Abstract:

Learning technologies´ procurement can be a challenging task for the buyer organization. Municipalities and schools buy software or hardware, which turn out to be difficult to use. (Clements & Wallin, 2017) Educational sector is also behind in utilizing innovative cutting edge technologies such as artificial intelligence, robotics or virtual reality, in comparison to more commercial fields (Gülbahar, 2007). Public organizations are not as attractive customers to technology suppliers, due to the slow decision-making processes and tight budgets. Despite of the barriers in the field: learning analytics and computational thinking could be the answer to support students preparing to the 21st century in the society where robots take care of many of the jobs we are used to. Personalized learning environments of 2018 are not using these new technology innovations in their full potential (Sjöden, 2017).

Two-part innovation procurement methodology has been developed to bridge the gap between technology suppliers and buyers, with the potential to develop new solutions to the market, corresponding to users’ needs. Pre-commercial procurement (PCP) is the first step of this challenge with the aim of launching a call for tender first to investigate ideas meeting the challenge at hand. European commission co-funded IMAILE- project (Bel & al, 2014) took the PCP instrument and conducted the innovation procurement process in four countries (Finland, Sweden, Germany and Spain) in 2014-2018 with the outcome that solutions created in the IMAILE are now reaching first customers due to this also new jobs in the Learntech market. The procurement in a PCP project is for research and development only, with the idea that the municipalities and schools could then join together later to form the common demand and the second stage of innovation procurement methodology, the public procurement of innovation (PPI) project, which the expanded consortium is now preparing.

In the PLEASE PPI preparation (Wallin et al., 2018), seven countries (with Portugal, Italy and Hungary joining as additional to IMAILE procurers) compared their needs and barriers for future learning technologies. 1300 students and teachers answered questionnaires and workshops were held in schools to study the end-users needs for future learning technologies across Europe (Lang et al., 2018). The results of the needs analysis clearly show that there is a need for an AI enhanced personalised learning environment solution which would answer to the societal problems of saving teachers time, motivating students to actively reach their learning goals as well as detecting school drop-outs early. Southern and Northern parts of Europe differ in the policies within schools radically: In Finland and Sweden, students are encouraged to bring their own devices to school and therefore the future solutions should work on these devices. In Italy, Spain and Portugal, it is forbidden to bring your smart phone into the classroom. Despite of the differences, the needs analysis conducted showed that common demand within Europe exists and innovative solutions should be developed together with the end-users to meet it (Lang et al., 2018).

Keywords:

Innovation, Procurement, Pre-commercial procurement, Public procurement, Learning technologies, Artificial intelligence, Personalised learning environments.