Author: nimda

The LEADER-AI Project at its end! – November 2024

We are proud to announce that our ambitious project – the LEAarning analytics and AI for personaliseD lEaRning [LeaderAI], it is approaching its successful end! It was a challenging but exciting
learning and creating adventure, in which our consortium succeeded to design, test and implement all foreseen outcomes! In this journey, ‘quality’ was our reference and we had benefitied of a strong suport from academia and have worked with large number of educational designers, professors, researchers, students and faculty staffs from Higher Education institutions accross Europe.
The last activities have been to pilot the LeaderAI Interactive eLearning platform that we developed: the alpha and beta testing phases of the platform went well, and due to the valuable feedback that we collected from our testers, we refined and improved the platform thus to fit perfectly o the required characteristics and the envisaged purpose. The dissemination workshops in partner countries have started and preparation for the Final Conference are in course. Through these multiplier events the outputs of our LeaderAI project are presented and shared with the target groups – Higher Education organisations and staffs. Hands-on sessions implemented during the dissemination workshops allow users to learn and practice the use of the results, especially the educational resources provided via the eLearning platform.

The main LeaderAI results are:
• LeaderAI Toolkit: an attractive support package for HE staffs to advance their professional practices in designing personalised courses (technology-enhanced face-to-face and fully online), by selecting and integrating data-driven and AI-based tools.

• Scenario-based training and piloting: a training program providing hands-on opportunities for HE teaching staff on how to personalise their instruction through open source/freeware AI-
based and data visualisation tools

• Interactive eLearning platform (https://leaderai.eu/elearning/ ): a platform providing instant access to a full suite of resources including the interactive Toolkit, a self-paced MOOC and demonstrations to support HE faculty to personalise their courses using AI-based and analytics tools.

We collegially invite you to access them and use them in your teaching-learning activities! We hope you will find them useful!

Our LEADER AI project is about to end!

If you want to learn more about the progress during the last months, check out our 4th Newsletter with updates about the eLearning platform, the upcoming dissemination workshops and our last Transnational Project Meeting.

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

Our LEADER AI Toolkit and transnational report are currently being translated in partners’ national languages and will be available soon in our website. Before finalized, each partners’ national committee consisted of Higher Education (HE) instructors, researchers, learning designers and eLearning developers will pilot test LEADER AI toolkit to provide their feedback on it.

Meanwhile, the project’s partners had an online meeting on 22nd September 2023 in order to finalize the learning scenarios template for Work Package 3, namely “Scenario-based training and piloting”.

In this work package, a training program will be developed with the aim to provide Higher Education teaching staff  with hands-on opportunities on how to personalize their instruction through open source/freeware AI-based and data visualization tools that do not require highly specialized programming skills.

Among implementation guidelines such us handouts, instructor notes and lesson plans, the training will include real case scenarios from various disciplines. Such scenarios are currently rare in the field. One idea is that learning analytics could be utilized to raise awareness regarding the learning subject of the scenario and collect feedback from students regarding the learning material, while AI tools could be used for providing asynchronous personalized support in flipped classroom settings.

Follow us on social media to stay informed about the project’s progress.

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

LEADER-AI Toolkit is coming soon!

The project’s consortium has compiled the information derived from the research in all partner countries and the LEADER-AI Toolkit is expected to be published in our website soon. The results will cover the potential benefits and risks of using smart data and digital technology in education, but also draw on examples of promising practices from different countries and institutions. Recommendations for future research and development in this emerging field, as well as for fostering collaboration and innovation among stakeholders will be presented.

Meanwhile, the project’s partners began working on Work Package 3, namely “Scenario-based training and piloting”. In this work package, a training program will be developed with the aim to provide Higher Education teaching staff with hands-on opportunities on how to personalize their instruction through open source/freeware AI-based and data visualization tools that do not require highly specialized programming skills.

The training will include real case scenarios from various disciplines, implementation guidelines such us handouts, instructor notes and lesson plans. The LEADER AI Toolkit will be an integral part of it. Two representatives from each partner country will be trained on how to deliver the training package. Then, the trainers will deliver it to at least 20 participants, members of the target group in their countries. Successful use of the training will allow Higher Education providers and their staff to acquire skills and competences, multiplying the impact of the project and ensuring the Toolkit’s continued impact in future

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.

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.

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

Cyprus partner (UNIC & CARDET) has conducted two focus groups with 4 participants each. The aim was to collect data regarding the readiness of Higher Education Institutes (current state, practices followed, and what is needed). The findings of the two focus groups concentrate on the below three categories (Personalised learning, Artificial Intelligence (AI) and Learning Analytics (LA), and Challenges and Benefits).

1. A) Personalised learning: This section explores the concept of personalised learning as defined by the participants of the focus groups. The participants emphasise the importance of adapting teaching methods and objectives to meet students’ individual needs. They mention techniques such as presenting materials in various formats, working in groups, and using adaptive learning technologies. The benefits of personalised learning include tailored instruction, increased engagement, and student collaboration.
2. B) Artificial Intelligence (AI) and Learning Analytics: In this section, the participants discuss using AI tools and learning analytics in education. They mention specific AI tools such as language learning apps with AI algorithms, translation tools like Google Translate and Deepl, and writing assistance tools like Grammarly and Turnitin. The participants also highlight the application of learning analytics for tracking individual learner progress, gathering data on student demographics and performance, and generating reports through platforms like Moodle and Blackboard.
3. C) Challenges and Benefits: The final section focuses on the challenges and benefits of implementing personalised learning and using AI and learning analytics tools. The challenges mentioned include the difficulty of fully personalising teaching in higher education, lack of university-wide practices, insufficient training and support, limitations of AI tools, and the need for critical thinking skills. On the other hand, the benefits discussed include time-saving, automation of tasks, access to vast information, improved teaching material preparation, and the ability to track and improve student engagement and progress through learning analytics.

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

During the first months of the project, the LEADER AI partners have been conducting national and EU-based desk and field research. First, all partners researched the status quo regarding the use of AI-based tools and learning analytics for personalisation of learning, in Higher Education. Then, national field research was conducted to investigate firsthand the target groups’ (academics, instructors, researchers, learning designers and developers) experiences with learning analytics and Artificial Intelligence (AI) for personalisation. The field research was mixed methods, involving the distribution of questionnaires and conduct of focus groups at national levels. The results will be presented in a transnational report, to share details about the state-of-the-art and challenges regarding personalised learning with learning analytics and AI.

The Transnational report will be the first LEADER AI product published during the next months. It will explore the potential benefits and risks of using smart data and digital technology in education, such as enhancing student engagement, improving learning outcomes, supporting decision-making and policy design, ensuring that ethical, legal, and social issues are addressed. The report will draw on examples of promising practices from different countries and institutions. Recommendations for future research and development in this emerging field, as well as for fostering collaboration and innovation among stakeholders will be presented. The results will be the foundation for the development of all project deliverables to follow (namely the practical Toolkit, scenario-based training and eLearning platform) while prompting further research for all interested parties.

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

Organizations from five European Union countries – Cyprus, Greece, Estonia, Romania and Portugal – joined forces into one project team and launched the LEADER-AI project. The project aims to address the need for effective Higher Education (HE) digital learning that responds to students’ needs, strengths and skills, through the proper exploitation of advanced technologies, including Artificial Intelligence (AI). LEADER-AI started on 31st Dec 2022 and will run for 24 months. Over the next two years, we will develop and share a range of educational resources for HE faculty and staff, to support them adopt AI-based and data visualization tools for personalized learning in HE.

To promote flexibility and openness in students’ diverse individualities, teaching and academic staff can implement a personalised instructional approach using Artificial Intelligence and data-driven tools. AI-based applications can automatically respond to students’ needs through adaptive and customised teaching and support. The LEADER-AI consortium will provide practical guidelines to be adapted by HE instructors, academics and educational technology practitioners for their teaching and learning activities. The consortium will work within the goals of the Commission’s European Education Area, to improve the digital capacity of Europe’s education system by exploiting the opportunities given by new technologies such as AI (EC, COM, 2020).

Throughout the project work, the partnership will ensure the delivery of the necessary inputs, improvements and solutions by developing and sharing the following major results:

• LEADER-AI Toolkit
• Scenario-based training and piloting
• Interactive eLearning platform and digital resources

Furthermore, the consortium will strongly encourage broad distribution and usage of the LEADER-AI results among relevant HE organisations and staffs providing their expertise and sharing the project results on testing & piloting events, national events and the Final Conference in Cyprus. All these educational resources will be freely available through the project website and accessible through our Facebook and Twitter pages.

During the first months of the project, the LEADER AI partners have been conducting national and EU-based desk and field research. First, all partners researched the status quo regarding the use of AI-based tools and learning analytics for personalisation of learning, in Higher Education. Then, national field research was conducted to investigate firsthand the target groups’ (academics, instructors, researchers, learning designers and developers) experiences with learning analytics and Artificial Intelligence (AI) for personalisation. The field research was mixed methods, involving the distribution of questionnaires and conduct of focus groups at national levels. The results will be presented in a transnational report, to share details about the state-of-the-art and challenges regarding personalised learning with learning analytics and AI.

The Transnational report will be the first LEADER AI product published during the next months. It will explore the potential benefits and risks of using smart data and digital technology in education, such as enhancing student engagement, improving learning outcomes, supporting decision-making and policy design, ensuring that ethical, legal, and social issues are addressed. The report will draw on examples of promising practices from different countries and institutions. Recommendations for future research and development in this emerging field, as well as for fostering collaboration and innovation among stakeholders will be presented. The results will be the foundation for the development of all project deliverables to follow (namely the practical Toolkit, scenario-based training and eLearning platform) while prompting further research for all interested parties.