Advanced Technologies for Open Education

The aim of this course is to give students an overview and specific knowledge about advanced and emerging technologies that are either already in operation in the context of Open Education or might be relevant to Open Education in the future.

After completing this course, students will:
• Know about the range of emerging technologies open education methods, including Artificial Intelligence;
• Be able to critically evaluate the application and use of such technologies in Open Education.
• Successfuly conduct a basic Machine Lerning project, and thus understand the basics of Machine Learning.

Prerequisites include knowledge obtained in Year 1 courses 'Introduction to OE' and 'Technologies for OE', as students should have a deep understanding of OE environments, processes, actors, OE strategies, technological needs and preferences for OE, concepts and mechanisms of openness. They should be familiar with the existing technologies used in ICT-supported education, basic ICT infrastructures and architectures. They should understand the mechanisms behind ICT supported collaboration, sharing and interoperability. Students should be able to use communication and collaboration tools, and should be prepared to work in interdisciplinary teams.

During the course students will learn about various advanced and emerging technologies that are already being used in Open Education or might be very relevant to Open Education, with a specific focus on Artificial Intelligence. Special attention will be put on technologies that are transforming traditional education. Students will learn about technologies from the broad areas of Artificial Intelligence, augmented reality and immersive technologies, distributed technologies such as blockchain, and Internet of Things. During the classes they will learn about the basics of the advanced technologies, existing approaches, strengths and weaknesses, their existing and potential use in Open Education and their costs and user experiences in Open Education.

The following topics will be covered:
1) Data mining, text mining, and web mining for education
2) Understanding AI and Open Education from a critical perspective
3) The application of AI in education
4) Teaching AI
5) Ethics of AI
6) Machine Learning basics
7) Machine Learning practice
8) The role of Open Education in teaching about AI
9) Blockchain, IoT and other emerging technologies
10) Machine Learning and Open Education

Students will combine online seminars and selflearning and will develop a critical evaluation of an AI-relevant tool being used in education to support Open Education. The emphasis will be on scientific methods for evaluating the contribution that the technologies are making to the overall aims of Open Education.

After completing this course, students will:
• Have a good overview and detailed knowledge about the specific advanced information technologies and how they impact on OE and its deployment;
• Master the selection process of proper technologies and relate them to a specific challenge to be solved in OE;
• Become proposers of new technology development and deployment in OE;
• Be able to prepare and manage RTD projects in implementing ICT in OE;
• Be able to evaluate, validate and critically assess the emerging technologies prospects for OE;
• Realistically assess their personal abilities in terms of integrating advanced and emerging technologies in OE, hence being able to identify appropriately skilled teams for specific integration projects.

• Max Bramer, Principles of Data mining, Springer, London 2007.
• Helen Sharp, Jenny Preece, and Yvonne Rogers, Interaction Design: Beyond Human-Computer Interaction, Wiley, 3rd Edition.
• Hillman, V., Holmes, W., & Duarte, T. (2025). A Rapid Review of AI Literacy Frameworks, with Policy Recommendations. Royal Institute. E-version
• Holmes, W., Mouta, A., Hillman, V., Schiff, D., Laak, K.-J., Atenas, J., Bardone, E., Lochead, K., Gonsales, P., Havemann, L., Seon, J., Go, B., Schreurs, B., Zhgenti, S., Lee, K., Bali, M., Bialik, M., Medina-Gual, L., Adhicandra, I., … Yeo, B. (2025). Critical Studies of Artificial Intelligence and Education: Putting a Stake in the Ground. (SSRN Scholarly Paper No. 5391793). Social Science Research Network. https://doi.org/10.2139/ssrn.5391793 E-version
• UNESCO GEM. (2023). Technology in education: A tool on whose terms? E-version
• West, M. (2023). An Ed-Tech Tragedy? Educational Technologies and School Closures in the Time of Covid-19. UNESCO. E-version

• 15-minute presentation about an example AIED application. 20%
• 15-minute practical presentation on data mining with Orange. 20%
• First version written assignment (a 3000-word written critical evaluation of an AIED application). This should build upon and improve the presentation about the example AIED application. 20%
• Final version written assignment (a final submission of the 3000-word critical evaluation of an AIED application). This should build upon and improve the first version written evaluation. 40%

Professor Dr Wayne Holmes
Professor Wayne Holmes (PhD, University of Oxford) is full Professor of Critical Studies of Artificial Intelligence and Education in the UCL Institute of Education, University College London (UK). He also holds a UNESCO Chair in the Ethics of Artificial Intelligence and Education (International Research Centre on Artificial Intelligence, Slovenia). His research explores the ethical, human rights, and social justice implications of teaching and learning with and about Artificial Intelligence (AI&ED). Wayne is also a lead member of the Council of Europe’s AI&ED expert group, which is developing legislation to protect the human rights of students and teachers engaging with AI-enabled systems. In addition, he is a consultant for UNESCO (for which he co-wrote ‘AI and Education: Guidance for Policy-makers’ and ‘Guidance for Generative AI in Education and Research’) and an AI&ED expert for the United Nations and the EU JRC. Wayne has written more than a hundred other publications (including most recently ‘Critical Studies of Artificial Intelligence and Education: Putting a Stake in the Ground’) and has given AI&ED keynotes in more than 20 countries.

Izbrane objave / Selected bibliography:
Hillman, V., Holmes, W., & Duarte, T. (2025). A Rapid Review of AI Literacy Frameworks, with Policy Recommendations. Royal Institute. https://royalsociety.org/-/media/policy/projects/ai-in-education/Hillman-et-al-a-rapid-review-of-AI-literacy-frameworks.pdf
Holmes, W. (2023b). The Unintended Consequences of Artificial Intelligence and Education. Education International Research. https://www.ei-ie.org/en/item/28115:the-unintended-consequences-of-artificial-intelligence-and-education
Holmes, W. (2025). AI, education, and children’s rights. Frontiers in Education, 10, 1656736. https://doi.org/10.3389/feduc.2025.1656736
Holmes, W. (Ed.). (2026). Handbook of Critical Studies of Artificial Intelligence and Education. Edward Elgar Publishing Ltd.
Holmes, W., Mouta, A., Hillman, V., Schiff, D., Laak, K.-J., Atenas, J., Bardone, E., Lochead, K., Gonsales, P., Havemann, L., Seon, J., Go, B., Schreurs, B., Zhgenti, S., Lee, K., Bali, M., Bialik, M., Medina-Gual, L., Adhicandra, I., … Yeo, B. (2025). Critical Studies of Artificial Intelligence and Education: Putting a Stake in the Ground. (SSRN Scholarly Paper No. 5391793). Social Science Research Network. https://doi.org/10.2139/ssrn.5391793
Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2021). Ethics of AI in Education: Towards a Community-Wide Framework. International Journal of Artificial Intelligence in Education, 32, 504–526. https://doi.org/10.1007/s40593-021-00239-1
Miao, F., & Holmes, W. (2023). Guidance for Generative AI in Education and Research. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386693
Miao, F., Holmes, W., Huang, R., & Zhang, H. (2021). AI and Education: Guidance for Policy-Makers. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000376709