Ai

Since November 2022, the advent of generative AI has been discussed more and more frequently and on a daily basis in public, at conferences, in literature, and in social media, what it is all about.

Generative AI refers to a class of artificial intelligence techniques and models designed to produce new, original content that resembles human-created data. Unlike traditional AI models that focus on recognizing patterns in data (as in machine learning), generative AI aims to create entirely new data that is similar, but not exactly the same, as the data on which it was trained.

Generative AI uses various architectures, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and autoregressive models to generate content such as images, text, music, videos, and more. These models learn from large data sets and then use that learning to create new, realistic examples of the data on which they were trained.

Here are some ways generative AI can be used:

• Image generation: Generative AI can be used to create realistic images that resemble photographs of objects, scenes, or even people. This has applications in graphic design, creating visual content for games and movies, and generating data sets for training other AI models.
• Text generation and summarization: Generative AI can produce human-like text, such as writing articles, stories, poems, or even generating code. It's also used to summarize long texts and summarize the main points in a more digestible format.
• Music and audio generation: Generative AI can compose music, create sound effects, or even imitate specific musical styles. It's useful in the entertainment industry to generate background music or create original compositions.
• Video generation and editing: Generative AI can generate video content, interpolate frames to create smoother animations, or improve video quality. This has applications in video production, special effects, and video editing.
• Style transfer: Generative AI can transfer the style of one image or artwork to another, creating artistic renditions. This is often used in photography and design to creatively alter images.
• Data augmentation:
• Generative AI can augment data sets by creating variations of existing data, increasing the variety of training data for other machine learning models, improving their performance.
• Drug discovery and chemistry: Generative AI is used in drug discovery to develop new molecules with specific properties. It speeds up the process of discovering potential drugs and materials.
• Chatbots and conversational agents: Generative AI can help chatbots and conversational agents interact with users in a more natural and engaging way by generating human-like responses based on input.
• Personalized learning materials: In education, generative AI can create personalized learning materials tailored to individual students, taking into account their learning styles, preferences, and progress.
• Art and creativity: Generative AI has already been used to create art and various forms of digital creativity, allowing artists and creators to explore new styles and ideas.
• Generative AI holds immense potential for a variety of fields, and ongoing research and development is expanding its potential applications, making it an exciting area of study and innovation within the broader field of artificial intelligence. However, ethical considerations, responsible use, and potential biases in generated content are important aspects that must be carefully considered.

Artificial intelligence, including generative AI, has the potential to revolutionize education and learning in a number of ways, but it also brings with it some challenges and ethical concerns. Below are some of the issues surrounding AI, generative AI, and their application in education and learning:

• Bias and Fairness: AI systems, including generative AI, often learn from historical data, which can perpetuate biases present in the data. In education, this could lead to biased learning materials, assessments, and recommendations that disadvantage certain groups based on race, gender, socioeconomic status, etc.
• Ethical use of AI in education: Clear guidelines and ethical frameworks need to be established for the use of AI in education. This includes issues of privacy, consent, and ensuring that AI is used in a way that is consistent with the values and principles of education.
• Privacy concerns: The use of AI in education often involves the collection and analysis of a large amount of student data. Privacy concerns arise in the collection, storage, and use of this data, especially when minors are involved. Ensuring data privacy and security is paramount.
  Overreliance on technology: There is a risk of over-reliance on AI and technology in education, potentially diminishing the role of human educators and personalized interactions. A balance between the use of AI as a tool and the critical role of human guidance and interaction is essential.
• Lack of human understanding and empathy: AI, including generative AI, lacks true understanding and empathy. While it can generate content and responses, it often lacks the depth of human understanding required for nuanced interactions, emotional support, and personalized learning experiences.
• Job displacement: The increasing use of AI and automation in education can lead to concerns about job displacement for educators. Although AI can help with various tasks, it should be seen as a supplement to, not a replacement for, human educators.
• Accountability and responsibility: Determining accountability for AI-generated content or decisions is challenging. In education, it's important to establish clear accountability for AI-generated instructional materials, assessments, and recommendations to ensure their accuracy, appropriateness, and effectiveness.
  Depersonalization of learning: Generative AI can lead to standardized learning materials that lack personalization and don't address individual learning needs, preferences, and abilities. Adapting AI-generated content to different learning styles and abilities is a major challenge.
  Transparency and explainability: It's important for educators and learners to understand how AI, especially generative AI, arrives at its results. To build trust in the technology, it's important to ensure that AI-generated content is transparent and explainable.
• Educational divide and access: The adoption of AI and generative AI in education can widen the digital divide, allowing students with limited access to technology to fall further behind those with greater access, exacerbating existing educational inequities.
• Addressing these challenges and responsibly using AI, including generative AI, in education requires careful consideration of the ethical, social, and educational implications. Striking the right balance and ensuring equitable access and use of AI in education is key to maximizing its benefits while minimizing potential harm.

Today, 24 July 2026, we held our weekly meeting to write and edit our white paper on behalf of ASEF7 and ASEF7 Innolab. Our topic is the triangulation of Open Education/OER, AI, and the SDGs. Our main question is: How can Quality Open Education shape the future of AI and sustainability? The central premise of this white paper is captured in the mantra: Human Wisdom. Trusted AI. Sustainable Futures. These three pillars are mutually reinforcing. Human wisdom provides the ethical judgment, critical thinking, and contextual understanding that AI cannot replace. Trusted AI enables responsible and transparent use of technology while respecting human values and rights. Open Education provides the knowledge infrastructure that allows reliable, multilingual, openly accessible educational resources to be created, shared, adapted, and continuously improved in support of the SDGs.

In today’s world, universities have a global social responsibility to help create dynamic, sustainable communities founded on equality of wealth, gender, and cultural background. International higher education has been criticised for being too neoliberal; now is the time for it to change. Hence, our white paper also focuses on the role of universities and on innovative, future-oriented leaders who have the courage for change, agility, and resilience.

ASEFInnoLab7

The programme aims to strengthen cross-regional networks and dialogue on the role of universities in AI innovation ecosystems by building institutional capacity, promoting ethical and inclusive AI practices, and generating actionable outputs to inform strategic policy and leadership in higher education. In this edition, we will deliver a full programme with virtual and in-person elements in various formats throughout the year. About 60 higher education experts, professionals, academics, and researchers from Asia and Europe will participate. I am proud and delighted to be part of this programme. White papers produced through this process will articulate strategic pathways through which higher education institutions can align AI development and deployment with societal needs, ethical principles, and long-term public value. The white papers will address issues within the three subthemes: AI4GlobalGood, AI4PublicInterest, and AI4InstitutionalExcellence.

All teams’ white papers should be ready and presented at the conference in Nottingham, UK, from 24 to 29 August 2026.

I had the great pleasure and honor to contribute and to speak at BILTEVT'2025 "UNIVERSAL DESIGN IN INFORMATION TECHNOLOGIES" -INTERNATIONAL BARRIER-FREE INFORMATION 2025 CONGRESS BILTEVT'2025 " October 15-17, 2025, Manisa. The Opening Ceremony and all sessions of the congress were held in the Akide Sultan Hall, which was broadcast live on the Barrier-Free Informatics YouTube Channel.  Sign language interpretation was available during the sessions.

My session was on Leadership and learning in the age of AI: Towards inclusive, ethical, and human-centered transformation, as program below. The presentation can be downloaded here at Slideshare Ossiannilsson.

My very first message was that education and leadership must evolve for a world shaped by AI, uncertainty, and complexity. 

As my topic was Leadership and learning in the age of AI, I also focused on the ecosystem of lifelong learning in this era, emphasizing that learning is lifelong, lifewide, and lifedeep. There is a need for collaboration between academia, policy, and civil society, and flexible pathways, micro-credentials, and recognition of informal learning

The Elephant in the room

The most important insights I took away from the summit concern the elephant in the room or the attacking elephant.
The natural human response to an encounter with an elephant is fight, flight, or freeze; what should we do? Another insight was the discussion on ontological and epistemological development for open education and ethical leadership in the era of AI. See also my LinkedIn post from after the conference beginning of June 2024 

I was involved in several events myself

• ICDE Board meeting Strategy plan 2025-2028
• Panel Moderator at the session on AI creating knowledge (s), teaching and learning
• Chair and presenter of the ICDE OERAC WORKSHOP on Navigating the Future: Ethical AI Leadership in Open Education and Inclusive OER Learning
• Quality review on behalf of ICDE at University Terbula Indonesia, 2016, 2019 and now 2023. During the ICDELS24 in Geneva U Terbuak was awarded  the certificate of completion 
• Launch of the ICDE TIN Technology Innovation Network
• ICDE Global Advocacy Campaign
• ICDE Launch of the TIN, Technology Innovation Network
• Dissemination of Call for articles in Sustainability Special Issue on Sustainable Education in the Age of Artificial Intelligence (AI)