Press Release #4

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

The Cyprus 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), with a particular emphasis on the use of analytics and AI in online learning during the COVID-19 pandemic. The study used a mixed-methods approach that involved two literature reviews, two focus groups, and a questionnaire survey to collect data from professionals from different HEIs. The findings from the focus groups confirmed some of the findings from the literature reviews, such as the definition of PL as adapting teaching and learning objectives based on student’s individual needs, backgrounds, knowledge levels, and technical skills. The participants also discussed various AI tools and LA applications they used or encountered in their teaching and learning practices, such as Duolingo, Speakly, Google Translator, Deepl, ChatGPT, Grammarly, Turnitin, Moodle, and Blackboard. The participants recognized the benefits of these tools for time-saving, quick access to information, automation of tasks, improved thinking and structure, translation capabilities, personalization and communication. However, they also mentioned several challenges related to the limitations of AI tools, the potential for plagiarism and misuse of AI-generated content, the need for critical thinking and evaluation of tool results, and the difficulty of finding reliable non-English resources. The findings from the questionnaire survey showed that the participants were moderate to very familiar with PL and educational data but less familiar with LA and AI. Most participants reported using LA for PL monthly to annually, but fewer used AI for PL at a similar frequency. The most common type of information used for personalization decisions was learners’ performance, followed by cognition, individual goals, and data patterns. The most common reason for not using LA or AI for PL was the lack of adequate infrastructure, followed by the lack of skills or knowledge relevant to training and university support. The study concluded that adopting LA, AI, and data-driven technologies for PL in HEIs has potential benefits but also faces challenges and gaps that need to be addressed by future research. The study also provided some recommendations for future research that can further advance the field of PL, AI tools, and LA in HEIs.