Finding your way

Use Case: Finding your way around campus


What’s the issue?

Universities can be complex physical spaces, with a myriad of different types of buildings, added in different decades and spread across a wide area, sometimes multiple campuses. Some buildings may be old with obscure access points and navigating adjoining buildings can be confusing. For come city universities sometimes the buildings are even close to or integrated with other shops and services or transport facilities, with the public crossing the campus en route to other destinations. In rural settings, campuses can be spread out with natural features interspersed and clusters of academic or residential buildings.

What specific challenges are there?

Once you are familiar with the campus it may become easier to find your way, but for visitors, prospective students and their parents on open days or graduation, or new staff, it can be confusing and time consuming to locate your destination and an effective route to it. There is a danger that such visitors leave frustrated and get a poor initial impression of the institution.

In addition, campus users with mobility difficulties or other special access needs can be particularly challenged by the campus layout.

Universities have invested considerable time and effort in providing good signage, but buildings change name, departments relocate and new facilities are added, impacting on multiple signs and making it difficult to avoid redundant signage and out of date labelling.
Also dynamic information about activities and events in a building can avoid the need for temporary (paper) notices on doors that look unprofessional and often remain after the event.

What could be done?

Digital maps and records of buildings and facilities could be combined with knowledge of the user’s location to provide information on routes, timings and facilities. Their mobile device could tell students (and staff) where the closest free work station, desk, or collaboration area is as they move around campus.


The navigation information could be tailored to the individual needs of the user, for example their mode of transport, special access requirements, or personal interests and preferences such as what type of food they like. Retail outlets could notify nearby campus users of their services or special daily offers, allow them to order items and collect on their way past. Information from their calendar and lecture timetable on their device could be combined with estimated timings to enable users to know whether they have time to stop or would be late for their lecture. In the event of missing a lecture, students could be notified of missed recordings and materials.

What wayfinding examples exist?

Google maps is familiar tool to many, identifying routes for different types of transport (including cycling and walking), estimating duration times and identifying bottlenecks and delay points. This information isn’t exact and typically not sufficiently detailed for on campus use.

Wayfinding solutions using smart devices have been used in other contexts such as car parking, where real-time information about parking spaces is used to control digital signage and a diagram showing the allocated parking space is shown to the user.

Gatwick Airport, way too early in the morning

Airports are introducing wayfinding solutions to provide information on key aspects such as waiting times at queues. At Miami airport a contextually aware app tailors the user experience based on their location. This includes retrieving flight schedules, navigating to the airport (integrating with Google Maps and other services) and identifying shops and restaurants near to them within the airport.

As satellites are unreliable indoors, at Gatwick airport a beacon based positioning system enables an augmented reality wayfinding tool. This allows passengers to see directions on their mobile device, overlaid on the camera view, to navigate to check in areas, departure gates and baggage belts.

Are there any examples in universities?

University of Technology Sydney created a digital ecosystem of physical and digital signs. Updating names and locations in the digital directory is immediately reflected on screens and signage.  Connective technologies are embedded inside each signage “totem” using NFCs and QR codes to allow mobile phones to interpret the information. Additional features include security and emergency devices, and directions provided via speech in multiple languages. In addition to improving navigation, it has led to reductions in cost and effort of maintenance .

What about ethical and other issues?

In many cases no personal data is collected to provide wayfinding services. The apps combine knowledge of the user’s position with generic digital map and route information transmitted. Generic information on ‘people densities’ can be used for example for queue measurement, streamlining flows of people and reducing congestion.

With additional consent, users could receive more personalised notifications for example if you are late for a lecture, estimate how far away you are and use this information to advise the lecturer whether to start without you or wait.

Who needs to be involved?

Existing campus services such as facilities management and IT would need to coordinate to maximise the benefits, but also those interested in supporting students with special needs, language, and the running of special events such as open days and graduation.

Nudge me

Use Case: The nudging campus


What is Nudging?

Wikipedia says:Nudge theory (or nudge) is a concept in behavioural science, political theory and economics which proposes positive reinforcement and indirect suggestions to try to achieve non-forced compliance to influence the motives, incentives and decision making….”

Nudging aims to influence individuals or groups to act or behave in a certain way using gentle encouragement. It should make the behavioural change simple and easy, with little effort from the individual concerned.

Nudge theory came to prominence through the book “Nudge: Improving Decisions About Health, Wealth, and Happiness”  by Thaler and Sunstein in 2008.

Possibly the best known example of the nudge is the fly etched on urinals at Schiphol airport resulting in lower cleaning costs. Its potential was seen across society with the British government even setting up its own “Nudge Unit”.

Nudging on the campus

A number of universities have been investigating the use of “nudge” to improve their campuses and enhance the student experience. In some cases these techniques are being used but are perhaps not always recognised as nudging. While a clear nudge example might be green footsteps, painted on the pavement, leading to a litter bin, a less obvious case would simply be positioning the bin in the right place and clearly marking the type of litter it should be used for. However, technology can help to nudge the individual and make it fun to use the bin. For example, turning a litter bin an arcade game – by depositing litter though different slots, as they light up, users achieve a game score.

Nudging techniques also overlap with a number of other use cases such as; finding your way around the campus, attendance and wellbeing. It can be used in almost all aspects of campus life. Although many examples are not related to technology, or are particularly innovative, they simply require a slightly different way of thinking, for example, relating to health, campus food outlets putting healthy options at eye level with less healthy options low down on shelving. In fact the area of healthy eating has numerous nudging examples:

  • using smaller plates for meals to reduce portion size and waste,
  • a default of salad rather than chips with a main course,
  • calorie counters and traffic light symbols on menus,
  • putting a high price unhealthy option next to a lower cost healthy choice on the menu

Using technology to nudge

Some of the healthy eating nudges can also involve the use of technology such as:

  • dietary requirements or allergies stored in a smartphone access the menus at eating places across the campus providing healthy or allergy appropriate options
  • the menu calorie counters and traffics lights could be provided via a mobile app

When looking at other technology based nudging, “the internet of things” is likely to play a role. As thousands of objects across the campus connect with the network, including vehicles, room sensors, lights, wearable devices etc. there will be increasing opportunities for campus users to be nudged, particularly by their mobile devices.

We are already familiar with our activity being monitored by wearable devices and may well find that in tracking our movements we will be nudged, by messages and flags on our device, to take routes that make us work a bit harder – using the stairs, walking via an open space and so on. Also data on likely drive times, can be used to nudge campus commuters to take public transport or cycle.

Providing enjoyable or rewarding feedback can also nudge campus user’s behaviour. One well know example is the piano stairs. A set of steps next to an escalator were refurbished to replicate piano keys visually and to play notes when stepped on. This resulted in more people using the stairs rather than the escalator.

The improved use of analytics and student record systems is allowing a number of universities in the United States to identify “at risk” students, who might drop out, and nudge them, using messaging, to remind them of the help and support that is available. In the UK a experiment used text based nudging to improve student attendance and attainment at Leicester College and Manchester College1. This wealth of data about students could include financial information and identify students who are likely to be in financial difficulty. They could then be nudged towards special offers in food stores, free forms of entertainment and low cost meals.

Nudge overload

There has been some discussion of nudge overload since it has become more widely recognised as a useful technique. Constant automated nudging, through the internet of things and use of analytics, could be considered nagging, particularly to those who have become aware of it and possibly resentful of what could be seen as manipulation. Nudging, at its best, is subtle and occasional, not badgering.

When to nudge

Although the definition of nudging has become very wide, with many marketing techniques being aligned with nudge theory, there are many benefits for universities and colleges that employ nudging intelligently across their campuses. The key will be to look for opportunities that are created by changes in behaviour, and particularly when the traditional approach is to give a negative message – “don’t drop litter”, “don’t miss your assignment deadline”, “you need to do more exercise” etc.

The availability of knowledge about the personal needs of campus users, through analytics and the internet of things, along with improved understanding of behavioural change, means that nudging has the potential become a huge asset to the intelligent campus.


1 Using text reminders to increase attendance and attainment (

Building analytics

Use Case: Building analytics


What’s the issue?

Universities have large numbers of buildings of varying ages and conditions, spread over a wide geographic area, sometimes multiple campuses. Managing energy, waste and resources efficiently is important for a number of reasons, including financial pressures, environmental principles and regulations, and improved working conditions for campus users.

What could be done?

A wide variety of environmental conditions can be monitored by sensors connected via the IoT to central control systems and mobile devices. These include temperature, lighting, noise and air quality. In addition, use of other resources such as water, waste and recycling

Collecting data from buildings on the current conditions and energy usage, systems can analyse needs and adjust based on a variety of factors including:

  • the occupancy of buildings and rooms
    • eg turning off heating and lighting when not in use or adjusting to account for the numbers in the room
  • the weather conditions and use of the room
    • perhaps dynamic temperature that takes account of people coming in from the cold and then warming up as they adjust, or getting colder as they sit still
  • predicting the conditions for the following day
    • setting heating and lighting accordingly
    • informing room users of the conditions, or even what part of the room is warmer or colder
  • the real time cost of energy supply
    • taking advantage of cheaper energy at different times
  • feedback from people currently within the building
    • individually reporting their comfort level or reporting problems via mobile devices
  • sensors reporting waste levels from bins to optimise collection


Another key issue is maintenance – that by understanding a more comprehensive picture of usage, the systems and infrastructure that provide heating, lighting etc can be optimally used to minimise or even predict failures and breakdowns and to provide maintenance more efficiently. Staff responsible for such systems can receive data in real time on mobile devices, respond to warnings and alerts, and more quickly be on hand to provide assistance. Routine maintenance can be prioritised to best avoid future problems by detecting the current state of systems.

Building analytics also has the potential to provide reductions in time and effort spent on building maintenance and the associated processes with live reporting and alerts.

What examples are there?

At Georgia Tech University, over 400 smart meters monitor 200 buildings. Their Smart Energy Campus Program works as as a “living laboratory” collecting data from energy utility systems across campus. They can quickly identify areas of unusually high energy usage, and provide a response, reducing time and resources. A visualisation of the campus energy usage gives them a view of consumption via a dashboard. Using thermal network and electric grid modeling, researchers aim to better understand energy usage as well as assess potential upgrades to energy systems and technology.

In Ontario, Canada, McMaster University is collecting real-time data from 60 campus buildings to optimise energy consumption. They also use dynamic-pricing data to forecast and simulate future usage.

Bristol University’s diverse buildings range from the 17th to 21st century and they use a building management system that includes alerts to the security office out of hours to monitor critical systems. A particular goal was to reduce carbon emissions, but the systems also include fire monitoring, and sustainability monitoring.

wind turbine

Looking further to the future

A variety of potential applications arise that are beginning to be explored in research using the data being collected from building analytics. These include:

  • predicting future energy consumption including the impact of increasing battery recharging such as mobile devices and electric vehicles and the provision of charging points
  • changes in energy sources such as the campus generating it’s own electricity from solar, and potentially selling it back to the national grid
  • the impact of weather patterns on the availability of different energy resources and smoothing demand
  • taking advantage of price variation of energy at different times

Scientists at Newcastle University’s Smart Grid Lab simulate power distribution for future scenarios. These include a severe weather power cut and increasing numbers of electric vehicles. Concepts they explore in the living laboratory including real-time notifications to users of consumption, peak demands and pricing, and problems within energy supply. They are developing a model of a “micro-grid” that provides local resilience to wider disruption.

What about ethical and other issues?

Environmental data typically doesn’t include personal data, although individual feedback on conditions could be identifiable. If health-related conditions or special needs were to be taken into account, to adjust environmental conditions, then this data is more sensitive and need appropriate management. There is a wider issue regarding the challenges of responding to individual preferences and the difficulty of getting the balance right across a group of users. Another difficulty is the lack of standards across legacy sensors and the huge challenge of maintaining and upgrading many hundreds of sensors across campus.

Who needs to be involved?

For most of the applications mentioned, the estates and facilities services are the most heavily involved, with appropriate collaboration with IT and networking services. In some examples data from timetabling and other student support services may be necessary to offer the more integrated solutions, and external data from weather forecasting or energy usage more widely.

Adaptive learning

Use Case: Adaptive learning


What’s the issue?

With the increasing use of e-learning and blended learning, there is a growing tension between the potentially interactive physical classroom experience and the largely static, homogeneous content provided through systems such as virtual learning environments. In addition, increasing student numbers, with widening diversity, pose challenges for teaching staff attempting to provide a differentiated, contextualised and personalised learning experience to a large audience.

How can intelligent campus help?

A combination of mobile devices and classroom technology, learner data and institutional systems can integrate to understand learners’ needs and assist them to effective outcomes. A multitude of data about learners already exists, not just their activity within the VLE, but their location on campus (or outside), interactions with the library, demographic data from student information systems, their historical patterns of learning and even their ambitions or goals for the future.

Individuals’ mobile devices along with classroom technology could interact with this wealth of data available about the student and their learning to provide an enhanced experience. Personal devices and apps may also hold information on individual preferences and learning styles to enable differentiated interactions.

What could be done?

  • Digital textbooks providing hints, explanations and practice questions, linked to the class content on the VLE. Monitoring common problems could feed into course design.
  • Headsets supporting virtual or augmented reality views of content and places around the lab, campus or at sites of educational interest.
  • Adaptive devices providing personalised interfaces (including accessible tools) to meet learning preferences, and by monitoring progress adding new or supplementary content to match the needs for individual pace and style.
  • Virtual field trips (or augmented reality physical field trips) including location-awareness to give students prompts and contextual content, allowing personalised interaction with the environment.
  • Science experiments conducted remotely through the internet, with devices connected to laboratory equipment to take measurements and set analysis tasks.

Other applications use real time natural language processing to spot intentions or emotions in conversations with peers or tutors, for example using chat-based apps on smartphones. Intelligent “conversational” agents can provide tailored feedback to students on performance or respond to common questions with personalised information understanding their context, including their geo-location on campus and proximity to learning resources and facilities.

Are there any current examples?

There are few examples in practice of IoT devices integrating into adaptive learning approaches. In most cases, where adaptive learning is being used, it relies solely on institutional systems and learner analytics. These could be adapted to add integration with mobile devices.

Bolton College is providing a personalised pathway through VLE content by differentiating students according the data about them such as how they performed in previous tutorials, leading to more stretch and challenge if they do well. The college have also implemented an online digital assistant that can respond to natural language questions, in some cases removing the need for the student to access other systems such as the VLE.

At Edinburgh University, a tool using algorithmic machine intelligence has been used and quizzes designed linking to learning pathways. Elsewhere they have piloted use of a twitterbot to answer simple questions for example about deadlines. Cluster analysis and segmentation has also been applied on a wide range of historical data to identify learning trajectories and map these to current students.


What about ethical and other issues?

Security of data is crucial, with these applications involving wide ranging data including personal circumstances of students and, potentially, financial data. For example a student may use a chatbot to query how much money they are still owing on their course.

Algorithms may be making decisions and providing feedback on behalf of tutors, including on sensitive topics. Even if teaching staff have been involved in programming the behaviours and responses, sometimes an automated system may not produce an output that a human would agree was “appropriate”. It is likely that careful moderation would be needed, at least until the technology was sufficiently mature.

Who needs to be involved?

Having teachers involved in the development alongside data and systems specialists allows for a more integrated approach utilising the technology within the pedagogic context. Skill sets including instructional designers and algorithm specialists may be needed. Having a robust policy at an institutional level will help manage the ethical issues more effectively.

Adapting to needs

Use Case: Adapting to needs – intelligent accessibility


What’s the issue?

The number of students entering university who have a known disability is increasing, (56% between 2011 and 2015) and at the same time funding has faced cuts, with an impact on outcomes.

Increasing demand for services and pressure on resources suggests universities could turn to intelligent solutions across campus to improve the student experience, encourage participation and improve outcomes.

What specific challenges are there?

Going to university can prove to be a daunting experience, from busy open days and freshers week to daily negotiation of timetabling and the campus.

Universities can be physically and organisationally complex, with a myriad of different types of buildings and access requirements, spread across large areas, sometimes involving multiple campuses. Getting around campus can be a challenge and that is before any adaptation for learning is considered.

Other concerns identified by prospective students include making friends and having the right equipment.

What solutions exist?

Many “smart” devices have emerged around independent living and home automation. These include controlling your heating, security and lighting through your smartphone, sometimes to a personalised schedule or in response to location. Lighting can be adjusted to improve contrast for improved visibility, or visual signals can indicate visitors at the door, or appliances in operation. Research on healthcare and society also is developing wearable devices to support different needs.

In addition, many commonplace smartphone apps can support communication, networking and socialising. Mapping and route finding is another application readily available, such as identifying routes for public transport, cycling or pedestrians.

Other apps available include those for communicating via sign language, speech generation from text, screen magnifiers using the phone’s camera, and tasks and visual prompting tools to help with organising and attention. The intelligent campus can provide ways of integrating these personal tools with physical facilities, IT and learning systems.


How can these be used on campus?

The intelligent campus is well placed to offer personalised services and adaptive facilities. Smartphones can operate apps appropriate to the individual’s needs, using interfaces including speech and natural language recognition. These could communicate with the university infrastructure, identifying particular needs within a building or area, and adjusting the environment to suit.

Navigating your way around campus might include apps for accessible routing, tailored for different needs, avoiding more challenging obstacles and estimating travel time. Real time notification of appropriate nearby facilities such as disabled toilets, parking bays, transport or access ramps could be transmitted to students as they move around the campus. Notification of special access or other needs might also be received by timetabling services to allow provision for flexible start times or remote access.

In the classroom

Example of adaptive learning environments could include

  • a lecture theatre adopting different lighting settings or activating special access features, triggered by the arrival of a student triggers (either automated or under the control of the student’s phone)
  • automatically storing content from the lecture, shared and translated into multiple formats, available on platforms that are accessed by a variety of different devices
  • automatically incorporating personal profiles of learning styles, preferences and special considerations into teaching and learning activities
  • integrating existing interface tools and apps such as speech-text conversion, captioning and gestures with learning content
  • connecting accessibility apps with classroom facilities for example to respond to head movements or translate impaired speech
  • structuring the delivery of content to allow review and reflection at the student’s own pace and comfort level

What about ethical issues?

Confidentiality and security are clearly important, with information about disabilities and special needs as well as access to specific support services and financial support. This could be even more detailed than other contexts, to include daily activities and living patterns. Students need to be confident that their information is kept private and shared appropriately with service providers. For universities there may be challenges in deciding on priorities and technical implementation of a wide range of devices and adjustments.

Who needs to be involved?

A wide range of services across the institution have an interest in such services, from housing to finance, estates to learning and teaching. Student records, IT and libraries can will also play a crucial role, alongside specialist support services for accessibility.

Entering Alpha


Following a successful transition meeting yesterday. The Intelligent Campus project has moved from Discovery to Alpha.

In the discovery phase we explore new ideas and emerging technology to establish which ideas meet Jisc member’s needs, are technically feasible, fit Jisc’s remit and stand a chance of becoming sustainable services. If an idea passes all these criteria, we move to the alpha phase.

The Intelligent Campus project has been looking at use cases following the co-design process in which the three ideas we came up with were use-cases which would be enabled if we could develop the basic data infrastructure for the intelligent campus.

However that is a big, long-term development so we decided that our immediate goal should be to analyse in more depth the potential use-cases as well as the technical, ethical and business implications of this approach. As part of this process we published a draft guide to the Intelligent Campus.

Having done this it gave us a better idea of where best to get started on developing the data infrastructure for the intelligent campus.

So now we enter the alpha phase. Here we will try out the most promising ideas with a small number of organisations to see what works and whether they offer real world benefits. If they do, we move onto the beta phase.

More information on how we innovate.


“If the walls could talk”

These are my slides from my session at the ALT Conference in Liverpool.

My twenty minute session I introduced the delegates to the concept of the intelligent learning space, one that could learn from the experiences of staff and learners in that space.

If the spaces we use for teaching and learning could speak to us, what would they say? The places and spaces across colleges and universities are some of our biggest investments. But are we using them effectively to enhance and enrich the learning journey? Does the environment in which we learn have impact on the learning journey?

There is an institutional memory within those walls that is inaccessible and lost every time the learners and teachers leave the room. The room doesn’t remember what worked well or what could have been better. The spaces, if they could store experiences and feedback, would know what worked well, and what didn’t, for different learning activities. What if, we could we use data gathered from teachers and students, as well as space usage, to inform and improve teaching and learning?

The hyperbole around AR, VR, artificial intelligence and the internet of things as created a cynical bubble among some staff and institutional decision makers, especially those that have been burned by previous tech fads. But it may be time to put aside the cynicism that this kind of hype generates and look seriously at how we can take advantage of these emerging technologies to improve the student experience, research and the management of our campuses (Clay 2017).

I also covered some of the more “creepy” elements that often arise when you start talking about gathering data about people (as well as spaces). I spoke about the importance of consent and openness when it comes to data tracking.

I presented six scenarios;

  • using environmental data to improve learning
  • using historical activity data to inform teaching practice
  • delivering appropriate interventions based on analysis of existing experiences
  • using the Internet of Things to inform teaching and learning practice
  • using artificial intelligence to plan lessons and room layouts
  • using artificial intelligence tools to influence and support learning, teaching and assessment

However in a twenty minute session we didn’t really have the time and space to discuss these in detail, we may explore these in more depth on the blog at a later date.

There were some interesting useful comments from the room (and on the Twitter) and these were captured in a Storify.

Overall an interesting and useful session.


Sclater, N. (2017). Code of practice for learning analytics | Jisc. [online] Jisc. Available at: [Accessed 20 Mar. 2017].

Clay, J. (2017). Could we use artificial intelligence to help manage learning spaces and improve teaching and learning? | Intelligent campus. [online] Available at: [Accessed 20 Mar. 2017].

Amplification of the conference session at #altc

At the Association of Learning Technology annual conference in Liverpool I delivered a twenty minutes session on the Intelligent Campus. These are the responses from the delegates who attended from the Twitter.+

At the Association of Learning Technology annual conference in Liverpool I delivered a twenty minutes session on the Intelligent Campus. These are the responses from the delegates who attended from the Twitter.

If the walls could talk #altc

This year at the Association for Learning Technology (ALT) Conference in Liverpool I am presenting a twenty minute session on the Intelligent Campus.

If the walls could talk #altc

If the spaces we use for teaching and learning could speak to us, what would they say?  The places and spaces across colleges and universities are some of our biggest investments. But are we using them effectively to enhance and enrich the learning journey? Does the environment in which we learn have impact on the learning journey?

There is an institutional memory within those walls that is inaccessible and lost every time the learners and teachers leave the room. The room doesn’t remember what worked well or what could have been better. The spaces, if they could store experiences and feedback, would know what worked well, and what didn’t, for different learning activities. What if, we could we use data gathered from teachers and students, as well as space usage, to inform and improve teaching and learning?

The hyperbole around AR, VR, artificial intelligence and the internet of things as created a cynical bubble among some staff and institutional decision makers, especially those that have been burned by previous tech fads. But it may be time to put aside the cynicism that this kind of hype generates and look seriously at how we can take advantage of these emerging technologies to improve the student experience, research and the management of our campuses (Clay 2017)

If the walls, our learning and teaching spaces, could talk, what could they tell us, and how would it change what we do?

This 20 minute interactive discussion, will challenge delegates to reflect and discuss a series of short scenarios on how data gathering, analytics and appropriate technological interventions could be used to enhance and improve teaching and learning. Participants will be asked if this approach of using data gathering, analytics, the Internet of Things and artificial intelligence be used to enable innovation and effectively drive and manage change. In a similar vein to learning analytics (Sclater 2017) what are the ethical questions we need to ask and answer when looking at learning space analytics.

Delegates will go away with an understanding of some of the issues that arise once we start making our learning spaces “smart” and “intelligent”.

The session is on Wednesday 6th September at 13:00 in the Elizabeth Gallery II (moved to larger room) Library Room


Sclater, N. (2017). Code of practice for learning analytics | Jisc. [online] Jisc. Available at: [Accessed 20 Mar. 2017].

Clay, J. (2017). Could we use artificial intelligence to help manage learning spaces and improve teaching and learning? | Intelligent campus. [online] Available at: [Accessed 20 Mar. 2017].

Do you need help?


Use Case: Emotional Recognition in the Library

Could we use emotional recognition technology to discover when students are “troubled” or “in need of help” and then make appropriate interventions to support them in their studies? With constant emotional recognition could we start to understand what a “troubled” user’s expression is.

What’s the issue?

Users of the library often need help and support when attempting to find resources, use resources or other aspects of their learning. Users may not know where to find resources, or even what resources may be useful for them at that point in time. They may be using resources, but unsure if they are appropriate or relevant. Students may be using the library as a study space and are having other trouble with aspects of their studies.

What are the current solutions?

Most solutions rely on the students seeking support or help from dedicated staff within the library, The implication is they know they can seek support and they can find a relevant member of staff to ask. There is also the assumption that the student knows they need help, they may not see that as a possible option.

Another solution is for library staff to “discover” which students are having issues and trying to guess which students need support.

Emotional recognition software could constantly scan users and using appropriate algorithms flag students that may need support and direct staff to them as quickly as possible.

Combined with facial recognition, details about the student could be provided to the staff to enable them to tailor and personalise the support the student needs.

What about ethical and other issues?

You can imagine the strong feelings that such a system could evoke, with the idea that users of the library are being constantly scanned and monitored. Consent to be part of a system would be critical, though often consent is not sought for non-technological solutions. Another challenge would be the concern that such scanning and data gathering may be used for other purposes outside support in the library.

Are there any current examples?

Emotion recognition and emotion analysis are being studied by companies and universities around the world, most are looking at the technology to improve marketing and effectiveness of video content.


So how could it work?

Humans can already recognise emotions in variable abilities, any kind of system to scan and recognise emotion using technology could inform and advise staff faster and quicker. Enabling a rapid response to potential difficulties.

Software already exists that utilises artificial intelligence to predict “attitudes and actions based on facial expressions”.

The challenge will be ensuring staff have the appropriate interventions to hand having identified those students who may be have problems.