Sessions
Keynote 1
Research designs for Artificial Intelligence in Education: Promoting Fairness and Ethics through Evidence-Based, Human-Centered Methodological Approaches - Prof. Dr. Irene-Angelica Chounta (EN)
The adoption of artificial intelligence (AI) in education provides us with numerous opportunities to support learning and teaching. For example, using cutting-edge technologies for developing intelligent learning environments, such as Intelligent Tutoring Systems (ITSs) or employing computational approaches, such as machine-learning and data mining, to model how people learn. On the other hand, it sets new challenges we are called to address regarding its pedagogically appropriate and ethical use, the fairness of AI algorithms, but also with respect to fundamental human rights, democracy and the rule of law. To meaningfully address concerns and criticism, we need to communicate with stakeholders – namely, researchers, teachers, students, parents, and potentially unions and policy-makers – the benefits of AI in education but also the potential pitfalls. At the same time, there is a need for evidence-based, human-centered research approaches that will allow us to communicate the advantages of using AI in education based on solid, scientific foundations while safeguarding its ethical and fair application.
In my talk, I will discuss opportunities and challenges for AI in education from the perspective of Fairness, Accountability, Transparency and Ethics (FATE) and I will be relating that to the need for evidence-based research. I will show that transparent communication with stakeholders, and systematization and standardization of research can support fair and ethical use of AI in education, thus allowing us to capitalize on the potential of AI for personalized and adaptive learning.
Preparatory literature:
Holmes, W., Persson, J., Chounta, I. A., Wasson, B., & Dimitrova, V. (2022). Artificial intelligence and education: A critical view through the lens of human rights, democracy and the rule of law. Council of Europe. https://rm.coe.int/prems-092922-gbr-2517-ai-and-education-txt-16x24-web/1680a956e3
Chounta, I. A., Bardone, E., Raudsep, A., & Pedaste, M. (2022). Exploring teachers’ perceptions of Artificial Intelligence as a tool to support their practice in Estonian K-12 education. International Journal of Artificial Intelligence in Education, 32(3), 725-755. https://link.springer.com/article/10.1007/s40593-021-00243-5
Chounta, I. A., Limbu, B., & van der Heyden, L. (2024). Exploring the Methodological Contexts and Constraints of Research in Artificial Intelligence in Education. In International Conference on Intelligent Tutoring Systems (pp. 162-173). Springer, Cham 10.1007/978-3-031-63028-6_13
Parallel Workshops 1:
Session 1: Perspectives on Ethics of AI in Education - Bhoomika Agarwal (EN)
This session will discuss the ethical issues raised by the increasing integration of Artificial Intelligence in Education (AIED). The first author will first explain the main concepts of AIED ethics based on her current research. Next, the co-authors will elaborate on the utilization of sensors and immersive technologies (e.g., VR and AR) in the training of psychomotor skills, delving into potential ethical considerations. The planned example cases include human-robot interaction and dance. Following this, there will be a role-playing game where the participants put themselves in the shoes of different stakeholders (e.g. student, teacher, developer, …) and have a collaborative discussion about the ethical aspects of the research topics of some of the co-organizers. These questions will highlight the ethical dilemmas or conflicts that can arise from ensuring ethical AIED, as every stakeholder will have their own requirements and responsibilities
Session 2: Generative AI for Assessment and Feedback - Kazem Banihashem, Nico van der Wiel (NL)
In recent times, technological advancements and the emergence of Generative AI tools like ChatGPT and Gemini have opened promising avenues for enhancing assessment and feedback practices in educational settings. However, the extent to which GenAI-powered tools can support teachers in these practices still requires in-depth investigation. In this workshop, we will explore this potential by introducing the use of a GenAI tool (ChatGPT) to assist teachers in three phases of assessment: entry/diagnostic assessment, formative assessment/feedback, and summative assessment. Utilizing a guideline for prompt engineering, we will provide examples of how different prompts can be used effectively for assessment and feedback purposes.
Preparatory literature:
Banihashem, S. K., Kerman, N. T., Noroozi, O., Moon, J., & Drachsler, H. (2024). Feedback sources in essay writing: peer-generated or AI-generated feedback?. International Journal of Educational Technology in Higher Education, 21(23), 1-15. https://doi.org/10.1186/s41239-024-00455-4
Parallel Workshops 2:
Session 3: AI, Large Language Models, and Avatars in Education: Augmented Reality Tutor (ART) - Dr. Deniz Iren, Krist Shingjergji (EN)
This session will discuss the ethical issues raised by the increasing integration of Artificial Intelligence in Education (AIED). The first author will first explain the main concepts of AIED ethics based on her current research. Next, the co-authors will elaborate on the utilization of sensors and immersive technologies (e.g., VR and AR) in the training of psychomotor skills, delving into potential ethical considerations. The planned example cases include human-robot interaction and dance. Following this, there will be a role-playing game where the participants put themselves in the shoes of different stakeholders (e.g. student, teacher, developer, …) and have a collaborative discussion about the ethical aspects of the research topics of some of the co-organizers. These questions will highlight the ethical dilemmas or conflicts that can arise from ensuring ethical AIED, as every stakeholder will have their own requirements and responsibilities.
Preparatory literature:
Shingjergji, K., Urlings, C., Iren, D., & Klemke, R. (2024, March). Shaping and evaluating a system for affective computing in online higher education using a participatory design and the system usability scale. In Proceedings of the 14th Learning Analytics and Knowledge Conference (pp. 382-391).
Böttger, F., Cetinkaya, U., Di Mitri, D., Gombert, S., Shingjergji, K., Iren, D., & Klemke, R. (2022, September). Privacy-preserving and scalable affect detection in online synchronous learning. In European Conference on Technology Enhanced Learning (pp. 45-58). Cham: Springer International Publishing.
Session 4: Workshop 'AI in het Onderwijs: De Scriptie in Beeld' - Iwan Wopereis, Hubert Vogten (NL)
The Open University sees great potential in text-based generative AI (GenAI) to optimize the assessment of theses within higher education. Our aim is not only to improve students’ academic writing skills, but also to increase the quality of the content of their work. We hope to enrich the students’ learning experience by providing more efficient and effective feedback methods, whereby the assessment and feedback process is shaped by the lecturer in interaction with GenAI.
During the workshop, you will be introduced to assessment and feedback methods explored in the ‘Slim Bekeken’ project (starting in early 2024). After discussing the project design, findings and challenges, you will get to work hands-on with a case study.
Preparatory literature:
W. Dai et al., "Can Large Language Models Provide Feedback to Students? A Case Study on ChatGPT," 2023 IEEE International Conference on Advanced Learning Technologies (ICALT), Orem, UT, USA, 2023, pp. 323-325, https://doi: 10.1109/ICALT58122.2023.00100.
Wopereis, I., Vogten, H., & Alqassab, M. (2024). Requirements for AI-enhanced thesis management in higher education: A group concept mapping study. Paper (to be) presented at the Innovating Higher Education conference.
Banihashem, S. K., Kerman, N. T., Noroozi, O., Moon, J., & Drachsler, H. (2024). Feedback sources in essay writing: Peer-generated or AI-generated feedback? International Journal of Educational Technology in Higher Education, 21, Article 23. https://doi.org/10.1186/s41239-024-00455-4
Dai, W., Ji, Z., Chen, H., & Wu, J. (2023). Can large language models provide feedback to students? A case study on ChatGPT. In M. Chang, N.-S. Chen, R. Kuo, G. Rudolph, D. G. Sampson, & A. Tlili (Eds.), Proceedings of the 2023 IEEE International Conference on Advanced Learning Technologies (ICALT) (pp. 323-325). IEEE. https://doi.org/10.1109/ICALT58122.2023.00100
Wopereis, I., Vogten, H., & Alqassab, M. (2024, October 23-25). Requirements for AI-enhanced thesis management in higher education: A group concept mapping study [Paper presentation]. Innovating Higher Education Conference 2024, Limassol, Cyprus.
Session 5: Het echte probleem van AI is niet AI - Reijer Passchier (NL)
Last year, politicians, scientists and leaders of tech companies met in Britain’s Bletchy Park to discuss the risks of Artificial Intelligence (AI). The main points of discussion were fake news, cyber attacks and the (small) likelihood of AI passing a ‘frontier’ and becoming a threat to all life on earth. This, as often happens on such occasions, did not address the real problem of AI: the power of disposal of this technology is mainly in the hands of a few very large commercial companies, within which, additionally, only a few CEOs or major shareholders call the shots. As owners of AI and the infrastructure required by AI, they, almost unilaterally, determine which AI continues to develop, at what pace, what risks are acceptable in the process, what values AI should serve and under what conditions others may use AI. In this process, their own private interests (or delusions) usually come first. Not the public interest. What does this situation mean for the use of AI in education? Or what should this situation mean for the use of AI in education? These questions are the focus of this workshop.
Keynote 2:
Learning Design meets Learning Analytics and Artificial Intelligence: Potentials and challenges - Prof. dr. Yannis Dimitriadis (EN)
Technology-enhanced learning ecosystems are becoming quite complex, especially when non-conventional approaches, such as collaborative or inquiry learning are employed. On the other hand, the recent advances in the learning analytics field have been very promising, for purposes of understanding and optimizing learning and the environments in which it occurs. However, the alignment between design for learning and learning analytics has been recently shown to be a pending, albeit essential, issue that would allow for effective and trustful pedagogical interventions and orchestration.
This talk discusses the interdependence and mutual benefits of bringing together learning design and learning analytics, as well as the eventual support of (generative) artificial intelligence to the learning design and orchestration lifecycle. It also argues that teachers’ agency should be safeguarded through human-centered design approaches. Illustrating examples and relevant pending challenges complement the overall discussion.
Preparatory literature:
Rodriguez Triana, M. J., Martínez-Monés, A., Asensio-Pérez, J. I., & Dimitriadis, Y. (2015). Scripting and monitoring meet each other: Aligning learning analytics and learning design to support teachers in orchestrating CSCL situations. British Journal of Educational Technology, 46(2), 330-343.
Mangaroska, K., & Giannakos, M. (2018). Learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning. IEEE Transactions on Learning Technologies, 12(4), 516-534.
Dimitriadis, Y., Martínez-Maldonado, R., & Wiley, K. (2021). Human-Centered Design Principles for Actionable Learning Analytics. In: Tsiatsos, T., Demetriadis, S., Mikropoulos, A., Dagdilelis, V. (eds) Research on E-Learning and ICT in Education. Springer, Cham.
Giannakos, M., Azevedo, R., Brusilovsky, P., Cukurova, M. Dimitriadis, Y., Hernandez-Leo, D., Järvelä, S., Mavrikis, M. & Rienties, B. (2024). The promise and challenges of generative AI in education. Behaviour & Information Technology. Commentary paper (accepted).
Inaugural lecture:
Technology-Enhanced Learning in the age of Technology that Learns - Prof. Dr. Roland Klemke (EN)
The main evolutionary advantages of human beings over other forms of life are our ability to learn and to teach. These abilities enable continuous growth of the body of knowledge of humankind and foster permanent innovation, where every new generation can build upon what has been discovered and invented before, ultimately reaching the point where we invented technology that learns by itself.
These abilities also lead us to permanently rethinking and redesigning the ways we learn and teach, with technology-enhanced learning (TEL) being the research field that addresses, explores and evaluates technological innovation and their impact on education.
Roland looks at the history of learning and highlights how the age of fast paced innovation changes the expectations towards education and which role technology-enhanced learning plays. He looks at four key domains of TEL (serious games and gamification, seamless and mobile learning, educational robotics and computational thinking, and multimodal artificial intelligence in education) to conclude how these fields in combination can contribute to education that prepares learners for an unknown future.