Yukimi Takahashi and Poonsri Vate-U-Lan Graduate School of Advanced Technology Management, Assumption University of Thailand, Bangkok, Thailand DOI: 10.34190/ECIAIR.19.091 Proceedings of the European Conference on the Impact of Artificial Intelligence and Robotics ECIAIR 2019 Hosted By EM-Normandie Business School Oxford, UK 31 October–1 November 2019 Edited by Dr Paul Griffiths and Dr. Mitt Nowshade Kabir

2. Proceedings of the
European Conference on the Impact of
Artificial Intelligence and Robotics
ECIAIR 2019
Hosted By
EM-Normandie Business School
Oxford, UK
31 October–1 November 2019
Edited by
Dr Paul Griffiths and Dr. Mitt Nowshade Kabir

3. Copyright The Authors, 2019. All Rights Reserved.
No reproduction, copy or transmission may be made without written permission from the individual authors.
Review Process
Papers submitted to this conference have been double-blind peer reviewed before final acceptance to the conference. Initially, abstracts
were reviewed for relevance and accessibility and successful authors were invited to submit full papers. Many thanks to the reviewers who
helped ensure the quality of all the submissions.
Ethics and Publication Malpractice Policy
ACPIL adheres to a strict ethics and publication malpractice policy for all publications – details of which can be found here:
http://www.academic-conferences.org/policies/ethics-policy-for-publishing-in-the-conference-proceedings-of-academic-conferences-and-
publishing-international-limited/
Conference Proceedings
The Conference Proceedings is a book published with an ISBN and ISSN. The proceedings have been submitted to a number of
accreditation, citation and indexing bodies including Thomson ISI Web of Science and Elsevier Scopus.
Author affiliation details in these proceedings have been reproduced as supplied by the authors themselves.
The Electronic version of the Conference Proceedings is available to download from DROPBOX https://tinyurl.com/ECIAIR19 Select
Download and then Direct Download to access the Pdf file. Free download is available for conference participants for a period of 2 weeks
after the conference.
The Conference Proceedings for this year and previous years can be purchased from http://academic-bookshop.com
E-Book ISBN: 978-1-912764-44-0
Book version ISBN: 978-1-912764-45-7
Published by Academic Conferences and Publishing International Limited
Reading
UK
44-118-972-4148
www.academic-conferences.org

4. i
Contents
Paper Title Author(s) Page
No
Preface v
Committee vi
Biographies vii
Research papers
Towards a Framework for Effective Instructor-
AI Collaboration in Education
Mamun Ala and Danilo Wegner 1
Artificial Intelligence Policy in China:
Implications and Challenges
Rafif Al-Sayed and Jianhua Yang 12
Terrorism and DeepFake: from Hybrid Warfare
to Post-Truth Warfare in a Hybrid World
Arije Antinori 23
Innovative Approaches in Russian Education: a
Model of Adaptive Learning Process Based on
Artificial Intelligence
Alexey Bataev, Olga Zaborovskaia and Alexandr
Gorovoy
31
JELLYBEANBOT: A Dynamic Low Cost Robotics
Shield for Education
Romeo Botes 40
Attitudes of the Net Generation when
Learning to Program Robots
Romeo Botes and Imelda Smit 50
Measuring Computer Science Students’
Acceptance of Arduino Micro Development
Boards as Teaching Tool
Romeo Botes and Malie Zeeman 61
Who Should Be the Boss? Machines or a
Human?
Jim Q. Chen 71
AI Ethics: The Thin Line between Computer
Simulation and Deception
Larry J. Crockett 80
The Effects of Artificial Intelligence, Robotics,
and Industry 4.0 Technologies. Insights from
the Healthcare Sector
Francesca Dal Mas, Daniele Piccolo, Lorenzo
Cobianchi, Leif Edvinsson, Gareth Presch,
Maurizio Massaro, Miran Skrap, Antonjacopo
Ferrario di Tor Vajana, Stanislao D’Auria, Carlo
Bagnoli
88
Case-Based Reasoning (CBR) and Neural
Networks for Complex Problems
Souâd Demigha 96
Case-Based Reasoning (CBR) and Robotics Souâd Demigha 106
Integrating Machine Learning Algorithms in
the Engineering of Weaponized Malware
Chuck Easttom 113
On the Relationship of Emergence and Non-
Linear Dynamics to Machine Learning and
Synthetic Consciousness
Chuck Easttom 122
The Potential Influence of Artificial Intelligence
on Plagiarism: A Higher Education Perspective
Errol Francke and Bennett Alexander 131

5. ii
Paper Title Author(s) Page
No
The Impact of AI on Employment and
Organisation in the Industrial Working
Environment of the Future
Swetlana Franken and Malte Wattenberg 141
Artificial Intelligence in Education: a Promise, a
Threat or a Hype?
Niklas Humble and Peter Mozelius 149
Teacher-Supported AI or AI-Supported
Teachers?
Niklas Humble and Peter Mozelius 157
Laws about Laws: Lethality, Autonomy and
Sovereignty in a 21st Century Perspective
Ion A. Iftimie, Elias G. Carayannis and Michele C.
A. Iftimie
165
A Bibliometric Analysis Deconstructing
Research on how Digitalization Transforms
Knowledge Worker Autonomy
Henry Isegran, Mats Kuvene and Karl Joachim
Breunig
172
Artificial Intelligence-based Digital
Transformation Strategy in Higher Education
Institutions
Mitt Nowshade Kabir 182
Whether and How Artificial Intelligence Will
Change Education and the School System
Mariusz Kakolewicz 191
Artificial Intelligence, Knowledge Management
and Human Vulnerability
John Kingston 198
RethinkAI™: Designing the Impact of AI in the
Future of Work
Vali Lalioti 205
An Approach for Enterprise Architects to
Analyse Opportunities and Constraints for
Applying Artificial Intelligence in Military
Transformations
Juha Mattila and Simon Parkinson 215
Training Data and Rationality Konstantinos Mersinas, Theresa Sobb, Char
Sample, Jonathan Z. Bakdash and David Ormrod
225
Choice, Agency, and Dignity in the age of
Artificial Intelligence
Yuko Murakami 233
Malicious Use of Artificial Intelligence:
Challenging International Psychological
Security
Evgeny Pashentsev 238
Harnessing Interdisciplinarity to Promote the
Ethical Design of AI Systems
Menisha Patel, Helena Webb, Marina Jirotka,
Alan Davoust, Ross Gales, Michael Rovatsos and
Ansgar Koene
246
Is Artificial Intelligence Disruptive? Olga Polunina 255
Possible Use of AI Technologies in
Counterterrorism Responses by Iraqi Security
Establishment
Vitali Ramanouski 261
Robotics and Artificial Intelligence (R+AI)
Solutions: Displacing or Augmenting
Professional Capabilities?
Olga Rivera-Hernaez, David Lopez-Lopez and
José-Miguel Zaldo-Santamaria
266
AI in Smart Cities Development: A Perspective
of Strategic Risk Management
Eduardo Rodriguez and John S. Edwards 277

6. iii
Paper Title Author(s) Page
No
AI, RPA, ML and Other Emerging Technologies:
Anticipating Adoption in the HRM Field
Juha Saukkonen, Pia Kreus, Nora Obermayer,
Óscar Rodríguez Ruiz and Maija Haaranen
287
Racist Soapdishes and Rebellious (?) Children:
Towards Human/AI Cooperation
Keith Scott 297
Artificial Intelligence for Analysis of
Collaborative Consumer Networks
Management
Elena Serova 304
Blockchain Technology Innovation Use-Cases
in the Agriculture Sector: A Systematic Review
Tebogo Sethibe 312
Digital Transformation and the EU Labour
Markets
Marta Christina Suciu, Elena Pelinescu, Mirela
Cristea and Graţiela Georgiana Noja
321
e-Learning, Artificial Intelligence and Block
chain
Paulo Vieira, Paul Crocker and Simão Melo de
Sousa
331
Law Enforcement Use of Artificial Intelligence
for Domestic Security: Challenges and Risks
Murdoch Watney 341
A Responsive Engagement Approach to
Promote the Development of ‘Fairer’
Algorithms
Helena Webb, Alan Davoust, Michael Rovatsos,
Menisha Patel, Ansgar Koene and Marina
Jirotka
349
AI, Business, Short and Long Term
Developments: An Anticipatory Ethical
Analysis
Richard L. Wilson 359
Requirements for Making the MQ-9 fully
Autonomous: An Anticipatory Ethical Analysis
Richard L. Wilson 369
Proposition to use System Dynamics for
Assessing the Impact of new Technologies on
Employment
José Miguel Zaldo, Olga Rivera-Hernaez and
Juan Martín-García
378
A Stochastic Dynamics Model for Shaping
Stock Indexes Using Self-Organization
Processes, Memory and Oscillations
Dmitry Zhukov, Tatiana Khvatova and Leonid
Istratov
390
Phd Research Papers 403
Smart Traffic Incident Reporting System in e-
Government
Mardin A. Anwer, Shareef A. Shareef and Abbas
M. Ali
405
Personalized Virtual Employee Assistant using
IOT Recommender System and Cognitive
Computing
Mona Bokharaei Nia and Mohammadali Afshar
Kazemi
413
Teachers’ Activity when Adapting Digital
Learning Objects: The Context of Foreign
Language Learning in Higher Education
Vilma Sukackė and Brigita Janiūnaitė 423
Toward Understanding the Impact of Artificial
Intelligence on Education: An Empirical
Research in Japan
Yukimi Takahashi and Poonsri Vate-U-Lan 433
Masters Paper 441
Will Robots have the Capacity to Replace
Mankind? Empirical Analysis from Portugal
Mafalda Rombão and Eduardo Tomé 443

7. iv
Paper Title Author(s) Page
No
Non Academic Paper 453
The AI Conveyor Belt Nilesh Gopali and Hasik Shetty 455
Work In Progress Papers 463
The Quest for Explainable AI and the Role of
Trust
Anne Gerdes 465
Using Lo-Fi Prototyping to Envision
Conversational Agents in Public Settings
Arnold Jan Quanjer, Antti Jylhä and Jos P. van
Leeuwen
469
Ethics in Artificial Intelligence: A Myth that
may Never be a Reality
Vishal Rana and Clarence Tan 474
The Impact of Artificial Intelligence on
University Cyber Security Programs
Char Sample, Cragin Shelton, Ian McAndrew
and Connie Justice
477
Late Submission 481
Conditions and Bases of Incorporation of
Artificial Intelligence into Czech School
Environment
Josef Malach and Tomas Javorcik 483

8. v
Preface
ECIAIR Preface
These proceedings represent the work of contributors to the European Conference on the Impact of Artificial
Intelligence and Robotics (ECIAIR 2019), hosted by EM-Normandie Business School on 31 October–1 November
2019. The Conference Chair is Paul Griffiths from EM-Normandie Business School and the Programme Chair is
Mitt Nowshade Kabir, from Trouvus, Canada.
The motivation to launch the European Conference on the Impact of AI and Robotics (ECIAIR) has come about
as a result of the maturing of artificial intelligence and robotics, particularly of the cognitive computing kind,
which is producing an unprecedented revolution in the role of work and professionals in society. So this
Conference aims to focus not so much on the technology itself, but rather its consequence on knowledge work
and society.
The opening keynote presentation is given by Dr Mariano E. Giménez, from the University of Strasbourg,
France on the topic of Hybrid Operating Rooms and Image-Guided Surgery, the way to Automation. Then an
afternoon screening of the Netflix Film "The Great Hacked" followed by a Q&A. The second day of the
conference will open with an address by Dr Mitt Nowshade Kabir, who will talk about Artificial General
Intelligence, Consciousness and the Future of AI. The conference will close on Friday with a Panel of
Philosophers: What limits should humans set on Artificial Intelligence?
With an initial submission of 104 abstracts, after the double blind, peer review process there are 44 Academic
research papers, 4 PhD research papers, 1 Masters Research papers, 4 non-academic papers and 4 work-in-
progress papers published in these Conference Proceedings. These papers represent research from Australia,
Belarus, Canada, China, Denmark, Finland, France, Germany, India, Iran, Iraq, Italy, Japan, Lithuania,
Netherlands, Norway, Poland, Portugal, Romania, Russia, South Africa, Spain, Sweden, Taiwan, Thailand, UAE,
UK and USA.
We hope you enjoy the conference.
Paul Griffiths
EM-Normandie Business School
Oxford, UK October 2019

9. vi
ECIAIR Conference Committee
Dr Kareem Kamal A.Ghany, Beni-Suef University , Egypt; Prof Azween Abdullah, Taylors University, Malaysia;
Prof Esma Aimeur, University of Montréal, Canada; Dr Kinaz Al Aytouni, Arab International University, Syria; Dr
Hanadi AL-Mubaraki, Kuwait University, Kuwait; Prof Hamid Alasadi, Iraq University college, Iraq; Prof Laurice
Alexandre, Sorbonne Paris Cité , France; Dr José Álvarez-García, University of Extremadura, Spain; Dr Xiomi An,
Renmin University of China, China; Prof Antonios Andreatos, Hellenic Air Force Academy, Greece; Prof
Leonidas Anthopoulos, University of Applied Science of Thessaly,, Greece; Prof Oscar Arias Londono,
Institucion Universitaria de Envigado, Colombia; Dr Gil Ariely, Interdisciplinary Center, Herzliya, Israel; Dr
Sotiris Avgousti, Cyprus University Of Technology, Cyprus; Prof Rosalina Babo, Polytechnic of Porto, Porto
Accounting and Business School, Portugal; Prof Liz Bacon, Abertay University, UK; Prof Yoann Bazin, EM
Normandie, UK; Prof Turksel Bensghir, Ankara Hacı Bayram Veli University, Turkey; MSc Jordi Bieger, TU Delft,
Netherlands; Prof Elias Carayannis, George Washington University, USA; Prof Davide Carneiro, Polytechnic
Institute of Porto, Portugal; Prof Karen Cham, University of Brighton, UK; Prof Jim Chen, U.S. National Defense
University, U.S.A.; Dr Pericles Cheng, European University Cyprus, Cyprus; Prof Koteshwar Chirumalla,
Malardalen University, Sweden; Mr. David Comiskey, Ulster University, UK; Dr Leonardo Costa, Universidade
Católica Portuguesa - Católica Porto Business School, Portugal; Prof Carmen-Eugenia Costea, The Bucharest
University of Economic Studies, Romania; Prof Larry Crockett, Augsburg University, USA; Dr Marija Cubric,
University of Hertfordshire, UK; BA, M.sc., JD, Ph.D Candidate Francesca Dal Mas, Università di
Udine/Università la Sapienza, Italy; Assc Ben Daniel, University of Otago, New Zealand; Prof Justine Daramola,
Cape Peninsula University of Technology, South Africa; Geoffrey Darnton, WMG, University of Warwick, UK; Mr
Martin De Bonis, Alma Mater Studiorum, Italy; Prof Armando Carlos de Pina Filho, Federal University of Rio de
Janeiro (UFRJ), Brazil; Dr Martin De Saulles, University of Brighton, UK; Dr María de la Cruz Del Río-Rama,
University of Vigo, Spain; Dr Souâd Demigha, CRI Univ of Paris 1 La Sorbonne, France; Paolo Di Muro,
Politecnico di Milano School of Management, Italy; Dr Mihaela Diaconu, ”Gheorghe Asachi” University of Iasi,
Romania; Inês Domingues, DEIS-ISEC, Portugal; Dr Patricio Domingues, Polytechnic Institute of Leiria, Portugal;
Prof Yanqing Duan, University of Bedfordshire, UK; Prof. John Edwards, Aston Business School, UK; Dr Kelechi
Ekuma, The Global Development Institute, University of Manchester, UK; Dr Scott Erickson, Ithaca College-
School of Business, USA; Dr José Esteves, IE Business School, Spain; PhD Fernanda Faini, CIRSFID - University of
Bologna, Italy; Dr Georgios Fessakis, University of the Aegean, Greece; Prof Eric Filiol, ESIEA - CVO Lab, France;
Dr Panagiotis Fotaris, University of Brighton, UK; Prof Andreas Giannakoulopoulos, Ionian University, Greece;
Dr Amol Gore, UAE Government HCT, UAE; Dr Paul Griffiths, Ecole de Management de Normandie, Oxford, UK;
Dr hossein hakimpour, IAU , Iran; Prof William Halal, George Washington University, USA; Prof A Hessami, City
University London, UK; Dr Grant Howard, University of South Africa (Unisa), South Africa; Prof Ulrike Hugl,
Innsbruck University, Faculty of Business and Management, Department of Accounting, Auditing and Taxation,
Austria; Prof Hamid Jahankhani, Northumbria University London, UK; Dr Aman Jatain, Amity University, India;
Dr Runa Jesmin, University of Roehampton, UK; Dr Jari Jussila, Häme University of Applied Sciences, Finland; Dr
Selvi Kannan, Victoria University, Australia; Prof Ergina Kavallieratou, University of the Aegean, Greece; Dr
Harri Ketamo, Headai ltd, Finland; Dr Nasrullah Khilji, University of Bedfordshire, UK; Prof Tatiana Khvatova,
Peter the Great St. Petersburg Polytechnic Universtity, Russia; Prof Jesuk Ko, Universidad Mayor de San
Andres, Bolivia; Prof Michael Kohlegger, Institute for Web Technologies & Applications, Austria; Assc Ahmet
Koltuksuz, Yasar University, Turkey; Prof Renata Korsakiene, Vilnius Gediminas Technical University, Lithuania;
Prof Ibrahim Krasniqi, University of Peja, Kosovo; Prof Konstadinos Kutsikos, Business School, University of the
Aegean, Greece; Dr Jean Lai, Hong Kong Baptist University, Hong Kong; Dr Isah Abdullahi Lawal, Noroff
University College, Norway; Dr Ramona-Diana Leon, National University of Political Science and Public
Administration , Romania; Dr Efstratios Livanis, University of Macedonia, Greece; Prof Eurico Lopes, Instituto
Politécnico de Castelo Branco, Portugal; Dr Martin Magdin, Constantine the Philosopher University in Nitra,
Faculty of Natural Sciences, Department of Informatics , Slovakia; Dr Paolo Magrassi, Alephuture, Switzerland;
Dr Hossein Malekinezhad, Islamic Azad University, Naragh Branch, Iran; Dr Mary Manjikian, Regent University,
USA; Prof António Martins, Universidade Aberta, Portugal; Prof Maurizio Massaro, Ca' Foscari University of
Venice, Italy; Dr Nuno Melão, Polytechnic Institute of Viseu, Portugal; Prof Anabela Mesquita, Polytechnic of
Porto, Portugal; Prof David Methé, Institute of Business and Accounting, Japan; Dr Larisa Mihoreanu, ANMDM
Bucharest, Romania; Dr Clemente Minonne, Lucerne University of Applied Sciences, Institute for Innovation
and Technology Management, Switzerland; Prof Harekrishna Misra, Institute of Rural Management Anand ,
India; Assc Ludmila Mladkova, University of Economics Prague, Faculty of Business Administration, Czech
Republic; Dr Artur Modliński, University of Łódź - Faculty of Management, Poland; Prof Fernando Moreira,
Universidade Portucalense, Portugal; Prof John Morison, Queen's University Belfast , UK; Dr Rabeh Morrar,

10. vii
Northumbria University, UK; Dr Hafizi Muhamad Ali, Yanbu University College, Saudi Arabia; Dr Antonio
Muñoz, Universidad de Málaga, Spain; Dr Antoinette Muntjewerff, University of Amsterdam Faculty of Law,
The Netherlands; Prof Mihaela Muresan, Dimitrie Cantemir Christian University, Romania; Dr Minoru
Nakayama, Tokyo Institute of Technology, Japan; Dr Tomasz Niedziółka, Collegium of Business Admin, Poland;
Prof Roger Nkambou, University of Quebec at Montreal, Canada; Dr MItt Nowshade Kabir, Trouvus, USA; Prof
Beatrice Orlando, Sapienza University of Rome, Italy; Prof Evgeny Pashentsev, Diplomatic Academy at the
Ministry of Foreign Affairs of the Russian Federation, Russia; Assc Corina Pelau, Bucharest University of
Economic Studies,, Romania; Dr Parag Pendharkar, Penn State Harrisburg , USA; Dr Alexander Pfeiffer, Applied
Game Studies, Austria; Dr Michael Pitts, Virginia Commonwealth University , USA; Dr Cosmin Popa, Innovative
Agricultural Services, UK; Prof Ricardo Queirós, ESMAD/P.Porto, Portugal; Prof Carlos Rabadão, Politechnic of
Leiria, Portugal; Prof Thierry Rayna, École Polytechnique, France; Assc Liana Razmerita, Copenhagen Business
School, Denmark; Dr Marcin Relich, University of Zielona Gora, Poland; Prof José Carlos Ribeiro, Polytechnic
Institute of Leiria, Portugal; Prof Sandra Ribeiro, ISCAP, IPP-Porto, Portugal; Dr Martin Rich, Cass Business
School, UK; Dr Kenneth Rogerson, Sanford School of Public Policy, USA; Prof Göran Roos, University of South
Australia, Australia; Dr Eleni Rossiou, Experimental School of Aristotle University , Greece; Prof Neil Rowe, U.S.
Naval Postgraduate School, USA; Dr Melissa SAADOUN, EIVP, France; Prof Lili Saghafi, MSMU, USA; Prof
Mustafa Sagsan, Near East University, Turkish Rebuplic of Northern Cyprus; Prof Abdel-Badeeh Salem, Faculty
of Computer and Information Sciences, Ain Shams University, Cairo, Egypt; Dr Char Sample, US Army Research
Laboratory, USA; Dr navjot Sandhu, birmingham city university, united kingdom; Prof Vitor Santos, NOVA IMS -
New University of Lisbon, Portugla; Dr Daniyar Sapargaliyev, Almaty Management University, Kazakhstan; Prof
Ramanamurthy Saripalli, Pragati Engineering College, India; Prof Amitrajit Sarkar, Ara Institute of Canterbury,
New Zealand; Prof Markus Schatten, Faculty of Organization and Informatics, University of Zagreb, Croatia; Dr
Elena Serova, National Research University Higher School of Economics , Russia; Assistant Professor Sandro
Serpa, Universidade dos Açores, Portugal; Dr Armin Shams, Sharif University of Technology, Iran; Dr Yilun
Shang, Northumbria University, UK; Prof Marina Shilina, Plekhanov Russian University of Economics, Russia; Dr
Eric Shiu, University of Birmingham, UK; Prof Fernando Silva, Polytechnic Institute of Leiria, Portugal; Prof
Andrzej Sobczak, Warsaw School of Economics, Poland; Dr Caroline Stockman, University of Winchester,
United Kingdom; Dr Darijus Strasunskas, Hemit, Norway; Dr Olga Strikulienė, Kaunas University of Technology,
Lithuania; Dr Marta-Christina Suciu, Bucharest University of Economic Studies, România; Dr Saloomeh Tabari,
Sheffield Hallam University, UK; Prof Ramayah Thurasamy, Universiti Sains Malaysia, Malaysia; Assc Milan
Todorovic, Royal Melbourne Institute of Technology, Australia; Prof Jim Torresen, University of Oslo, Norway;
Dr Mariza Tsakalerou, Nazarbayev University, Kazakhstan; Dr Changiz Valmohammadi, School of Business and
Law, Taylor's University, Malaysia; Dr khan ferdous wahid, Airbus, Germany; Prof Fang Wang, Nankai
University, China; Prof Murdoch Watney, University of Johannesburg, South Africa; Prof Bruce Watson,
Stellenbosch University, South Africa; Dr Santoso Wibowo, Central Queensland University, Australia; Prof
Robert J. Wierzbicki, University of Applied Sciences Mittweida, Deutschland; Dr Marcus Winter, University of
Brighton, UK; Dr Adam Wong, School of Professional Education & Executive Development, The Hong Kong
Polytechnic University, Hong Kong; Mr Yoke Seng Wong, KDU University College, Malaysia; Mr Tuan Yu, Kent
Business School, UK; Dr Norshuhani Zamin, Universiti Sains Islam Malaysia (USIM), Malaysia; Prof Daiva
Zostautiene, Kaunas University of Technology, Lithuania;

11. viii
Biographies
Conference and Programme Chairs
Dr Paul Griffiths BSc, MEng, DBA, A.Dip.C is Professor of Banking, Finance and Fintech and
Academic Director of the MSc in Banking and Fintech at the Ecole de Management de
Normandie and is based at the UK Campus in Oxford. Prior to becoming a full-time
academic Paul spent many years in leadership positions at global management consulting
firms, serving Boards of blue-chip companies, particularly in the financial services sector.
He specialises in the management of intangible assets such as intellectual capital and
artificial intelligence. He helps organisations and industry sectors set up knowledge
networks on technological platforms such as cognitive computing, augmented reality and blockchain. Having
lived in nine and worked in 15 countries he defines himself as multicultural.
Dr. Mitt Nowshade Kabir is a serial entrepreneur, AI evangelist, a business accelerator
and investor. He is the present CEO of Trouvus, an AI company that develops state-of-
the-art recommender systems based on latest machine learning technologies. He holds
an M.Sc. in Computer Science, an MBA from Georgetown University, a Ph.D in
Information Technology, and Doctorate in Business Administration from Newcastle
University, UK. His present interests are strong AI, innovation, knowledge management,
semantic technologies, Entrepreneurship, and Strategy. Dr. Kabir is also a teacher,
inventor, and writer. He has published many articles, peer-reviewed papers and book chapters in his areas of
interest. As an academic, he developed and taught a course on Applied Artificial Intelligence and Knowledge
Entrepreneurship. His book “Knowledge-based Social Entrepreneurship” is due to be published in early 2019
by Palgrave Macmillan.
Keynote Speaker
Dr Mariano E. Giménez is Professor of Surgery and “Taquini” Chair of General and
Minimally Invasive Surgery at the University of Buenos Aires, and Director of the DAICIM
Foundation in Argentina, which is dedicated to Teaching, Treatment and Research into
Interventional Radiology and Minimally Invasive Surgery. He is also Scientific Director of
Percutaneous Surgery at IHU-IRCAD at the University of Strasbourg, France and Chair of
Excellence in Percutaneous Surgery at its Institute for Advanced Study (USIAS) at the
University of Strasbourg, France and his main practice focuses on Percutaneous Image
Guided Surgery and Minimally Invasive Hepato-Pancreato-Biliary (HPB) Surgery. Dr. Giménez completed his
PhD in 1996 at the University of Buenos Aires. He is a prolific member of the academic community and has
published more than 200 scientific papers, 10 books on Surgery and written over 85 book chapters. Dr.
Giménez has, in recent years, carried out humanitarian surgical missions in Kurdistan, Guatemala, Honduras,
Bolivia and Peru.
Mini Track Chairs
Francesca Dal Mas, BA, MBA, JD, Ph.D., works as an independent consultant in the field of
strategic management and knowledge management. She is a member of Women in AI.
She is affiliated to La Sapienza University of Rome, Italy.
Dr Maurizio Massaro, Ph.D., is aggregate professor at Udine University since 2008, having
worked as teacher at Udine University since 2001. He was visiting scholar at the FGCU,
Florida, USA, in 2010 and Leicester, UK, 2013. His academic interests are primarily in the
field of business performance measureme nt, intellectual capital, knowledge management
and entrepreneurship.

12. ix
Evgeny N. Pashentsev, DSc Is a leading Researcher in the Diplomatic Academy at the
Ministry of Foreign Affairs of the Russian Federation, senior researcher at Saint Petersburg
State University. He is also a Professor at the Lomonosov Moscow State University and a
Director at the International Centre for Social and Political Studies and Consulting. He is a
member of the international Advisory Board of Comunicar (Spain) and is on the editorial
board of The Journal of Political Marketing (USA). Evgeny is author, co-author and editor
of 36 books and more than 150 academic articles published in Russian, English, Spanish,
Portuguese, Italian, Serbian and Bulgarian languages. He has presented papers at more
than 180 international conferences and seminars for the last 20 years in 19 countries. His
areas of research interest include strategic communication, international security, perspective technologies
and social development.
Dr. Char Sample is a researcher for ICF Inc. and a visiting academic at the University of
Warwick. Her research focus areas are Threat Intelligence, Fake News, Data Resilience
and cultural influences on cyber events and behaviors. Dr. Sample’s background includes
time in private sector, public sector and academia. She continues to try to merge the
best features from each of these areas to guide her research.
Dr Caroline Stockman is an experienced Senior Lecturer with a demonstrated history of
working in higher education and the commercial e-learning industry. She is currently a
Senior Lecturer at the University of Winchester, UK, and a Research Fellow at the Institute
of Cultural Studies at the University of Leuven, Belgium. As a Fellow of the Higher
Education Academy, and a JISC Digital Leader, she is a recognised expert on effective
human-technology relationships. She has presented her work at MIT and BCS - the
Chartered Institute of IT, and published her most recent monograph on technology
acceptance with Routledge.
Biographies of Contributing Authors
Dr Mamun Ala teaches international business and strategic management at the Australian Institute of
Business (AIB). He has also taught various postgraduate and undergraduate courses in Flinders University and
University of South Australia. Mamun is an active academic researcher. His current research interests include
economics, management, international business, political economy and education.
Arije Antinori is a Professor of Criminology and Sociology of Deviance at “Sapienza” University of Rome - Dept.
of Communication and Social Research. EU Senior Expert on Terrorism and Organized Crime (EENeT, ECTC-AN,
CEPOL, EUROMED), Geopolitics and OSINT Analyst, Social Media and Stratcom Expert. Ph.D in “Criminology
applied to Investigations and Security” and Ph.D in “Communication and Media Studies”.
Alexey Bataev is an associate professor of economics and information technologies at Peter the Great St.
Petersburg Polytechnic University, Russia. He received his PhD in technical sciences from St. Petersburg
Polytechnic University in 1996. His main research areas are innovations, information technology management
and cloud computing.
Mona Bokharaei Nia is a PhD Candidate in IT Management. Her research interests are human interaction
services and data sciences. She received her MBA from UT and M.Sc. in IT e-Commerce Engineering from AUT
and BSc degree in Engineering from UT. She has more than 12 years experience in innovative digital services &
solutions design.
Romeo Botes studied at the North-West University and completed his Honours and Master’s Degree in
Computer Science, fulfilling various technological roles and helping him gain practical experience including
being a Programmer, a Database Administrator, and a researcher. As a confident individual he portrays a

13. x
positive attitude while also enjoying a balanced lifestyle including sports such as swimming, cycling, hiking and
anything else adventurous.
Karl Joachim Breunig is a Full Professor of Strategic Management at the Oslo Business School, Oslo
Metropolitan University – OsloMet, where he is heading the research group on Digital Innovation and Strategic
Competence in Organizations (DISCO). He received his Ph.D. from BI Norwegian Business School, and holds a
MSc from London School of Economics. Prof. Breunig’s research concentrates on the interception of strategy-
and innovation theory, and involves topics such as service- and business model innovation as well as
digitalization in knowledge intensive firms.
Dr. Jim Q. Chen, Ph.D. is Professor of Cyber Studies in the College of Information and Cyberspace (CIC) at the
U.S. National Defense University (NDU). His expertise is in cyber warfare, cyber deterrence, cyber strategy,
cybersecurity technology, artificial intelligence, and machine learning. Based on his research, he has authored
and published numerous peer-reviewed papers, articles, and book chapters on these topics. Dr. Chen has also
been teaching graduate courses on these topics. He is a recognized expert in cyber studies and artificial
intelligence.
Larry J. Crockett is Professor of Computer Science and Philosophy at Augsburg University, Minneapolis, MN,
USA. He took his Ph.D. from the University of Minnesota and has published three books, including Universal
Assembly Language (McGraw-Hill) and The Turing Test and the Frame Problem (Ablex), a philosophical critique
of AI.
Souâd Demigha is a Doctor in Computer Science from the University of Paris1-Sorbonne. She is a researcher at
CRI ( Sorbonne-University) and Lecturer at the University of Paris XI. Her Research deals with: Information
Systems, Medical Imaging, eLearning, Knowledge Management, Big Data, Data Mining. She is the author or co-
author of 50 international scientific papers.
Chuck Easttom is the author of 26 books, approximately 50 scientific papers, and an inventor with 16 patents.
He is an adjunct professor for Capitol Technology University. He is also a Senior Member of both the IEEE and
ACM and a Distinguished Speaker of the ACM.
John S. Edwards is Professor of Knowledge Management at Aston Business School, Birmingham, U.K. Research
interests include how knowledge affects risk management; knowledge management strategy and
implementation; and its synergy with analytics and big data. He has published over 75 research articles and
three books. He is consulting editor of the journal Knowledge Management Research & Practice.
Anne Gerdes is an Associate Professor at the Department of Design and Communication, University of
Southern Denmark. Her research focuses on ethical issues related to artificial intelligence, machine ethics,
robot ethics, value-sensitive design, and privacy. She is engaged in the AI ethics research community, and she
is experienced working in cross-disciplinary fields with computer scientists.
Nilesh Gopali has over two decades of successful experience, Nilesh's expertise goes well beyond strategy, all
the way into execution & success. Nilesh’s considerable expertise in both the UK and India markets has
enabled him to be a market-entry-specialist and a speaker in the emerging technology industry. Currently sits
on the CII National Steering Committee for AI
Maija Haaranen is a Senior lecturer of Management at the JAMK University of Applied Sciences in Jyväskylä,
Finland. Haaranen has a status of a Specialist in many Leadership Projects by JAMK Business School and its
partner network. Her expertise covers topics of Management, Leadership, HR and Organizational
Communication, as well as RPA and Entrepreneurship.
Niklas Humble is a PhD student at the Department of Computer and System Science at Mid Sweden University.
Ion A. Iftimie is an independent consultant and former United States Cyber Command Information Warfare
Deputy Chief. He is a doctoral candidate in Vienna, Austria and holds a Bachelor of Business Administration in
International Business from the George Washington University in Washington D.C., a Master of Arts (M.A.) in
Strategic Security from the National Defense University in Washington D.C., and a M.A. in International

14. xi
Security from Bundeswehr University in Munich, Germany. He is also a recent graduate of the Harvard
Kennedy School Executive Program in Cybersecurity Policies and of the Swedish Defense University.
Dr. Brigita Janiūnaitė is a full professor and Principal investigator at Faculty of Social Sciences, Arts and
Humanities in Kaunas University of Technology, Lithuania. She has PhD in Social sciences. She is an expert of
the European Science Foundation; Lithuanian Science Council, etc. Her research is focused on the issues of
change management and social innovation; development of innovative culture at individual and organizational
level; higher education; curriculum development.
Dr Mariusz Kakolewicz is a senior lecturer of media in education at A. Mickiewicz University in Poznan/Poland.
He received his PhD in pedagogy from AMU in 1996. He has also M.E. in telecommunication. He is an author of
two monographs and 100 other publications. His main research areas are AI, media, cognitive science in
education.
Professor Tatiana Khvatova, Dr. of Science in the field of Management, PhD in Applied Sciences, SPbPU (Peter
the Great Saint-Petersburg Polytechnic University, Russia). Currently employed as a Professor for the Graduate
School of Management and Business of SPbPU and visiting professor at IDRAC (France), Haaga-Helia
Universities of Applied Science (Finland), EDC-Paris (France). The present research is focused on knowledge
management, business models in education, innovation policies, and innovation systems. Tatiana teaches
Mathematical Methods, Innovation Management, Risk Management.
Dr John Kingston is a Senior Lecturer in Cyber Security at Nottingham Trent University. He has 25 years’
experience of artificial intelligence based on human knowledge. He also has qualifications in law. The focus of
his research is on projects requiring at least two of cyber security, artificial intelligence and law.
Dr Vali Lalioti is Senior Research Tutor at the Royal College of Art (RCA), UK. She has a PhD in Computer
Science, University of Manchester, an MBA and MRes in Design, RCA. Her research explores how technology
interacts with society, focusing on the emerging space where AI and xR meet in the Future of Work.
Dr David López in an academic collaborator at ESADE Business School. He holds a PhD in digital
transformation, and a joint MBA from ESADE (Spain) and Duke University (USA). He is also an entrepreneur
and investor in the digital sector, he has founded a digital consulting firm that employs more than 180 people
and has participated and advised 8 hi-tech start-ups.
Juha Mattila is a PhD student from Aalto University, Helsinki, Finland. His thesis is about finding why so many
Command, Control, Communications, Computers and Information system development programs have failed.
He is an officer retired from Defence Forces Finland and currently consulting the Armed Forces of UAE.
Dr Konstantinos Mersinas, PhD, MSci, MSc, CISSP, is Director of the Distance Learning MSc Programme in
Information Security at Royal Holloway University of London. Konstatninos has worked in various information
security roles before moving from the industry to academia and has been teaching a wide range of information
security courses during the past decade. A trained mathematician, his research interests lie with behavioural
and experimetal economics in cybersecurity, decision-making, and cybercrime.
Dr Peter Mozelius is a PostDoc researcher at the Department of Computer and System Science at Mid Sweden
University.
Dr Yuko Murakami (Ph.D.(Philosophy), Indiana University) is a specially appointed professor of philosophy of
science at Rikkyo University, Japan, currently preparing a new graduate school of artificial intelligence in
charge of AI ethics to launch in April 2020. Her main research areas are philosophy of logic, philosophy of
science, and applied philosophy.
Prof. Evgeny Pashentsev is a leading researcher at the Diplomatic Academy of the Ministry of Foreign Affairs
of the Russian Federation and a senior researcher at the School of International Relations, Saint Petersburg
State University, Russia. Evgeny is an author of more than 150 publications on international security, AI and
psychological warfare issues.

15. xii
Dr. Menisha Patel is a research associate at the Department of Computer Science, University of Oxford. Her
research is at the intersection of the social sciences and computer science, particularly considering how we can
mitigate negative impacts of existing or future technological innovation on society.
Arnold Jan Quanjer MSc is researching how interactive technology is increasingly weaving itself into the fabric
of daily life and how artificial intelligence can be deployed in a responsible way is the main interest of Arnold
Jan Quanjer (1959). He holds an MSc in Media Technology from the University of Leiden and has extensive
experience as a UX designer both in a commercial and governmental setting. He works as lecturer and
researcher at The Hague University of Applied Sciences.
Vitali Ramanouski is PhD candidate in Politics (Diplomatic Academy of the Russian Ministry of Foreign Affairs,
specialization 23.00.04 – Political Problems of International Relations, Global and Regional Development). He
received Master’s Degree in International Relations (International Security) in the Diplomatic Academy of the
Russian Ministry of Foreign Affairs in 2017. He is a member of International Studies Association (ISA) and East
European Studies Association (CEEISA). His main research area is counter terrorism in the Middle East.
Vishal Rana is a Lecturer in Human Resource Management and Organizational Behaviour at the Australian
Institute of Business, Adelaide, Australia. Additionally, Vishal is the CEO & Co-Founder of an AI based mental
health start-up, Watchyourtalk, that is developing a SaaS based platform to detect early symptoms of
depression by monitoring speech in real time.
Olga Rivera-Hernáez is Professor of Strategy and Innovation at Deusto Business School. She has been Deputy
Minister for Quality , Research and Innovation Health of the Basque Government and President and member
of the Council at diverse leading research centers. She has been part of the MOC Network of Harvard
University, and has also worked on System Dynamics as MIT alumni.
Dr. Char Sample is research fellow for ICF Inc. at the US Army Research Laboratory in Maryland, and is a
visiting academic at the University of Warwick, UK. She has over 20 years experience in the information
security industry and focuses her research on Fake News, cultural values in cyber security events, and data
resilience.
Juha Saukkonen is a Senior lecturer of Management at the JAMK University of Applied Sciences in Jyväskylä,
Finland. Saukkonen has published both individually and in multinational teams in journals and conferences on
Foresight, Knowledge Management Anticipation and Entrepreneurial Learning and Education. He is also a
guest writer and lecturer in various universities and organizations abroad.
Dr Keith Scott is the Subject Leader for Languages at De Montfort University (UK). As a member of the UK All-
Party Parliamentary Group on Cyber Security and the independent Cyber Policy Centre, he pursues research on
online influence (and gaming as a modelling and training tool for examining influence) and the human/cultural
aspects of cyber.
Dr. Elena Serova is an academic working now in National Research University Higher School of Economics St.
Petersburg Branch. Her role combines teaching and research in equal measure. She is an Academic supervisor
and coordinator of Doctoral Schools of Economics and Management. Her research interests are related to
Business Analysis, Strategic Management, Marketing, and Information Management. She has co-authored the
books and collections of essays, regular key presenter at national and international conferences.
Hasik Shetty is an AI enthusiast and speaker who works as a Machine Learning Engineer at AAVOR. He has
done his Bachelors of Engineering from University of Mumbai. After completing more than a dozen of courses
and equal number of Projects in AI, Hasik now focuses at the intersection of AI, Finance and Business models.
Dr. Christina Suciu is professor & PhD supervisor at Bucharest University of Economic Studies, Romania She
received PhD in 1995. Since 2016 is Vice-Dean for RDI & International Relations, Faculty of Theoretical &

16. xiii
Applied Economics. She coordinated 8 grants & published 20 books & 150 papers. Topics: cultural & creative
economy, knowledge management, intellectual capital.
Vilma Sukackė is a PhD student of Educational Sciences at the Faculty of Social Sciences, Arts and Humanities
in Kaunas University of Technology, Lithuania. At the same University, she is also a lecturer and technical
editor of a scientific journal. She has been involved in organizing various national and international scientific
events. Her main research areas are in Educational innovation, Technology-enhanced learning, Foreign
language learning.
Mrs Yukimi Takahashi is a director of Media Education Centre at Sano Educational Foundation in Tokyo, Japan.
She is now a PhD candidate of Assumption University of Thailand. Her research interest includes eLearning,
Artificial Intelligence and machine learning.
Dr. Clarence Tan is an Adjunct Professor at Griffith University, Gold Coast, Australia in the Department of
Information and Technology. Dr Tan is a futurist and was the Asia Pacific Ambassador for Singularity University
(SU), a benefit corporation based on NASA’s research campus in Silicon Valley. Dr Tan holds several patents in
mobility technology globally.
Paulo Vieira is graduated in Pure Mathematics from the University of Porto, he has a master in Mathematics
from the University of Lisbon and a PhD in Computer Science from the University of Salamanca. He was
Monitor in the Department of Mathematics at the University of Porto and Assistant Professor at the ESTG of
the Polytechnic Institute of Guarda. As a teacher he worked and supervised several projects in the areas of
computer science, electronics, mathematics and artificial intelligence. Has several papers and communications
published. He currently is dedicated to research after passing through the software development industry. He
is working in C4 Cloud computing competence center at the University of Beira Interior (UBI)
Murdoch Watney is a professor at the University of Johannesburg, South Africa. She is Head of the
Department: Private Law. Murdoch is an NRF rated established researcher. She contributed to four textbooks
and has published on the law of criminal law, and cyber law and has delivered peer-reviewed papers at
national and international conferences.
Malte Wattenberg is a Research assistant at the "Denkfabrik Digitalisierte Arbeitswelt" at Bielefeld University
of Applied Sciences, Germany. His work and research focuses in particular on the requirements of industry 4.0
in companies as well as digital business models. Involved in teaching in the module "Communication and
Management Competencies" and in various modules of business informatics.
Dr Helena Webb is a Senior Researcher in the Department of Computer Science at the University of Oxford.
She received her PhD in Science, Technology and Society from the University of Nottingham in 2004. She is a
social scientist and draws on a variety of social research methods in the conduct of interdisciplinary projects.
Her main research area is human centred computing, and she is particularly interested in the ways that users
interact with technologies in different kinds of setting.
Colin Williams is Director Software Box Ltd. Visiting Professor De Montfort University. Honorary Fellow
University of Warwick. After a quarter of a century in the realms of public sector enterprise IT, information
assurance and cyber security, Colin has concluded that it might after all be possible for a refugee historian to
impersonate someone with a vague semblance of technical knowledge ... or perhaps not!
Richard L. Wilson is a Professor of Philosophy at Towson University in Towson, MD. Teaching Ethics in the
Philosophy and Computer and Information Sciences departments and Senior Research Fellow in the
Hoffberger Center for Professional Ethics at the University of Baltimore.
Jose Miguel Zaldo-Santamaría, is doctoral student at Deusto Business School, but previously has been
General Manager, President, Member of the Board in several companies, not only in Spain but also in North
Africa, mostly in Morocco, Algeria and Tunisia.Since the year 1996 has been teaching at Deusto Business
School in International Management, writing two books about. He has always be worried about

17. xiv
unemployment, writing also two books. His doctoral dissertation relates Robotics and Artificial Intelligence
with the Employmens, developing a model of System Dynamics.
Me Malie Zeeman is a lecturer in Computer Science at the North-West University, South Africa. She received
her MSc degree in 2015 from North-West University, South Africa and is a PhD student in Computer Science at
the same university. Her main research areas are computer programming, serious games and robotics in
higher education.

18. Toward Understanding the Impact of Artificial Intelligence on
Education: An Empirical Research in Japan
Yukimi Takahashi and Poonsri Vate-U-Lan
Graduate School of Advanced Technology Management, Assumption University of
Thailand, Bangkok, Thailand
Imaoka.yukimi@gmail.com
poonsri.vate@gmail.com
DOI: 10.34190/ECIAIR.19.091
Abstract: This study aimed to explore the impact of using an artificial intelligence (AI) application to practice English-
speaking skills from the users’ points of view. This empirical study took place at a vocational school in Tokyo, and was
conducted using an existing AI application. The population was assembled from a group of more than 1,229 freshman
students from a private, two-year vocational institute in Tokyo, Japan. The gender ratio of the population was 30% male to
70% female. The participants in this study consisted of 20 purposively selected freshman students studying English as a
foreign language through an AI application, chosen for their level of English proficiency. This group of students was at a
lower level of English proficiency according to the profiles available from the institute. It is essential to study this particular
group of participants because significant findings from a previous study, carried out in the context of this research,
confirmed that students at a lower level of proficiency expressed a severe lack of confidence in their English-speaking skills.
In order to avoid confounding effects, this research focused only on those students who exhibited lower levels of
proficiency. The research findings from in-depth interviews suggest that the AI application is a useful tool for practising
English but also indicate that there are too many gaps in the current application. Areas for improvement can be classified
into five components of AI machine learning: data mining, deep learning, supervised learning, unsupervised learning, and
reinforcement learning. The research findings elaborated on the functions of each machine learning component in order to
improve the application and eliminate future errors. The outcome of this study revealed how AI applications perform
accurate feedback, such as the pronunciation of words and phrases, correcting grammar, and speed of speech. This
research offers new insight into understanding the mechanism of AI in education and will be useful in preventing future
errors.
Keywords: AI in education, English as a second language, English speaking skill, high score, machine learning, vocational
school
1. Introduction
The incorporation of AI technology into education is becoming commonplace. Many educational applications
and services that incorporate AI have become available in the market (Halimah, 2018). However, the specific
aspect of machine learning AI that can be used to improve second-language acquisition has not been clarified.
Japan is considered to be an English as a second language (ESL) environment, in which the nation does not use
English in everyday life. In such an environment, it is challenging to improve English proficiency due to the
minimal number of engagements in English. In order to improve English proficiency, a review of the literature
has shown that increasing the frequency of engagements in English will improve English proficiency (Hatou,
2006). Japan will be hosting the 2020 Olympic and Paralympic Games. Since the decision, the number of
inbound people has increased each year, and, according to the announcement of the Japan National Tourism
Organization (Japan National Tourism Organization, 2019), the number of foreign visitors to Japan in April 2019
reached the highest record. While foreign language demand is increasing rapidly, many people lack such
proficiency. A self-assessment questionnaire on English proficiency at a private vocational institute in Tokyo
revealed a severe lack of self-confidence, especially in what is regarded as speaking abilities.
At the selected vocational school in Tokyo, the AI eLearning application has been integrated into the
curriculum. It aimed to provide personal training for each student to improve English proficiency outside the
class by increasing engagement through English conversation lessons. The AI eLearning application implements
a voice chatbot with speech syntheses and voice recognition technology. Lessons have been prepared for
students to practice English conversation in various scenes, such as at the airport, hotel, and shopping mall.
Each lesson contains various functions to support conversation skills, such as speaking, dictation, vocabulary,
pronunciation, personalization and learning progress measurement. When students practice dialogue through
role-playing in this application, a portion of the student's voice is analyzed at the back end, and the AI gives
433

19. Yukimi Takahashi and Poonsri Vate-U-Lan
feedback. It is ideal for training as it generates a drill that collects the students' poor pronunciation, learning
activity, and voice data, estimating English proficiency, and providing feedback to teaching materials and task
data. The critical part is inferring English proficiency in order to personalize the training.
Practicing speaking requires at least takes two people to conduct, making it problematic. The AI is expected to
become a personal coach in speaking practice and provide support beyond traditional materials, such as paper
and video. The manual approach of teaching conversational English will be realized in all contents of the
dialogue available. However, since dialogue in a real situation might vary and depend on many circumstances,
the machine-learning approach in AI, with integrated technology capabilities, should offer a significant
improvement to the way conversations are practiced.
In theory, AI represents a complicated computer system endowed with the intellectual processes and
characteristics of humans. In practice, AI for education is still new and needs to be improved, particularly in the
technical, social, scientific, and conceptual limits of its performance (Tuomi, 2018).
2. Problem statement
In order to determine how well the new technology performs, this AI eLearning application needs to be
evaluated to see its impacts on practising to speak the English language. Therefore, an in-depth interview was
employed, as it can provide more profound insights and rich detail from users' experiences. According to the
interview, results confirmed a gap for improvement. It is apparent how important it is to recognize the gaps for
improvement and upgrade the application accordingly. By continuing to use the incorrectly working
application, students will become frustrated and eventually not use it. This study will define the problem by
categorizing the gap into five groups, which will be described, in detail, later in this paper, and explain how the
AI eLearning application affects practising conversational English.
3. Research objective
The general objective of this study was to explore the impact of AI eLearning applications have on practising
English-speaking skills for freshmen at a selected vocational institute in Tokyo, Japan. The specific research
objective aimed to:
1. Identify improvement areas after using the AI eLearning application to practice English-speaking skills;
2. Classify improvement areas into the five components of machine-learning under AI: data mining, deep
learning, supervised learning, unsupervised learning, and reinforcement learning;
3. Propose an approach to debug the problems of each AI component.
4. Research question
The general research question for this paper was: What impacts the practise of speaking English when using
the AI eLearning application at the selected vocational institute in Tokyo, Japan? From this question, the
following three specific research questions were generated:
1. What list of areas that require improvement was generated from the in-depth interviews?
2. What areas of improvement were classified into the five components of AI machine learning: data
mining, deep learning, supervised learning, unsupervised learning, and reinforcement learning?
3. Which AI approach can debug the problem of each component?
5. Literature review
This section describes the machine learning categories that the researcher will define to improve AI.
5.1 Machine learning and essential components of AI for education
Machine Learning is the way in which computers are able to learn by themselves using large data sets instead
of inputting all codes manually. This type of learning takes advantage of the processing power of modern
computers, which can efficiently process large data sets. Revolutionizing machine learning is essential,
particularly in education, since its purpose is to increase efficiency by completing tasks such as scheduling,
classroom management, learning analytics, and predictive analytics. The primary role of machine learning
could support the learning process and prevent students from dropping out (Popenici & Kerr, 2017). Machine
learning covers several techniques, such as adaptive learning that comprises personalized learning. To improve
AI interaction in this current research, five categories of machine learning: data mining, deep learning,
supervised learning, unsupervised learning, and reinforcement learning will be focused (Ciolacu et al., 2018).
434

20. Yukimi Takahashi and Poonsri Vate-U-Lan
5.2 Data mining
Data mining is about extrapolating patterns and new knowledge from the data that already exists. There are
seven data mining techniques: pattern tracking, classification, association, outlier detection, clustering,
regression, and prediction (Alton, 2017). For English language education, data mining is the practice of
examining large databases of English conversation content in order to generate many courses or other word
interactions with students. Tracking patterns is beneficial for learning about the errors that students tend to
make. The association technique is related to tracking patterns but is more specific to dependently linked
variables (Luckinn et al., 2016). This technique is valuable for defining mother tongue-related error tendencies.
There is a tendency for the mother tongue to influence grammatical and pronunciation errors in English
proficiency. Therefore, the association technique could define the mistake derived from the mother tongue.
5.3 Deep learning
Deep Learning is a machine learning method that aims to train an AI to predict outputs, given a set of inputs.
The “deep” in deep learning refers to having more than one hidden layer, or neurons in the input layer, which
are working together to predict or provide a correct answer. The technical problem of deep learning operation
is the demand for big data as an input in order to generate correct output (Karsenti, 2019). The problem of
deep learning can be solved by coding it to function from fewer data and building smaller models since the
number of errors might not be apparent or distinct enough. Thus, the deep-learning function needs to scale
down to the existing data. Natural Language Processing (NLP) by deep learning is becoming important due to
their demonstrated success at tackling complex learning problems (Lopez & Kalita, 2017). The NLP can
organize and structure knowledge to perform tasks such as automatic summarization, translation, named
entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation
(Lopez & Kalita, 2017).
5.4 Supervised learning
Machine-learning processes, such as classification or prediction, are categorized as supervised learning.
For example, inferring the pattern of English conversation in asking for direction, the interactions between
persons will be based on questions and answers according to English grammar; this is clustered as supervised
learning. Supervised AI learning algorithms are based on historical data or on teaching English. Supervised
learning will be based on the textbook; supervised learning can only see English conversations as a repetition
of the contents of the textbooks (Tuomi, 2018). Logic-based and knowledge-based AI approaches can solve
errors in supervised learning.
5.5 Unsupervised learning
Unsupervised learning is about learning data structures. Unlike supervised learning, it perceives patterns
within unlabelled data by assembling elements of data or deducing density functions based on similarities
between input data (Tuomi, 2018). From a practical point of view, this type of learning is used for spam filters
and the like. With regards to the English learning process, categorizing into groups (such as stress accent, pitch
accent, and intonation) is unsupervised learning. Unsupervised learning issues can be solved through the
effective decision-making AI approach.
5.6 Reinforcement learning
Reinforcement is a paradigm for learning by trial and error that is inspired by the way humans learn new tasks
(Nathan, 2017). Unlike in supervised learning, no teacher explicitly indicates the correct action output for a
stated input. Instead, learning is based on scalar information called rewards; however, there are noises and
delays in rewards (Cuayáhuitl, 2017). A reinforcement agent must find optimal strategies for accruing rewards
while exploiting the best strategy it has found to achieve the desired goal (Andrychowicz el al., 2017).
Therefore, it is difficult for the learning subject to judge whether an action is correct or not by just looking at
the reward immediately after executing the action (Zheng et al., 2018). For example, if a non-native English
speaker always mispronounces a syllable or uses the wrong tone when speaking English, the reinforcement
learning function will only work to solve such problems when the student repeats the error. There are many
ways in which AI can help to improve this problem, for example, by providing more exercises to help the
student overcome that particular error. During the English-learning process, the students’ gender, age, or
435

21. Yukimi Takahashi and Poonsri Vate-U-Lan
seniority—as well as the context, time, and place—might be crucial factors to consider when applying
reinforcement learning. The shortcomings of reinforcement learning can solve through adaptive and
personalized learning.
6. Research methodology
This research was qualitative, incorporating an in-depth interview technique. It aimed to explore the
application problems or errors by conducting an in-depth interview while delving into the participant’s
experiences, everyday behaviour, and thoughts.
6.1 Participants
The study included 1,229 freshman students at the selected private vocational institute in Tokyo, Japan. The
gender ratio of the population was 30% male to 70% female, between the ages of 18 and 20. The 20
participants consisted of five males and fifteen females chosen from the population with a purposive sampling
method. They are all Japanese citizens and have no experience living overseas. They study English as a foreign
language and use an AI eLearning application as a part of their learning materials for their compulsory class:
English Foundation.
In this institute, in order to provide appropriate education to match the students’ proficiency level, the English
Foundation course divided students into upper and lower levels according to their English placement test
score. Upper- and lower-level courses use different materials in their English Foundation courses. In order to
avoid confounding effects, this research focused on a lower-level student group only. The participants were
the most frequent users of an AI eLearning application based on the profiles available at this selected institute.
6.2 Research instrument
An in-depth, semi-structured interview was employed as the primary research methodology instrument for
collecting data, which took place in May 2019 outside of class hours. Twenty participants were selected for this
qualitative study. In order to maintain the uniformity of the interview, one interviewer was in charge of all
interviews. The interviewer explained that this interview was for research purposes only, and it would not
affect their grades. Based on the participant’s availability, five groups of four participants were formed to
conduct this interview. Each interview took about one-and-a-half hours in a meeting room where all sat down
in a circle together with one interviewer. The participants had their iPads with the AI eLearning application
opened during the interview. Interview results were recorded in the form of video and used for qualitative
analysis.
To ensure consistency and completeness, the list of interview questions was prepared before the interview as
below.
 What is the expectation from the AI teacher that motivated them to practice English conversation
spontaneously?
 What kind of feedback or scoring annoyed them from performing better?
 What is the part of the application that motivates them to practice? Explain why?
 What is the part of the application that discourages them from practising? Explain why?
 What is missing in the application?
A specific interview script was not prepared in order to adapt to the character of each group in a timely
manner.
6.3 Data analysis
The data was analyzed after the interview, which aimed to understand the gap to improve the current AI
eLearning application and classified them into machine learning categories.
A thematic analysis was applied in this qualitative study. It aims to develop a better understanding of the
participants’ experiences with the application, and it relies on the participants’ own perspectives to provide
insight into their motivations. It focuses on the frequency of specific types of utterance, as indicated below.
 What is the gist of a participant’s comment?
 What is the unexpected idea that the participant has?
 How are the participants’ ideas different?
436

22. Yukimi Takahashi and Poonsri Vate-U-Lan
 What is a typical comment?
In-depth interview data is the result of interview responses, and it provides detailed records of actions,
attitudes, feelings, beliefs, relationships, and perceptions. Inevitably, there are problems with accuracy,
reliability, and validity. Understanding its nature, this study incorporated in-depth interviews because it was
important to gain insight into the respondents’ perceptions.
In order to mitigate bias as an interviewer, the researcher tried to listen closely, eagerly, actively, and without
prejudice during the interviews.
7. Research findings and discussion
Refer to the general research objective. Twenty freshman students participated in the in-depth interviews,
which aimed to investigate the gaps in the current eLearning application along with the nature, use, and
potential benefits of the AI eLearning application for ESL students. It has been found that the main problem
lies with the “process” of AI and machine learning generating. As such, the current result and discussion focus
on those aspects related to the original research questions that motivated the work. In order to answer the
first research question, Table 1 describe identified themes from comments found in an in-depth interview.
Table 1: List of comments from an in-depth interview (N=20)
Classification of comments from users and example Frequency Percentage
1 Students have no means of checking what mistakes they have made. 6
Male:2
Female:4
30%One female student expressed that “There is no function to see what mistake I
made during my practice session in this application.”
2 The system sometimes gives a high score even though the answer is entirely wrong. 12
Male:5
Female:7
60%One male student expressed that “It sometimes gave us a high score even if we say
something unrelated.”
3 The system gives a high score even when the student’s pronunciation is wrong. 12
Male:3
Female:9
60%One male student expressed that “The system gives a high score even if we
pronunciation badly.”
4 Even when the student speaks English with a strong Japanese accent (English
pronunciation being negatively influenced by a Japanese accent), the system gives a
high score.
13
Male:4
Female:9
65%
One male student expressed that “Japanese-English works in this application, and
they gave us a high score.”
5 The lesson in the actual class does not acknowledge a student’s concise answer,
even if it is a correct answer. Nevertheless, the system gives the student a high
score.
2
Male:1
Female:1
10%
One male student expressed that “Even if I do not speak in a sentence, but the only
reply with one word still marks a high score.”
6 The student’s speaking speed should be considered as one of the components for
scoring.
5
Male:0
Female:5
25%
One female student expressed that “It seems not to matter whether I speak quickly
or extremely slow.”
7 The student’s response time should be considered one of the components for
scoring.
2
Male:0
Female:2
10%
One female student expressed that “It does not make any difference whether I
reply in a second or in a while.”
8 In the current application version, the conversation flow directly follows the
dialogue order. In order to build student flexibility, it should randomize the order of
the dialogue, but only when it makes sense.
2
Male:1
10%
437

23. Yukimi Takahashi and Poonsri Vate-U-Lan
Classification of comments from users and example Frequency Percentage
One female student expressed that “It is always straightforward, and we need the
challenge to be a flexible speaker.”
Female:1
9 Students should be able to understand the criteria for scoring. 4
Male:1
Female:3
20%One female student expressed that “We do not know how to get a gold trophy.”
Table 2: Categorizing problems
Machine learning function
to improve
Summary of comments Interpreting the findings map to the
function of each component of
machine learning
Datamining
Deeplearning
Supervised
learning
Unsupervised
learning
Reinforcemen
tlearning
1. Students themselves have no means of
checking what mistakes they have
made.
There is no clarification of users’
mistakes; thus, the machine learning
of this eLearning application needs to
classify what the mistake is and then
provide meaningful and appropriate
feedback when students interact
with the application.
   
2. The system sometimes gives a high
score when the answer is completely
wrong.
The machine learning could identify
neither a slightly wrong nor entirely
wrong answer.
   
3. The system gives a high score even
when the student’s pronunciation is
wrong.
The machine learning could not
identify the level of incorrect
pronunciation.
   
4. Even when the student speaks English
with a strong Japanese accent (English
pronunciation is negatively influenced
by a Japanese accent), the system gives
a high score.
The machine learning could not
determine the level of an
inappropriate accent.    
5. The lesson in the actual class does not
acknowledge a student’s concise
answer, even if it is a correct answer.
Nevertheless, the system gives the
student a high score.
The machine learning needs to
expand criteria to check what is
correct and explain to the student
why and how the answer is correct
or incorrect.
    
6. The student’s speaking speed should be
considered one of the components for
scoring.
The machine learning needs to count
and check the speed of speaking as a
criterion.
   
7. The student’s response time should be
considered one of the components for
scoring.
The machine learning needs to add
criteria for appropriate response
times and mention them in the
instructions for the application.
   
8. In the current application version, the
conversation flow directly follows the
dialogue order. In order to build
student flexibility, it should randomize
the order of the dialogue, but only
when it makes sense.
The unsupervised learning needs to
offer dialogue that is flexible and
appropriate to encourage the
student to practice smoothly. 
9. Students should be able to understand
the criteria for scoring.
The AI eLearning application needs
to provide instructions for the
scoring criteria.
Total frequency 7 6 7 8 2
438

24. Yukimi Takahashi and Poonsri Vate-U-Lan
Table 2 answered the second and third research questions, which addressed classifying the areas that need
improvement in five components of AI machine learning: data mining, deep learning, supervised learning,
unsupervised learning, and reinforcement learning.
An analysis of the in-depth interview results indicated that most of the opinion obtain gap to improve that
cross multiple machine learning components. Therefore, the table had to analyse the nature of the opinion
comprehensively. This table shows the relationship between each component of machine learning in AI, which
need to work together to offer a useful eLearning application.
All of the participants’ comments are complex. It is necessary to translate the feedback in order to classify it
correctly into the relevant field of AI. For example, comment No. 1 from Table 2 states that the students
themselves have no means of checking what mistakes they had made. By translating this comment into the AI
component, there are lurking problems, such as the interface problem of the invisibility of the students’ test
results and the problem of not being able to obtain the correct feedback according to the judgment results.
Data mining acts as a basement technology that gathers keywords through data and uses statistical analysis
methods to discover valuable insights hidden in the information. For example, if the metadata from this
eLearning application contains attribute data such as the gender, mother-tongue, or age of students, it is
possible to categorize the score data according to the attribute of the students’ mother tongues. Speech
recognition is possible with Deep Learning. This AI eLearning application uses speech recognition to receive the
human voice and output it as text. Natural language processing is also one of the features that Deep Learning
makes possible. Natural language processing is a technology that allows computers to process and understand
the written and spoken words that humans use daily, and the eLearning application for this study uses natural
language processing for certain exercises such as pronunciation drills, role-playing practice, and vocabulary
practice. Therefore, the problems found in these features are categorized in Deep Learning. Issues with true or
false questions are classified as supervised learning. At the other end of the spectrum, the problems which
cannot be predicted and cannot be prepared for with correct data in advance are classified as unsupervised
learning. In order to realize the accurate feedback, it is challenging to prepare clear answers in advance
because there is not only one answer for each question, but a myriad of choices. Such problems are
categorized as Reinforcement Learning.
8. Conclusion and recommendation
This paper described the gap to improve the current AI eLearning application, which was implemented to
increase opportunities to practise English conversation that could not be realized with existing learning
materials, such as paper, video, and audio. Research findings from in-depth interviews suggest that the AI
application is a useful tool for practising English, but indicate that there is too much of a gap in the current AI
eLearning application. Areas for improvement can be classified into five components of machine learning: data
mining, deep learning, supervised learning, unsupervised learning, and reinforcement learning. The outcome
of this study discovered how to use AI to provide accurate feedback, such as the pronunciation of a word and
phrase, correcting grammar, concerning speed of speech.
There is a risk that AI might be used to scale up corrupt pedagogical practices. If AI is the new electricity, it will
have a broad impact on society, economy, and education, so it needs to be treated with care (Tuomi, 2018).
The AI surely adds value to the computer application in education. However, an ideal AI with error-free will
involves lots of functions that try to lift AI capabilities to almost as good as the human brain. It is crucial to
maintain focus on the idea that humans should identify problems, critique, and identify risks (Popenici & Kerr,
2017). Therefore, the research for this purpose is in high demand. This research offered a new insight into the
mechanism of AI in education and significantly proposed a solution.
The current study focused on student perspectives; thus teacher perspectives are also necessary for future
study. Moreover, it is essential to verify the effectiveness of this AI eLearning application. The
recommendation is to study more in-depth with the various research techniques in order to confirm this
finding and innovate new research in the future.
439

25. Yukimi Takahashi and Poonsri Vate-U-Lan
References
Alton, L., Blog, V., 2017. The Seven Most Important Data Mining Techniques [WWW Document]. URL
https://www.datasciencecentral.com/profiles/blogs/the-7-most-important-data-mining-techniques
Andrychowicz, M., Wolski, F., Ray, A., Schneider, J., Fong, R., Welinder, P., McGrew, B., Tobin, J., Abbeel, P., Zaremba, W.,
2017. Hindsight Experience Replay.
Benaich, N., 2017. Six areas of AI and machine learning to watch closely [WWW Document]. Medium. URL
https://medium.com/@NathanBenaich/6-areas-of-artificial-intelligence-to-watch-closely-673d590aa8aa
Cuayáhuitl, H., 2017. SimpleDS: A Simple Deep Reinforcement Learning Dialogue System, in: Jokinen, K., Wilcock, G. (Eds.),
Dialogues with Social Robots. Springer Singapore, Singapore, pp. 109–118.
Halimah, H., Lustyantie, N., Ibrahim, G.A., 2018. Students’ Perception of the Implementation of Oral Application in
Teaching Speaking. JEELS (Journal of English Education and Linguistics Studies) 5, 1–22.
Hatou, Y., 2006. Mechanism of “able to speak” for those who learn or teach English. Sekaishisosha Co., Ltd, Kyoto.
Karsenti, T., 2019. Artificial Intelligence in Education: The Urgent Need to Prepare Teachers for Tomorrow’s Schools (SSRN
Scholarly Paper No. ID 3346658). Social Science Research Network, Rochester, NY.
Lopez, M.M., Kalita, J., 2017. Deep Learning applied to NLP.
Luckin, R., Holmes, W., Griffiths, M., Corcier, L.B., Pearson (Firm), University College, L., 2016. Intelligence unleashed: an
argument for AI in education.
Lynch, L., 2019. 4 Ways Artificial Intelligence Will Transform E-Learning. LearnDash.
Lynch, M., n.d. 6 Ways Machine Learning Will Revolutionize the Education Sector - The Tech Edvocate [WWW Document].
The Tech Edvocate. URL https://www.thetechedvocate.org/6-ways-machine-learning-will-revolutionize-the-education-
sector/
M. Ciolacu, A. F. Tehrani, L. Binder, P. M. Svasta, 2018. Education 4.0 – Artificial Intelligence-Assisted Higher Education:
Early recognition System with Machine Learning to support Students’ Success, in: 2018 IEEE 24th International
Symposium for Design and Technology in Electronic Packaging (SIITME). Presented at the 2018 IEEE 24th International
Symposium for Design and Technology in Electronic Packaging (SIITME), pp. 23–30.
Popenici, S.A.D., Kerr, S., 2017. Exploring the impact of artificial intelligence on teaching and learning in higher education.
RPTEL 12, 22.
Shivhare, N., n.d. AI in schools — here is what we need to consider [WWW Document]. The Conversation. URL
http://theconversation.com/ai-in-schools-heres-what-we-need-to-consider-109450.
Tanmoy, R., 2019. Machine-Learning and AI in the Education Sector | [WWW Document]. URL
https://www.stoodnt.com/blog/machine-learning-ai-in-education/
Tuomi, I., 2018. The Impact of Artificial Intelligence on Learning, Teaching, and Education: Policies for the Future.
Zheng, L., Yang, J., Cai, H., Zhang, W., Wang, J., Yu, Y., n.d. MAgent: A Many-Agent Reinforcement Learning Platform for
Artificial Collective Intelligence 2.
440