AI in Education

Areas of Focus

With the emergence of AI and its prolific use in education and research, universities are urged to cultivate an environment conducive to the nurturing of a high-demand skill set among learners effectively addressing the changing demands of the job market. Institutions of higher education play a pivotal role in realising the transformative potential of AI in education.

We aspire to equip the forthcoming generation with the competencies and outlook needed for propelling technological innovation, nurturing economic growth, and equitable societal development through the exploration of pioneering pedagogical methodologies, robust industry alliances, and the facilitation of entrepreneurship. Building and sustaining intellectual curiosity, encouraging the use of AI to adapt to the future, developing an AI literacy framework, and investing in innovative AI curriculum are some priorities we must bear in mind as educators.

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Thus AE4AI Network puts an emphasis:

• On developing AI skills and content, AI in education should promote an equitable access to AI learning and skills development and relevant competencies that would benefit learners across different disciplines. AI-embedded learning should provide opportunities to include low-level coding or no-code programming at all levels of education in Asia and Europe. In the pursuit of talent development in AI, higher education institutions have both the responsibility and capability to ensure that AI education is inclusive, ethical, and transformative.

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• On increasing social awareness on AI, we believe universities should take an active role in the open and accessible reskilling and upskilling of the public on the basics of AI, emphasising its risks, opportunities, as well as responsible, and ethical use.

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• On formulating learning and teaching methodologies, creation of self-paced learning opportunities, partnerships for shared accountability for learning processes, and provision of opportunities for meaningful interactions should be priorities. Concepts such as enabling spaces, outcomes-based education (OBE), nonviolent communication (NVC), reverse mentoring, the corporate perspective, and affirmative inquiry should inform methodologies to teaching and learning.

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• On building institutional capacity to conduct policy dialogue and strengthen educational capabilities, resilience, and develop reliability, there is a need to first address the lack of full engagement from stakeholders (policymakers, universities, etc.). Furthermore, international collaboration in addressing the challenges of adapting AI is key. It is also important to pinpoint where to catalyse change in universities and promote AI-friendly culture in higher education and provide incentives, especially in developing countries.

Action Lines: 

• Create a self-paced course covering AI literacy empowered by Generative AI technologies. Consider the train-the-trainer approach targeting academics and local community members, in their local language. Consider segmenting the content along “learning about AI” and “learning with AI” categories, customized to the target group, and providing certification (e.g. micro credentials).  

• Build a platform to incentivize open collaboration, by featuring information and partnership opportunities from various sources (e.g. EU funded projects, Leadership in Innovation Fellowship, etc.). The key goal of the platform is to incentivize forming consortiums to work on AI in Education initiatives. 

AI in Education Coordinators:

• Dr Monika SONTA (Poland), Adjunct, Researcher, Head of The Bachelor in Management & Artificial Intelligence Program, Kozminski University

• Dr Rahul BHANDARI (India), Joint Director and Asst Professor, School of Management and The Office of International Study Abroad Program, O.P. Jindal Global University

• Dr Vicente PITOGO (The Philippines), Dean of the College of Computing and Information Sciences, Caraga State University