on key areas such as delivering services effectively, enhancing lives and improving the environment. Drivers include increasing urbanisation and population density, financial pressures, sustainability agendas, regulatory requirements and the complexities of managing areas of high population. The latter include issues around housing, employment, crime, pollution and transport. The public also have increasing expectations now they are used to instant, personalised information and 24-hour access to services through mobile devices.

The intended results would be healthier communities, more efficient use of resources and an enabling infrastructure supporting both businesses and the public. Many of these are also objectives for the intelligent campus.

Smart city examples

Smart applications in towns and cities typically include energy and the environment, health, transport and the movement of people. Examples include:

  • Smart traffic sensors easing congestion by adjusting signals
  • Location sensors tracking emergency vehicles or public transport and reporting how close they are to you
  • Weather sensors identifying where floods will occur and notifying residents to evacuate
  • Monitoring where people congregate at different times of the day, the impact on services and the environmental conditions
  • Switching lights/heating on and off in public buildings depending on use
  • Innovative storage and distribution approaches for energy resources, for example drawing unused electricity from electric vehicles
  • A smart water network that detects and reports leaks
  • Hygiene and health management, including monitoring and adjusting cleanliness

As with campus examples, combining and integrating data can yield insights that lead to innovative interventions. One example is combining data on population growth with historic data on traffic and trends in transportation such as cycling. Used to explore new transport routes, this data could enable adjusting the traffic controls in real time to achieve optimum flow.

Linking the campus

The links between city and campus are strongest when strategists take a holistic view of people and facilities. Such a view understands that individuals are simultaneously members of a community, residents in a neighbourhood, students/staff at a university and participants in social groups and activities across boundaries. Data-sharing across the campus and the local area can enable efficient use of resources and better services for users.

The environment and sustainability are common, connected interest areas. Businesses, education bodies and public agencies all have legal, financial and ethical responsibilities to use energy more efficiently.

Monitoring and adjusting energy use in response to demands and conditions is similar on- and off-campus. By joining forces, local agencies and university managers can share expertise, contribute to common goals and coordinate at the interfaces of the campus and the local community.

Transport is another obvious link between the local area and the campus, allowing students and staff to move more easily to, from and around the campus. Key transport routes into the campus may already be identified and prioritised, for example expanding cycle lanes from student accommodation areas. Public transport routes and times could be combined with information on timetabling and actual attendance at curricular and extracurricular campus activities. This can enable real-time information on the next available and closest bus to take you home after lectures, and even facilitate special services. After a conference or a sporting fixture, for example, shuttle buses or shared taxis could be provided in response to demand from individuals expressing interest or booking through their mobile devices.

Research into smart cities

Much of the work being done around smart cities is research involving universities and colleges. Both in the UK and abroad they are conducting experiments around urban design, energy and the environment, transport and health. The campus can act as a test bed for larger-scale activities. In addition, the skills and expertise of researchers across various disciplines can be brought together to address societal challenges.

Here are three examples:

  • The University of Strathclyde’s Institute for Future Cities is using data to develop innovative approaches in areas such as crime, economics and sustainability; previous examples include open data integrated to plot a visualisation of geographical distribution of disease across Glasgow. Data across the city of Glasgow is available through the Glasgow Open Data Hub.
  • In Milton Keynes, as part of the MK:Smart initiative, the Open University and the University of Bedfordshire provided the MK Data Hub to manage and allow access to huge quantities of data from city systems. The sources include the local authority, government, business and private contributors, and application developers used the data relating to energy, water use, transport etc, via APIs and software tools to create applications
  • The University of California has a well established microgrid providing its San Diego campus’s 45,000 people with 85% of their electricity needs and 95% of both heating and cooling via a mixture of solar, gas and steam turbine power. These university campus systems have 200 monitors on power lines and buildings tracking energy use minute by minute

Moving it forward

Smart city and intelligent campus designers have common challenges. Data collection and processing in silos has limited benefits and the integration of different departments and functions is one of the keys to delivering value from the concept. This cross-functional approach is challenging, but the benefits are clear – more efficient use of limited resources, streamlined processes and increased value in services provided.

While the focus initially may seem to be around new technologies, in practice the real effort lies in achieving human coordination and collaboration.

Universities and colleges are well placed to understand and implement key aspects of the intelligent campus – cutting-edge innovation, cross-disciplinary working, rigorous processes, a sharing and collaborative culture and a concern for ethical standards.

Being at the forefront of innovative approaches to smart technology and applications around the digital experience can strengthen the reputation of universities. The societal impact of research is increasingly important, and this can combine with delivering direct benefits to students and other campus users.

Leda's day: catching her bus home

Leda was reading a book in the library. Her phone buzzed notifying her that her bus home was due shortly - if she left now, she would be able to catch it. Leda really liked this because although there was a bus timetable the realities of traffic and weather meant buses weren’t always on time. The bus company used GPS to identify the exact location of their buses and the university app used this data to help learners catch their buses. This saved Leda from having to stand in the rain for too long. As she sat down on the bus her phone buzzed again. As she had walked from the library to the bus stop, her phone downloaded an interesting podcast related to the lecture she had been to, ready for her to listen to on the journey home.


This guide is only a starting point. There will, of course, be many new and ongoing developments that are not touched upon here, while some that we’ve mentioned won’t bear fruit. It may be that issues such as COVID-19 and the energy crisis drive development in unexpected directions, but we hope that over the coming months and years this work will provide food for thought and drive discussion though channels such as the Jisc blog, JiscMail lists and other forums.

So this is just the start. The long-term possibilities for the intelligent campus can only be imagined.

Leda's day: that evening

As Leda settled down for the evening, she reflected on her day. What kind of day would have it been without her phone, without it being connected to the different services on campus and working in a smart, even intelligent, way? It was making her whole experience better, she could focus on her studies and spend a lot less time trying to find rooms. The university called it the intelligent campus, but in Leda’s view it was more than that, it was a campus that improved the whole student experience.

This guide is made available under Creative Commons License (CC BY-NC-ND).