Press Release #5

The LEADER-AI Project: LEAarning analytics and AI for personaliseD lEaRning – July 2023

The Greek National Report investigated how Learning Analytics (LA), Artificial Intelligence (AI), and data-driven technologies are adopted for Personalized Learning (PL) in Higher Education Institutions (HEIs). As part of our work in this project’s deliverable, we conducted a literature review, a focus group and an online survey. Regarding the literature review, most of the studies used learning analytics data provided from the Learning Management Systems like Moodle. Data were used mainly to predict students’ performance and behavioral patterns via data mining algorithms. The focus group was contained between heterogeneous participants concerning job description, gender and age. Participants stated that PL is a generic educational methodology aiming to individual’s preparation, regardless of the means and the context, considering educational needs, personality characteristics, learning styles and background. Moreover, according to their own experience, there aren’t any systematic initiatives for PL in HE in Greece. None of the participants was actually using strategies for personalization. Educational data was used not for personalization purposes, but rather for monitoring/tracking students’ performance during the semester, either in summative or formative assessment settings. Participants are not using any AI tools for personalization in their courses. In some cases, they include in their courses examples or case studies about the potential use of AI in online distance education and e-Learning. A questionnaire was also completed by 50 participants. Most of the participants are very familiar or extremely familiar with the term PL. Most of them have said that they have never used LA or they use them very rarely. Main reason for that is the lack of university support and training to the staff. When used, they are used for self-assessment, for supervised Learning, for statistical reasons, use of chatbots in conversations, for research reasons and analysis of student data in order to predict the duration of studies/academic degree grade. They personalize and adapt the pace/time of learning, the assessment method and the teaching method. Finally, as a general conclusion, the adaption of AI and LA in HEIs in Greece is still in a pre-mature phase and as the focus group and survey revealed, despite the potential benefits that were reported, there is a number of major challenges and obstacles addressed in terms of technology, pedagogy, security, privacy and ethics, mandatory for the successful adoption of AI in education.