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Latest trends across all industry are discussing how to deal with explosion of (big) data, how to connect people and devices, how to organize processes across geo zones, systems and organizations and how to optimize process using machine learning and/or artificial intelligence capabilities. The answer to these trends is a solution portfolio for Internet of things (IoT), artificial intelligence (AI) and machine learning (ML).

This is a trend which is valid for higher education & research as well but are universities and research institutes having their own use cases?

During a meeting with customers from universities around the globe we organized an interactive brainstorming session to find out about use cases for IoT, ML and AI especially in higher education & research institutions. To find out about their needs and their ideas how to support the daily business with Iot, ML and AI we asked the following questions

  • What are the business challenges that may be addressed with IoT technology?
  • Think about your institution in 2022: which use cases will be deployed leveraging IoT capabilities?
  • What are the business challenges that may be addressed with AI/ML technology?
  • Think about your institution in 2022: which use cases will be deployed leveraging AI/ML capabilities?

We were looking for use cases which support the university in the term of how to run the university. Of course, some of these institutions do utilize IoT, AI, ML use cases in their research area or they execute research projects in relation to IoT, AI and ML. But there are only a few cases where this do support the administration of education and research and the university in general. At least not today.

Our participating customers gave us a very good insight into their daily challenges and their ideas how a deployed solution could look like.

Our participants do see the challenges to be addressed by IoT in connected student & staff, connected infrastructure and assets, connected fleet and transportation, smart city = smart campus and research (lab) facilities. E.g. there is the need to track student’s attendance automatically and prevent deception. Or the need to utilize all rooms and resources dynamically and sustainable. There is the need to track assets and inventory predictive and proactively. Also, researchers need to be able to monitor the lab equipment & goods for expiration & explosion and/or find underutilized equipment and make it available for others.

With the outlook in 2022 the participants do see quite many use cases which could be deployed leveraging IoT solutions. They defined use cases for such as

  • Connected infrastructure: Universities and research institutes should know utilization of all rooms to plan dynamically and real time timetables. Instructors and researchers should be able to book a room ad hoc and have this insight every time and everywhere. Connected rooms and infrastructure should also support administration to work on security and evacuation plan.
  • Energy saving and utilization: University staff would like analytics of room utilization to optimize energy usage and save costs. Especially facility management would like to organize the energy assignment for the rooms accordingly to utilization to make sure resources are used sustainable.
  • In relation to connected infrastructure participants came up with further use cases such as garbage and waste control, building access control, etc.
  • For connected assets there were use cases listed such as inventory and asset tracking plus predictive & proactive maintenance to avoid additional costs due to missed maintenance.
  • Regarding connected fleet & logistics use cases were simple and nothing which cannot be provided today. Use cases are covering requirements related to campus transportation including E2E mobility integrating public transports, intelligent parking management, etc.
  • Research lab facilities: research groups should be able to manage inventory, equipment and goods. They need to know every time and everywhere, how this equipment and rooms are used. The researcher needs a way to plan research experiments especially the utilization of equipment and goods. That research itself should always run smoothly and not be hindered by lack of resources.

In relation to IoT use cases the outcome shows that participants discussed challenges and use cases which could be used in any industry and not only in higher education & research industry. These are use cases discussed in concepts for smart city or in any organization who is dealing with infrastructure and assets. As our participants were representing university IT organization and management we just may have not uncovered the real higher education & research use cases.

Moving on to next question in relation to AI and ML. our participants do see challenges to be addressed by AI/ML around student performance and progression, research management or scheduling and organization. E.g. student would like to make sure that their selected courses and performance fit to their career plans. Or how can Universities utilize AI/ML capabilities to optimize research proposal processes as well as research collaboration? Universities need to schedule events and always assign automatically the appropriate resource and room to it.

With the outlook in 2022 universities do see quite a lot of use cases which could be deployed leveraging AI/ML. They defined use cases for

  • Research area: e.g. to be successful, researchers need to collaborate across research projects, get the optimal collaborators suggested and make sure it is an intelligent, hyperconnected and reciprocity research collaboration
  • curriculum planning: holistic planning and scheduling tool which considers the progression of every student, the availability of teachers and instructors and complete facility circumstances. If a new event is planned, only based on the content of the event, the appropriate room and resource should be suggested.
  • Optimization in areas such as budget and planning, facility management and logistics by using predictive analytics and intelligent sharing tools.
  • Natural UX like voice based chat consoles (Siiri, Alexa), chat bots integrated with any legacy system. Intelligent support of staff during their daily business and automatically improvement.
  • Pedagogy: e.g. AI based questionnaires, utilize student’s notes in real time during the lecture. Use student’s note for intelligent evaluation, find out where students may need further insight or where lecture needs improvement.
  • Student Services around admission advises, progression advises, employability advises. Monitoring student’s performance and success and providing personalized progression advise. Offer to the students an intelligent career developer mentor, evaluate hiring probability by industry.

In relation to ML and AI, discussed challenges and use cases had a stronger focus on education and research and less on generic administration related areas. With the usage of predictive analytics universities are today deploying solutions utilizing AI capabilities. But there is a much bigger field of options for innovations.

After this brainstorming session, we could say we do see the willingness to innovate specially to drive own digital transformation but less the willingness to invest in deploying use cases leveraging IoT, AI and/or ML.

Maybe it would be an idea to follow the approach “live what you breech” – university administration should be able to leverage on the IoT, AI, ML related research projects of own institution. This may accelerate digital transformation.

 

Silke Jakobi

www.linkedin.com/in/silke-jakobi

SAP Higher Education & Research Cloud Solutions

Learn more on SAP & Higher Ed

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