Master’s thesis
Business Management and Entrepreneurship, Hämeenlinna Autumn 2020
Salla Niittymäki
Business Management and Entrepreneurship Abstract
Author Salla Niittymäki Year 2020
Subject Artificial intelligence to support study guidance Supervisors Helena Turunen
The objective of this thesis work was to find out how artificial intelligence can be used in study guidance in higher education and to create an action plan for the target organisation, Häme University of Applied Sciences (HAMK), on how to proceed with the implementation of artificial intelligence in the area of study guidance.
Technology is constantly bringing up new possibilities in all fields and all lines of business.
Artificial intelligence is enabling for example high quality services responding proactively to individual needs, which makes services more personalised, timelier and easy to access in all times and locations. Utilising these technologies in higher education enables educational organisations, and eventually graduates who enter future job markets, to be able to meet the continuously developing competence requirements of world of work and employers. The importance of lifelong learning is increasing, and this sets development requirements not only for education providers but also to guidance activities.
The need for this research came from the drivers set by technological development and future competence requirements and from HAMK’s strategy, were it is envisioned that over the next couple of years student services are available 24/7 and artificial intelligence is used to support guidance and counselling.
The research was implemented though literature review, observing best practices and empirical research with brainstorming workshops involving HAMK students and staff
members. As an outcome, an action plan was created to support the introduction of artificial intelligence in the target organisation, HAMK.
In order to meet the growing needs of guidance, routine activities should be automatized, and artificial intelligence should be used to ensure high quality, accessible and adaptable services. As the competence requirements of future world of work are indicating, lifelong learning and constant self-development play a key role. Therefore, guidance activities should be organised to support expanding education possibilities that are becoming more and more customised, and this requires also individual guidance and counselling. When taking artificial intelligence in to use, it is essential to remember, that it is more than just technology; it is about defining the operations and processes of the organisation, considering ethical perspectives, and eventually, it is about the needs of users and creating better services.
Keywords Study guidance, higher education, artificial intelligence.
Pages 92 pages and appendices 24 pages
Contents
1 Introduction ... 1
1.1 Background ... 1
1.2 Objective and research questions ... 3
2 Target of development ... 5
2.1 Häme University of Applied Sciences ... 5
2.2 Study guidance at HAMK ... 7
2.3 Digital tools and applications at HAMK ... 10
3 Future of education... 13
3.1 Role of world of work ... 13
3.2 Technology to support education ... 14
3.3 Competence requirements and demonstrating competency ... 15
4 Study guidance ... 17
4.1 Concept of guidance ... 17
4.2 Study guidance in university of applied sciences ... 18
4.3 Study guidance framework: student lifecycle ... 20
5 Artificial intelligence and its applications... 26
5.1 Concept of artificial intelligence ... 26
5.2 Areas of artificial intelligence ... 29
5.3 Artificial intelligence in higher education ... 31
5.4 Guidance related artificial intelligence applications ... 33
5.4.1 Institutional level ... 34
5.4.2 Pre-entry phase ... 35
5.4.3 Studying phase ... 36
5.4.4 Chatbots for several purposes ... 44
5.5 Adopting artificial intelligence ... 46
5.6 Challenges and ethical questions ... 48
5.6.1 Trust and transparency ... 48
5.6.2 Artificial intelligence ethics in higher education guidance ... 50
6 Empirical research ... 53
6.1 Methodology ... 53
6.2 Scope of the research and sample ... 55
6.3 Implementation of the research ... 56
6.4 Evaluation of the research ... 58
7 Results of the empirical research ... 61
7.1 Guidelines for development work ... 62
7.1.1 Foundation of guidance ... 62
7.1.2 Future visions and requirements ... 65
7.1.3 Reflecting ethics, values and responsibility ... 66
7.2 Artificial intelligence and other technical solutions to support guidance of students ... 67
7.2.1 Institutional level ... 67
7.2.2 Pre-entry phase ... 68
7.2.3 Studying phase ... 69
8 Conclusions ... 76
8.1 Response to research questions ... 76
8.2 Reflections ... 79
8.3 Proposals on further research ... 81
8.4 Afterword ... 82
Appendices
Appendix 1 The introduction material for workshops Appendix 2 Action plan
1 Introduction
Technology is constantly bringing up new possibilities in our everyday lives, in all fields and all lines of business. Our activities, sleep and even toothbrushing is not only monitored but also guided by smart watches and other applications. In the industry, human is working alongside with collaborative robots, and robot vehicles are giving transportation to both human and goods. Artificial intelligence is making much of these new areas possible, and the underlying objective is to make things more safe, sustainable and eventually extend and enhance people’s lives.
The usage of artificial intelligence is increasing also in the field of higher education, that has already entered he path of digitalisation and is taking first steps in the area of artificial intelligence. The driver of the development is both the willingness to offer better and more suitable services for students to support their studies, development and lifelong learning, and to support the work of staff members by taking away routine tasks and reliving work time to interact with the students and guide them personally and individually.
This thesis is observing the possibilities of artificial intelligence in study guidance especially in the context of universities of applied sciences. The target of development is guidance activities of Häme University of Applied Sciences (HAMK). In addition to the drivers of development described in previous paragraphs, the need for the thesis work comes directly from HAMK strategy for 2030; in the vision, “student services are available 24/7” in the year 2021 and “artificial intelligence to support guidance and counselling” in the year 2023 (Häme University of Applied Sciences, 2019).
1.1 Background
The objective of universities of applied sciences is to provide higher education based on the requirements of world of work and its development as well as to support the professional growth of students and promote lifelong learning (Finnish Universities of Applied Sciences Act, 932/2014). To reach these goals, study guidance is implemented to get students attached to studies, to support the development of professional identity, to manage the
transition towards labour market, further learning and entrepreneurship opportunities, and eventually becoming a responsible actor in the society (Vehviläinen 2014; European Lifelong Guidance Policy Network, 2015, p. 5; 36-38).
The ways of working and the world of work have changed significantly in a short period of time due to technological development and digitalisation, and so has the field of education.
The competence requirements of work life are developing all the time, and technology plays a major role in this area; new technology requires new kinds of skills and for example
artificial intelligence changes organisations, ways of working and the work itself. People must constantly renew their skills and competences, and educational institutions must develop their activities in order to meet the needs of learners as well as the needs of world of work. The role of work life – specifically the role of employers – and the importance of lifelong learning are forecasted to be increasing in the field of education and skills
development (UK Commission for Employment and Skills, 2014, p. 98; Jousilahti, Koponen, Koskinen, Leppänen, Lätti, Mokka, Neuvonen, Nuutinen & Suikkanen, 2017, p. 93-94).
As the pace of development seems to be constantly accelerating and the competence requirements changing rapidly and according to the environment, the need for
comprehensive degrees is forecasted to be getting smaller and there is a need to develop new methods for qualification and skills assessment – in many areas, other types of
demonstrations of skills can become more important than degrees. (Linturi & Kuusi, 2019, p.
174; UK Commission for Employment and Skills, 2014, p. 98; Devaux, Dunkerley, Koch, Bruckmayer, Phillips & Jordan, 2019, p. 66-67)
Developing environment, technology and competence requirements as well as the need to eventually renew qualification demonstrations and assessments have an inevitable effect to higher education and the needs for guidance. The opportunities for education and self- development increase and become more versatile, and the development paths become more personalised and needs oriented. This increases the need for guidance and also brings new requirements for it, and it means that new methods – including technologies – must be taken into use in order to provide high quality guidance services for all those in need.
1.2 Objective and research questions
The objective of this thesis work is to find out how artificial intelligence can be used in study guidance in higher education and to create an action plan for HAMK on how to proceed with the implementation of artificial intelligence in the area of study guidance.
The research questions are:
1. How can artificial intelligence be used in study guidance, what are the possibilities and applications?
a. What is done at the moment, what are the current applications?
b. What could be the future applications?
2. What are the areas of study guidance where artificial intelligence would be most beneficial/needed from the perspective of a student and from the perspective of higher education institution staff members giving guidance?
3. What are the recommendations for HAMK to start using artificial intelligence in study guidance?
To get started, the current situation needs to be analysed; what is the current status of artificial intelligence in guidance processes at HAMK. Secondly, it must be found out what are the artificial intelligence applications that are and could be related to study guidance;
what can be discovered through a literary review and existing studies, and what is done elsewhere – what are best practices. In this phase, it is also essential to see education related future trends and visions to make sure the focus of development will be forward- looking. These first steps are aiming to answer to the first research question.
After the first steps, there will be an empirical part, where target groups – students to whom the operations are targeted and different staff member groups working with guidance – are involved. Brainstorming workshops are organised based on background information, so that the target groups will have a future mindset, not just focusing on the current situation. The aim of future visions and scenarios is not to predict a specific future, but to influence and challenge thinking in a creative way. As an outcome of the workshops, there will be ideas on which areas and/or how to use artificial intelligence in study guidance and this aims to answer to the second research question.
Answering to the third research question will be based on the results of previous questions.
The recommendations for using artificial intelligence in study guidance, the action plan, are based on workshop results as well as on theory and best practices.
The process of the thesis work is described in Figure 1.
Figure 1 Thesis process
The key concepts are study guidance especially in the context of universities of applied sciences as well as artificial intelligence and its applications in the area of guidance in university of applied sciences context. In addition, future visions of education and students’
guidance are brought up in order to target the development towards future instead of focusing too much on current structures and situations.
The theoretical framework combines study guidance with artificial intelligence. At first, study guidance is defined based on student’s path, student lifecycle, and this creates the
framework for study guidance. Secondly, artificial intelligence is observed on general level as well as from the perspective of higher education and especially from the perspective of study guidance. The possibilities of artificial intelligence will be considered around the defined framework of study guidance, and in that kind of areas where it could be used to support to guidance especially in higher education.
Analysing current situation
Literature review & best
practices
Empirial research:
Ideas from target groups
Action plan:
How to start using artificial intelligence in
study guidance
2 Target of development
This thesis is exploring the possibilities of artificial intelligence in study guidance in target organisation Häme University of Applied Sciences (HAMK). The focus is the guidance of degree students, in all forms of it: informing, guiding and counselling. Some of the guidance methods or applications are in use also for other student groups, such as open university students or professional teacher education students, but these are not specifically taken into consideration in this thesis work. Different types of guidance are implemented by several actors, not only by study counsellors, and the whole range of guidance work is taken into consideration in this thesis work.
The author of this thesis is currently working as a guidance counsellor in the Degree Programme of Mechanical Engineering at HAMK and has 15 years of experience in higher education in the areas of study guidance and development projects related to developing education and guidance. This thesis work is not only based on the organisation’s strategy and development needs, but also on the observations that emerge from author’s own work.
Therefore, the development work benefits the whole organisation as well as the work of the author.
2.1 Häme University of Applied Sciences
HAMK is a middle-sized multidisciplinary higher education institution located in seven campuses in southern Finland, in the regions of Kanta-Häme ja Pirkanmaa. HAMK has been offering higher education since 1990s, when universities of applied sciences were formed by uniting several educational institutions. However, the roots lead as far as to year 1840, when agricultural education started in Mustiala Campus.
HAMK offers education in 37 degree programmes on bachelor’s and master’s level as well as professional teacher education in its schools of bioeconomy, wellbeing, technology,
entrepreneurship and business, and professional teacher education. HAMK offers also continuing education and further education, open university studies and exchange studies as well as educational products for the global market. HAMK is strongly connected to business and industry, and carries out customer-oriented applied research as well as development
projects. HAMK has approximately 7800 students and 670 teachers and other staff. (Häme University of Applied Sciences, 2020a)
At HAMK, there are three different study modes:
• 8–16: Full time studies
o Studying intensively at a steady pace on five working days a week with own student group
• 18–100: Part-time studies while working
o Studying alongside working, studies organised in a multiform way to fit into the schedules of those who work/are employed
o Work experience in own field is an advantage as competences can be demonstrated through skill demonstration and own work environment can be used as learning environment
• 24/7: Tailored, fast-track studies
o Studying according to personal study plan by using different existing study modes (8–16 and 18–100) and virtual studies
o Work experience in own field is an advantage as competences can be demonstrated through skill demonstration and own work environment can be used as learning environment
(Häme University of Applied Sciences, n.d.a)
The first and the second study modes are currently in full use in almost all af the degree programmes. The third mode, 24/7 model, is entirely implemented in some of the degree programmes, but in some degree programmes it is under development. The need for completely functioning 24/7 study model is high, since the life situations of students are getting more versatile and the world situations have their own impacts, for example the global Covid-19 pandemic increased the need for entirely online implemented learning.
The funding of Finnish universities of applied sciences will change in the beginning of year 2021. Reforming funding models is part of implementing the national 2030 vision for higher education and research. The aim of the vision is to raise the level of education, increase opportunities for continuous learning in higher education and increase investments in
research and development. In the new funding model for years 2021–2024, the share of degrees in the funding of universities of applied sciences will be increased to 56 % from current 40 %. Sectoral differences, faster graduation and more efficient use of university places are taken into consideration; there will be coefficients for graduation times, multiple similar degrees and fields of education. Incentives for competitive funding for lifelong learning, employment and internationalization, as well as for university publications, will be strengthened. (Ministry of Education and Science, 2019)
According to Controller of HAMK, Pekka Ankkuri (interview 6 October 2020), it can be
roughly said, that only graduates on regular basis are profitable students financially, and that any other types of students generally bring only costs. Graduates bring 56 % of income as presented in the previous paragraph, and as a study time lengthens, the coefficients in terms of funding increases. In 2021, funding will be based on the following coefficients:
• Degree completed on time: coefficient 1,5
• Degree completed up to 12 months after the deadline: coefficient 1,3
• Degree completed more than 12 months after the deadline: coefficient 1
This means, that it will be even more important than earlier, that students graduate, and that they graduate on given timeframe, says the Vice Rector of HAMK, Heidi Ahokallio- Leppälä (interview 12 November 2019). Funding is one of the important factors affecting to study guidance, especially when prioritising activities and choosing the areas of
development.
2.2 Study guidance at HAMK
The Ministry of Education and Culture points out the importance of guidance services in their vision for higher education for 2030, and accordingly, HAMK strategy emphasises the importance of study guidance from the perspective of proceeding of studies as well as from the perspective of continuous learning (Ministry of Education and Culture, n.d.). In the vision of HAMK strategy for 2030, it is stated that “student services are available 24/7” in the year 2021 and “artificial intelligence to support guidance and counselling” in the year 2023 (Häme
University of Applied Sciences, 2019), and these make the foundation for current development work.
The organising of study guidance at HAMK is under change in the year 2020, and the role of study guidance will be strengthened during the years 2020 and 2021. For example, there has been reorganising in the structures of guidance during year 2020: most of degree
programmes have now their own guidance counsellor instead of someone doing guidance alongside other major tasks, and the rest will do the reorganising during year 2021. During the academic year 2019–2020, the guidance plan of HAMK was entirely updated, and this version is used in this thesis. However, the updating work will continue during the academic year 2020–2021 to respond to the changed situation, to the strengthening of the guidance work.
According to the guidance plan of HAMK, the student and her/his objectives are in the focus of the study guidance. The student has the ownership of her/his own learning and is an active and responsible actor in directing her/his own activities. The aim of guidance work is to get students attached to their studies, the development of professional identity as well as students becoming responsible members of the society. Guidance is based on respect and confidentiality, is easily accessible and takes into account the needs of students in a diverse and equal way. (Häme University of Applied Sciences, 2020b)
According to the Vice Rector of HAMK, Heidi Ahokallio-Leppälä (interview 12 November 2019), the three different study modes used at HAMK are not just different according to the implementation, but also from the perspective of guidance and students’ need for guidance services. Students in different study modes have different types of needs for guidance and the implementation of guidance varies. For example, students in the study model 8–16 (full time studies) are present at the campus during the week, when the students in the study model 18–100 (part-time studies while working) are studying mainly online and
independently with far less contact days. This means that the guidance for 8–16 students can take place on the campus and can have more live meetings, group sessions and so on.
For the 18–100 students the guidance is more virtual and digital, and it is not tied to a specific place or time. Generally, the students in 8–16 model are younger and less
experienced than the students in 18–100 model even though the study groups are more and more homogenous nowadays than they used to be for example a decade ago.
Based on the author’s more than 15 years of experience from university of applied sciences, it can be stated, that students’ life situations are usually somewhat different in 8–16 and 18–
100 models, and they need support in different types of areas; younger ones in early adulthood life management and more experienced for example in combining family, work and study life. Also learning abilities might have differences if it is about students with young age and older age, positive and negative ones, and motivation level is somewhat different in these study models; usually study motivation is much higher with 18-100 students, since they have already had time to figure out their objectives, when younger ones are more often still looking for their place in the world and might not be sure of the field they have chosen.
At HAMK, the guidance of students is part of every staff member’s work, and it is taken care of collectively. In addition to study counsellors, other staff members and service units are participating to the informing, guiding of HAMK students. The roles and services related to the guidance of students are:
• Study counsellors
• Heads of degree programmes
• Teachers and tutoring teachers
• Student assistants
• Tutoring students
• Study Secretaries and Student Services
• HAMK International Services
• Admissions Office
(Häme University of Applied Sciences, 2020b)
In addition to the internal actors, there is a wide internal and external network to support the guidance of students. The internal network includes for example student wellbeing services, student health care, student priests, library services, alumni and student unions.
The external network includes for example the operators of world of work (companies and
other organisations), other educational institutions (national and international) and services related to student wellbeing such as local employment services and social services. (Häme University of Applied Sciences, 2020b)
2.3 Digital tools and applications at HAMK
According to the Vice Rector of HAMK, Heidi Ahokallio-Leppälä (interview 12 November 2019), HAMK has a strong strategy in digitalisation. Digitalisation is an essential part of the guidance services of HAMK in all phases of studies, and digital services are independent of time and place and equally accessible.
There are several digital tools and services for students at HAMK. The main ones are Pakki student desktop (used for example for study planning, browsing grades, applying for accreditation of prior learning and obtaining official transcript and study certificate), the Starter Kit of Digital Skills to get familiar with different digital tools used in studies, Yammer information channel and HAMK App mobile application. There is also a service desk system for contacting student services or reporting about problems related to different information technology systems. (Häme University of Applied Sciences, 2020b)
HAMK is currently in the process of taking into use a new digital tool, HowULearn student experiment indicator, that has been under testing in spring 2020. There will also become later an additional tool for teachers related to the same area, HowUTeach, that works as a feedback and support tool for teachers. HowULearn is based on a survey, that students reply three times during their studies: in the first year, middle of studies and before graduation.
The areas of focus are student’s learning processes, experiences from learning environment, general work life skills and wellbeing. For students, the tool will work as a self-reflection tool;
to recognise and develop own learning. For HAMK it offers research-based information on students’ learning and study experience to develop education and services related, to offer students more detailed feedback and to offer them suitable support and guidance, and it also works as a tool for quality management. The tool benefits also employers and work life, since through the tool students, the future employees, will recognise their skills in a better way. (Postareff, 2020)
HAMK is also currently observing the possibilities of learning analytics in guidance through a national joint project of APOA, Learning Analytics in the Universities of Applied Sciences. This learning analytics focusing project is researching and piloting the use of learning analytics universities of applied Sciences. In HAMK, there will be a pilot in spring 2021 related to IntelliBoard application, that will be added to online learning platform Moodle. IntelliBoard helps to visualise and collect together student’s activities in Moodle in a versatile way to support guidance of students. (Jaakkola, 2020; Tampere University of Applied Sciences, n.d.) None of the current digital systems used in study guidance are using artificial intelligence, so they are basic level digital services and tools. According to Head of Information Management of HAMK Kari Helenius (interview 12 November 2019), there is a lot that can be done with just automation or software robotics, and there is not necessary a need for artificial
intelligence. However, whether it is about automation and robotics or artificial intelligence, all data and systems play an essential role and there needs to be a constant development perspective in these.
However, artificial intelligence is already used in one area of HAMK. Library serviced started to use artificial intelligence in year 2018 with Iris.ai tool. Iris.ai uses natural language
processing to review massive collections of scientific literature and find relevant content for research topics. With Iris.ai the perspective is very different because instead of using key words, it uses a larger piece of text and the user cannot see the search logic behind the service. The most beneficial part of the service is that you can quickly find information and terminology on a new topic, and since it uses artificial intelligence, it can find
interdisciplinary information without the user knowing the exact terms. (Kivinen, interview 28 November 2019; Iris.ai, n.d.)
Another artificial intelligence application at HAMK is library’s chatbot LibBotti, that was taken into use in 2019 and that works in Finnish language. In addition to the chatbot, there is also a live chat with a real person during work hours answering to the questions if the
chatbot cannot help the customer. The chatbot was built based on service desk tickets, the most common questions were listed and used when building the chatbot. The replies of the chatbot are built so, that it takes the customer to one of the library websites. During this
work it was noticed in the library, that the websites must be very carefully built in order to meet the needs created by chatbot service.
There have also been some pilot tests related to teaching and guidance in some
development projects. One of the pilots was about using Moodle online platform and the analytics provided, and login data was used to contact students who seemed to be dropping out. During this pilot it was noticed that the report created by artificial intelligence needed further interpretations by a person, since simply based on the data, the conclusions could not be drawn. The pilot did not continue after the project finished and data protection regulations also created a problem in this case. Another pilot was related to using artificial intelligence, more specifically work recognition, in the assessment of learning assignments.
The technology functioned quite well in this context, but the teacher felt that there was also a need for more personal and detailed feedback, since the essays of students included quite personal information, and artificial intelligence did not bring actual help in this course, where the topic was career planning and it is very much about personal and individual issues. (Kerkola, 2020)
3 Future of education
The ways of working and the world of work has changed significantly in a short period of time due to technological development and digitalisation, and so has the field of education as well. The skills needed in the future work life is a very popular topic in many researches, future foresights and vision documents around the world. Education is very much related to this question, since skills and competences need updating all the time in the rapidly changing environments and learning has become lifelong activity instead of something you complete with a specific degree or training programme.
Development and changes in education affect greatly to guidance of students and the
requirements and needs for guidance. Future of education is presented in this thesis work as a background for change requirements and to promote future oriented mindset when it comes to the development of guidance.
3.1 Role of world of work
Based on UK Commission for Employment and Skills (2014, p. 98) scenarios, education is becoming more market-based and employer focused in the future. As the skill requirements are changing and people must learn constantly new skills at workplaces, education is
becoming more work related and learning is becoming more work based.
Similar statements about the future of education can also be seen in Demos Helsinki’s (Jousilahti et al, 2017, p. 93–94) scenarios about the future of work. In the scenarios the role of companies in the field of education is strengthening as they have significant importance in context specific education; there are short trainings for short-term needs. People are
studying a lot, and training and education are taking place alongside with working
throughout people’s lives. There are however differences in these scenarios: in one of them the content of education is focused on meta learning and developing competences in changing work environment, in the other one focus is on learning practical skills whenever needed and in the third scenario the education system is diverged and meritocratic so that expert level employees are highly and constantly trained while employees with general skills are educated cost efficiently.
3.2 Technology to support education
According to the Finnish Committee for the Future (Linturi & Kuusi 2019, p. 172–173) there will be a transformation from traditional teaching to learning by trial and error. Simulators and AI play an important role here, since simulators make it possible to test one’s own skills and perceptions, and AI can be used in simulators to correct performance and provide motivative stimulation. Alongside with simulators, other visual aids utilising for example virtual and augmented reality, will make learning more versatile, fast, motivated and deep.
There will be less need for instrumental learning because of AI and other new measurement devices and search engines. For example, knowing the grammar of a foreign language is not the only way to possess skills, but also knowing how to use appropriate tools for language checking is counted as a part of language skills.
Online learning and the growth of it is mentioned in several researches and scenarios (UK Commission for Employment and Skills, 2014; Jousilahti et al 2017, p. 93 ; Linturi & Kuusi, 2019, p. 172–173; Devaux et al, 2019, p. 66). In order to meet the skill requirements of the future, the education system needs to be changed, and in some areas, this needs to be completed quite radically and rapidly.
The Finnish Committee for the Future (Linturi & Kuusi 2019, p. 176) present, that in order to promote the needed change, all public education should be offered in MOOC (massive open online course) platforms and that public education study materials should be freely available and exercises should use artificial intelligence so that the feedback is instant. According to the research by Devaux et al (2019, p. 66) inclusive digital learning could be widely adopted in order to ensure that everyone is included in the move towards digitalisation of learning and no one would be excluded from work and society in this era of digitalisation.
Steps towards the development presented previously are now taken in Finland. In autumn 2020, a new project to support digitalisation of Finnish education started. Digivisio with a slogan “The digital vision 2030 – Finland as a model country for flexible study”, is a joint project of all Finnish higher education institutions. The purpose is to open national data resources for learning for the use of the individual and society. Long-term digital vision work supports learners' learning throughout life and enables the development of pedagogy and
the renewal of higher education institutions. The objective is, that in 2030, Finland will have an open and recognized learning ecosystem, which will also benefit both research and innovation activities and work life. (Digivisio, 2020) This project and the promises included are a remarkable step towards the future of education and enabling education for all.
3.3 Competence requirements and demonstrating competency
Having the pragmatic understanding of larger entities, significances as well as tools will be the new definition of proficiency. The importance of learning how to learn, knowledge networks and tools that enhance understanding is increasing. Being resilient, ability to adapt to change, media literacy and digital navigating will be integrated into the curricula.
Competence and skills come from several sources and the knowledge outdates faster than earlier. The need for comprehensive degrees is getting smaller as the need for flexible, student-centred learning pathways and bite-sized opportunities is getting higher and there is also a need to provide new methods for cross-crediting as well as for qualification and skills assessment. Degrees can be replaced with competence-based qualifications that measure the required competency instead of courses completed in a certain institution, and in many areas, other types of demonstrations of skills become more important than degrees. (Linturi
& Kuusi, 2019, p. 174; UK Commission for Employment and Skills, 2014, p. 98; Devaux, 2019, p. 66–67)
The change of competence requirements needed in the world of work is followed by a challenge of continuous learning – the updating, supplementing, improving and renewing the skills of those currently employed. Updating the skills and competences of adults is an important societal goal. According to the results of the National Forum for Skills
Anticipation’s anticipation work, there is a need for reform of continuous learning in order to create a clear and motivating structure for the different phases of career and learning paths.
The competence should be documented and demonstrated in other ways than degree certificates, for example by citizen’s competence portfolio, and there should be more additional and continuous education in higher education. (Finnish National Agency for Education, 2019, p. 37–40)
All these changes as well as future visions of world of work and the development education set change requirements to guidance as well. Supporting continuous learning of individuals, whether it takes place in higher education institutions or elsewhere, or whether it is
targeting to degrees or not, and guiding them in the jungle of learning possibilities requires new skills and methods.
There are already several digital tools in use in this work mentioned above, and some of them are using artificial intelligence at least on some level. However, since the work is becoming more and more complex due to the changing environment and increasing number of learning opportunities, it would be beneficial to study these more and find new ways to apply artificial intelligence in this area for the benefit of individual development, continuous learning and organising education and guidance services relates.
4 Study guidance
The key concepts of this thesis are study guidance especially in the context of universities of applied sciences as well as artificial intelligence and its applications focusing in this same context. The theoretical framework combines study guidance with artificial intelligence. At first, in chapter 4, study guidance is defined based on student’s path and this creates the framework for study guidance. Secondly, in chapter 5, artificial intelligence is observed on general level as well as from the perspective of higher education and especially from the perspective of study guidance. The possibilities of artificial intelligence will be considered around the defined framework of study guidance, and in that kind of areas where it could be used to support to guidance especially in higher education.
4.1 Concept of guidance
Guidance as a concept has several meanings or levels. It can be seen as a profession and the work of a specific group, for example study counsellors. Guidance can also be seen an operating and guidance environment. For example, in the context of learning environment, the guidance is targeted to learning and studying. Another level is guidance as a form of producing communication, where encounters and participation play an important role, and which is different type of communication than for example teaching. The fourth way to see guidance is guidance as a methodology of professional help, where the focus is on
counselling and ethics play an important role. Seen this way, guidance has its own principles and this way is different to other forms of helping, for example therapy. The fifth perspective to guidance is to consider guidance as processes, including for example the processes of professional growth and learning skills, or the individual processes of students or the processes of individual guiding activities. (Pasanen, 2011; Karhumaa, 2006, p. 38)
Study guidance consists traditionally of informing, advising and guidance. Guidance can be implemented by several different types of interaction, work processes as well as through information and documents. However, guidance is the type of work, that is mainly done by discussing and being in interaction. It can also be described as a service type of helping work that is based on the needs of the target person. In this kind of situation, the person giving guidance must be genuinely present in the situation, able to listen and use his/her expertise
for the benefit of the person receiving guidance. (Parkkonen, Raudasoja & Rinne, 2017;
Vehviläinen, 2001, p.12; Kainiemi, 2007, p. 31; Karhumaa, 2006, p. 41)
The objectives of study guidance in university of applied sciences can be described as follows: to get students attached to studies, the development of professional identity and becoming a responsible actor in the society (Vehviläinen 2014). Getting attached to studies is one of the most efficient ways to support students in their studies, and this is the work of the whole staff; higher education institution should be a community, where student is taken as a member and where their studies and wellbeing is in the center of actions (Penttinen, Kosonen, Annala & Mäkinen, 2017, p. 30–31). Karhumaa (2006, p. 39–40) also points out the importance of student being a member of a community. As the learning processes are increasingly changing towards being more work life related, experience focused,
independent problem solving required and collective working based, this requires careful collaborative planning, close connections to work life, the development of self-management skills of students as well as strong student and teacher teams. To function, this on its behalf requires means to create and maintain motivation and inspiration, which can be obtained with a communal atmosphere where all members are responsible for achieving it.
4.2 Study guidance in university of applied sciences
According to the Finnish Universities of Applied Sciences Act (932/2014), universities of applied sciences are to provide higher education for professional expert jobs based on the requirements of work life and its development and support the professional growth of students as well as promote lifelong learning. Therefore, the focus is very much on future;
supporting the growth of future professionals and equipping them with skills as well as abilities to develop themselves throughout their lives, and this affects also to the guidance of students.
European Council defines lifelong guidance as a continuous process of identifying capacities, competences and interests in order to make educational and occupational decisions and to manage individual learning, work and other paths where these capacities and competences are learned or used. Guidance on general level covers different activities related to
informing, counselling, competence assessment, support and the teaching of decision-
making and career management skills. (European Lifelong Guidance Policy Network, 2015, p.
5; 36–38)
As promoting lifelong learning is one of the key tasks of universities of applied sciences, and the importance of lifelong learning as well as lifelong guidance are increasing all the time, guidance activities must be organised to support this objective. This means, that in one educational institution or in one education level, the guidance should not focus only on the current issues but look forward to upcoming possibilities.
In the law (Finnish Universities of Applied Sciences Act, 932/2014), study guidance is mentioned in relation to degree-awarding tuitions; study guidance must be arranged to enable full-time students to complete degrees in time, during normative duration. This task is also emphasised in the funding. At the moment, most of the funding comes based on students, who complete at least 55 study credits per year, but according to the new funding model starting in year 2021, the funding is stressing students to graduate in time, as
described more in details in chapter 2.2.
Guidance in higher education focuses on helping students to select appropriate study programmes and to manage the transition to higher education as well as to labour market, further learning and entrepreneurship opportunities. Enhancing the potential of academic experience and linking it to personal and career development is one of the key functions.
Work life connections, career management and employability skills are pointed out several times when considering guidance in higher education in the European Lifelong Guidance Policy Network’s guidelines. (European Lifelong Guidance Policy Network, 2015, p. 5; 36–38) The laws and policies bring up principal elements of guidance, but they do not give
instructions on how study guidance should be arranged in higher education level. Therefore, the ways and means of study guidance are determined independently by each universities of applied sciences, even though there are great similarities in guidance activities and
organising of them in different institutions. According to Karhumaa (2006, p. 38), it is for example typical for universities of applied sciences, that every teacher is giving guidance to students, even though there can also be actual study counsellors, who coordinate guidance activities. Especially in the support of professional growth, which is one of the main
objectives of guidance in universities of applied sciences, the role of teachers is crucial, since they are connected to the world of work in their own field and through their own
experiences and connections, they can offer valuable professional support and insights.
4.3 Study guidance framework: student lifecycle
The guidance of students consists of several components from marketing education and recruiting students to guiding throughout studies and finally to alumni activities and
cooperation through alumni’s workplaces. There are several phases and the transition from one phase to another is complex; there are different needs and demands at different times.
To understand the complexity of student transition, all different phases of student lifecycle should be considered. Understanding the student lifecycle – the mixture of evolving
identities, needs and purposes of students – is important for designing the curricula and learning environment as well as designing the guidance and other support services around students’ phases. If this is taken into consideration in higher education institutions, it can remarkably enrich the student experience as well as their success. (Karhumaa, 2006, p. 38;
Lizzio, 2011, p. 1–2; Matheson, 2018, p. 7–8)
Phases of student lifecycle are presented through similar type of frameworks by several experts. Morgan (n.d.) calls her model the student experience transitions model, which is a framework that describes the student’s study cycle and interlinks academic, welfare and support activities at university level to support the student throughout their university journey starting from first contact and admissions and ending at outduction. This model emphasizes the activities implemented by higher education institution to have an effect on students’ experiences, aspirations and expectations. Morgan’s lifecycle model is visualised in Figure 2.
Figure 2 Morgan’s student experience practitioner transitions (SEPT) framework (Khare, Stewart & Khare, 2018, p. 66)
Lizzio (2011, p. 1) has the similar kind of process-like structure as Morgan, and the phases are named by emphasizing the transition from one phase to another: transition towards (future students), transition in (commencing students, transition through (continuing students) and transitions up, out and back (graduates and alumni). In transition towards, is about exploring opportunities and making choices. In transition in, it is about committing and preparing for studies, joining the community and getting engaged. Transition through means the time of studies and building on success. The phase of transitions up, out and back means graduation phase, becoming an alumnus, partnering and cooperation as well as continuous education. In Lizzio’s model, the identity of students and the development of it play an important role – the identity evolution takes place during the whole cycle and should be considered when designing the education.
The framework for study guidance used in this thesis is based on student’s study path, student lifecycle, like it is in HAMK’s guidance plan, as well as models presented previously in this chapter. Student’s study path is a series of events, that start from pre-entry phase with the search of a study place and admission. This is followed by studying phase with all the
different steps related to degree included, for example compulsory and optional studies, study planning, thesis work and work placement. After finishing studies and graduation, the final phase is becoming an alumnus. The cycle does not end to graduation and becoming an alumnus, but after this there can be for example continuous trainings, and after a bachelor’s degree there can be master’s degree studies. In addition, many alumni stay in close contact to HAMK after graduation, they for example offer project and thesis topics to students, work as a guest lecturer and participate to education development through their work roles.
Through these different cooperation and continuous learning roles, students continue in the cycle as cooperation partners, and if they start studying again, they go to pre-entry and studying phases. This framework is visualised in Figure 3.
Figure 3 Framework for study guidance: student lifecycle
In HAMK’s guidance model, the student is in the centre of the framework, like it is the centre of all operations at HAMK, and guidance services and activities are organised around
student’s needs and study phases. This is also the foundation of this thesis work.
The different steps in the student lifecycle in higher education, parts of students’ study path, can be described as follows:
Studying
*Orientation
*Study planning
*Professional growth
*Career planning
*Graduation
Cooperation
*Alumni activities
*Professional cooperation
*Futher education Pre-entry
*Looking for a study place
*Admission
Pre-entry phase: looking for a study place and admission
In the first stage of student lifecycle is the phase of looking for opportunities and observing possibilities. This phase can start very early, some students start making decisions already in elementary school when choosing optional studies and focusing on areas that might be useful in terms of possible future education or career.
In this phase, it is about looking for information and making decisions. Matheson (2018, p.8) points out, that educational institutions must provide opportunities that enable potential students to make informed choices and to orientate themselves towards their chosen education and future aspirations. This means for example having enough and right kind of information available in a way that students can get realistic image of what is being offered.
The information and images are not only a task for higher education institutions, but the whole society is involved. It affects to educational decisions for example what kind of image different industries are giving of themselves, what is the public opinion and what is valued in the society.
In this first stage, sense of purpose plays an important role. The potential students should have the feeling that they will be able to success, that they need to study in higher education and have the resources for that. They are dealing with questions about fitting in, what is it that they are able and willing to do, and what it all will take. (Matheson, 2018, p. 10; Lizzio, 2011, p. 8) These are the essential questions for educational institutions – how to support the students in finding answers to these.
After the decision has been made, the potential student is applying for education. This is not necessary a long phase, but it is very important, because the application process can be very complicated and require using of software since most of the applications are done digitally.
The applicant must be aware of the specific rules, for example in Finland in the joint application of universities of applied sciences you can only apply for a certain amount of study places.
Educational institutions compete in this pre-entry stage – who will attract the best students, what is the most wanted education. They also compete with other options, like going to work instead of going to school. In many western countries the generations are getting
smaller, so the competition is getting harder – if there are less people, there most likely cannot be the same amount of education available as there is now.
Starting studies and orientation
It is important that students are encouraged and supported to recognise their own
perceptions about learning, studying and development of competences since from the very beginning of their studies. Getting attached to studies should be one of the key factors since the day one of studies, and this means actions such as cooperation with employers and alumni, feedback from student’s learning and studying skills and interest towards students’
career objectives and development throughout the whole study time. Becoming something is the key factor in growing expertise and professional identity, and this process is about finding balance between own actions and expectations from outside. (Kukkonen, 2018, p.
129–131; Penttinen et al, 2017, p. 30–31).
In the beginning of studies, there should become a sense of positive identity for students.
This can be achieved by for example guided self-assessment, showing visions of future career choices and getting connected to the studying environment and society. Engaging socially, learning from and about each other and promoting belonging are important tasks to provide opportunities for. In this phase, students also learn the rules, procedures and norms related to studies, and learn how to navigate in the new environment. (Matheson, 2018, p.
10; Lizzio, 2011, p. 8–9) Studying phase
After getting studies started and being oriented to the new environment, the studies continue towards graduation. Planning of studies is one of the important processes that is going on throughout the whole study time. Study planning involves activities and decisions related to choosing studies from individual courses to study paths that lead to for example specific profession, finding and choosing work placement place and thesis topic, making choices about study time and pace and so on. All these decisions have an effect not only on student’s studies, but also to other areas of life.
There can be several different types of steps during the study years, for example training or work placement periods or exchange studies abroad. Sometimes there can be change of educational institution or degree programme, which requires adapting to new environment.
There can also be events in students’ personal life affecting to studies, for example changes in physical or mental health, in family relations or other situations in life.
Educational institutions should have the ability to support students’ in their different
situations during the whole study time. According to Matheson (2018, p. 12) students should be engaged in activities that make it possible for them to reflect their past academic and personal learning gains and identifying on how they can build on these during the coming years. Feedback and feedforward play an important role here, and the studies should not just focus on the specific substance, but also on developing the general skills needed in professional world.
During studies, the student identity is changing towards graduate identity, and professional aspects start playing a larger role. As the studies proceed, students start developing sense of professional community, mastery, independence and employability. This does not take place automatically, but the education must be designed to support this development. (Lizzio, 2011, p. 8)
Cooperation phase: alumni activities, professional cooperation and further education After graduation phase, students are aiming towards professional world and/or further studies. The process does not stop in this stage, but it has several different forms to continue further. There can be for example continuous trainings, and after a bachelor’s degree there can be master’s degree studies. In addition, many alumni stay in close contact to their educational institution after graduation, they for example offer project and thesis topics to students, work as a guest lecturer and participate to education development through their work roles.
Morgan (n.d.) proposes that students should be outducted from university as they are inducted to university, and she points out that this is a much-neglected area of support in the student lifecycle.
5 Artificial intelligence and its applications
Artificial intelligence is widely used in different areas of business and services, and its usage is increasing also in the field of higher education. Internet made online learning possible, and data processing and different educational technology tools are now part of or even replacing teachers’ daily routines. Education has become more and more digitalised, and a great leap in this area is happening right now in year 2020 because of the global Covid-19 pandemic.
Education in all levels in Finland as well as in many other countries has being at least partially operated remotely in year 2020, and it remains to be seen, how this will affect activities after the pandemic situation calms down one day.
Artificial intelligence models do not operate in principal directly, but it takes numerous iterative improvement processes and quality assurance rounds to create a functional application. However, one of the benefits of artificial intelligence applications is, that it forces modelling the problem to be solved logically, often simplifying the problem. This approach can help to understand and open up processes that are stuck. Acquiring deep skills takes a lot of time and choosing the right problems – or leaving the ones out that do not actually require artificial intelligence – is the most relevant issue. (Halonen & Ranta-Meyer, 2019) This is good to keep in mind when observing the possibilities of artificial intelligence and making the decisions and planning the applications for different purposes.
In this chapter, the concept and application areas of artificial intelligence are presented on general level. Secondly, the application areas are observed in the context of higher
education and specifically in the area of guidance of students according to the framework presented in chapter 4.3. There are examples based on both existing studies and best practices found by the author. After the overview to application areas and examples, some recognised challenges and ethical perspectives are brought up.
5.1 Concept of artificial intelligence
According to Roos (2018), artificial intelligence can be described as automation of reasoning usually based on some mass of data. He also points out, that the term artificial intelligence is somewhat wrongly chosen, since it is about being a tool, not outsourcing our thinking in
some way. This is something, that is good to keep in mind, when exploring the world of artificial intelligence.
Artificial intelligence can be also be determined as a situation, when computer is able to act on data independently and make decisions on that data without having been specifically programmed, and it learns to function better in situations the more it is used (Klutka &
Ackerly, 2019; Nurmi 2019). When it comes to digitalisation, artificial intelligence is one of the key issues right now, and there are numerous companies and other organisations, research and educational institutes as well as public operators working around the theme and trying to figure out how to benefit from the various utilisations of artificial intelligence.
During recent years, many countries have created or are at the moment creating national artificial intelligence strategies and other instruments to be in the front of the development and also to control at their own part the development bringing up important discussions for example about data protection, cyber security, the ethical perspective and so on.
One of the classical approaches to artificial intelligence is dividing it into weak or narrow artificial intelligence and strong or general artificial intelligence. Weak artificial intelligence means that machines can act as they were as intelligent as humans, that they are simulating human thinking, for example performing a task on their own, but always relying on pre- programming. Weak artificial intelligence is mainly solving a certain, predetermined problem. Strong artificial intelligence means that machines can think in the same way as humans, can process and make independent decisions because of complex algorithms behind – it would have a human like understanding and consciousness. Both these definitions compare machine intelligence to human intelligence, and therefore one more perspective to artificial intelligence is rational artificial intelligence, which means that human like features are removed, and basically any kind of artefact performing actions can be intelligent on its own way. (Warwick, 2011, p. 64–69; Jääskeläinen, 2019, p. 14–15)
At the moment, all artificial intelligence applications are using weak artificial intelligence and performing only what they have been programmed to perform, even though complicated and advanced algorithms may seem like strong artificial intelligence applications. There are for example reactive machines that can make predictions based on programmed data or machines that have memory and can learn from past experiences. Current artificial
intelligence applications are working in some areas of human brain, for example in visual or speech recognition, but they are not able to work in that kind of areas, where more parts of brains are functioning at the same time, for example when it comes to self-understanding, self-motivation and self-control. Theory of mind and self-awareness have not yet been invented for machines, but when they maybe one day are, they will be the examples of strong artificial intelligence, and this would mean that machines could outperform humans at cognitive tasks. (Galov, 2019; Lu, Li, Chen, Kim & Serikawa, 2018)
Artificial intelligence can overperform humans in many areas, for example in speed of processing, accuracy of mathematical calculations, extent of memory and the ability to work 24/7 (Warwick, 2011, p. 58). The development of technology, digitalisation and the use of AI has changed not only societies, industries and organisations, but through these the world of work and the way of living on several levels. Artificial intelligence solutions are implemented in several sectors and businesses mainly in order to support human work or replace it. In many cases, tasks that no longer need human labour, become cheaper, more efficient and bring eventually more value to the consumer (Klutka, Ackerly & Magda, 2018, p. 5).
There is no universal definition on artificial intelligence at the moment. The applications of artificial intelligence are expanding all the time, and new ways to apply artificial intelligence in different sectors emerge daily as the technology develops and more experiences are gained. Based on Klutka et al (2018, p. 5–6), artificial intelligence can be described through the following characteristics, if not having them all, but at least some of them:
• Adaptive: taking in new information and adjusting its behaviour accordingly for better effectiveness
• Decisive: interpreting given information and taking appropriate actions to achieve its authorised goals
• Independent: completing most of its decision-making process without the human input
• Responsive: engaging in interaction with humans or other machines, interpreting meaning and formulating an appropriate response
The elements of artificial intelligence are algorithms, data and the computing power of the computer. During recent years, algorithms have developed, and more data has become available. However, artificial intelligence requires good quality big data, and the good quality is still requiring a lot of work. (Nurmi, 2019) One of the reasons behind this is most likely the fact, that the data sources and systems are designed before artificial intelligence
applications have been even considered and they are not functional for artificial intelligence purposes.
5.2 Areas of artificial intelligence
Artificial intelligence is a collection of different kind of technologies, applications, methods and research directions. Some of the most common methods or research areas related to artificial intelligence are for example machine and deep learning, neural networks, computer vision, natural language processing and robotics. (Stanford University, 2016; Ailisto,
Neuvonen, Nyman, Halén & Seppälä, 2018)
An important perspective to artificial intelligence is the ability of computers to learn; learn from experience, change their mode of operation and behaviour (Warwick, 2011, p. 52–53).
Machine learning means computer systems, that can improve their performance the more experience or data they get; they can perform without clear instructions relying on patterns and conclusions. Machine learning areas are for example supervised learning, where
machine is being taught beforehand with specific teaching materials; unsupervised learning, where machine is independently searching data for regularities; reinforcement learning, where machine learns based on the feedback received; and deep learning, where the teaching algorithm is based on a neural network that mimics the structure of the human brain. Machine learning systems can be used to for example for data classification, prediction based on data, identify objects in images, to transfer speech into text, choose internet content based on the user’s preferences or interests or to select relevant search results. Machine learning is used to make artificial intelligence applications adaptive. (LeCun et al, 2015; Aalto University & Reaktor, n.d.; Jääskeläinen, 2019, p. 11–12)
Already now a lot of data is collected and stored in different databases, but the data is not always used for any purposes. At its best, automatically collected data can be processed
automatically in several systems, and machines can be taught to learn from the data in order to create new information and offer new solutions for different purposes.
One great example is from Finnish secondary level educational institution, where they started to use machine learning to recognise the students with high risk of dropping out in order to target support activities to these students. The data included information for example about absences, educational success, motivation survey results, parents’
educational background and as a comparison the information about the same factors from past three years and the statistics about dropouts. (Ailisto et al, 2018, p. 14)
Artificial neural network imitates the function of neurons in the human brains. It is made up of numerous interconnected computing elements, like natural neural networks. Artificial neural network is a parallel distributed processer with generalisation ability, and it stores information through learning. Deep neural networks are used for example in advanced image and speech recognition and translation as well as in online stores’ recommendations.
(Ailisto et al, 2018, p. 15, 47; Guresen, & Kayakutlu, 2011, p. 176).
Computer vision, which can also be called as machine vision, means methods that promote extracting information from a picture automatically and understanding the content of the picture, which can be in different forms like ultrasound picture or 3D picture. In addition to vision, there can also be other types of machine or computer senses, for example motion sense or GPS sense. (Ailisto et al, 2018, p. 10)
Natural language processing, NLP, means using computer programs to analyse and produce natural text and speech. This includes for example machine translation, speech recognition, speech synthesis, optical character recognition, text-to-speech and smart text input. (Ailisto et al, 2018, p. 11–12) There are several practical applications due to NLP, for example all current smart phones include speech recognition with which you can call someone simply giving some oral commands or find a route to your holiday destination by saying the name of the hotel to your Google Maps. Translations programs are becoming so fluent and real-time, that you can easily interact with people without having a common language.
Robotics require almost all aspects of artificial intelligence; it is also said to be the physical dimension of artificial intelligence. Robotics means building and programming the kind of
equipment that can operate in a complex real world. Robot is a device, that includes sensors and actuators that detect the environment, with which the device can perform different actions in its environment and can affect to the physical environment around them. One of the current topics in robotics, is robots interacting with the world around it in generalisable and predictable ways. (Ailisto et al, 2018, p. 19–21; Aalto University & Reaktor, n.d.; Stanford University, 2016)
5.3 Artificial intelligence in higher education
Artificial intelligence is already used in many areas of higher education and there are several plans or visions on how it could be used even more. In some areas, artificial intelligence itself is not bringing anything new, it is replacing human work and this way releasing academic staff’s time for something else. However, this can make a big difference, since this time can be used for example to develop something new or to offer face to face services. In some areas, artificial intelligence can bring remarkable benefits and new services, that have not been possible or reasonable to implement by human work.
The inclusion of artificial intelligence in the range of educational tools is likely to require a reform of pedagogical practices in the future. In addition to artificial intelligence, other new technologies also have applications in education, for example evolving educational online environments and platforms, augmented reality and virtual reality. (Ministry of Education and Culture, n.d. p. 22) For example, the University of Helsinki has started a project to study the application of artificial intelligence technology in professional training situated in virtual reality, and they are aiming to enable the development of VR-based learning technology and products by creating pedagogical and process models for the contextual learning scaffolding provided by an artificial intelligence tutor within a VR environment (University of Helsinki, n.d.).
In addition to these current technologies, the future is also likely to bring completely new kinds of technologies. Educational institutions must keep on following the technological development very carefully and observing potential applications from all fields – in many cases the it might be about transferring the technology into another target of application.
