WSIS Action Lines C4 and C7:E-employment: Emerging technologies in the world of work: Addressing challenges through digital skills

Summary

This discussion focused on preparing the workforce for the digital future, particularly addressing the challenges and opportunities presented by artificial intelligence and rapidly evolving skill requirements. The panelists, including representatives from Microsoft, ITU, ILO, and UNESCO, explored strategies for developing future-ready skills in an era of technological transformation.

A key finding highlighted by Microsoft’s Anupama Shekhar was that LinkedIn data projects a 70% change in skills needed for most jobs by 2030, emphasizing the urgent need for adaptive learning approaches. The discussion emphasized three critical strategies: supporting innovative peer-to-peer learning models, developing locally generated but globally relevant programs, and building capacity within educational and training systems rather than just targeting individual learners directly.

The panelists stressed the importance of comprehensive policy frameworks, with ILO’s Dorothea Schmidt-Klau advocating for inclusive national employment policies that integrate digitalization strategies with employment frameworks. She highlighted the need to update international labor standards to address digital transformation while ensuring decent work conditions. ITU’s Susan Teltscher discussed scaling challenges, particularly in reaching underserved rural communities that lack internet access and require resource-intensive, localized approaches.

The conversation addressed AI’s dual role as both a tool for learning and a subject requiring literacy, with emphasis on developing critical thinking and human skills alongside technical competencies. Participants raised concerns about assessment challenges in higher education, the need for continuous course updates due to rapid technological change, and gender disparities in AI skill development. The discussion concluded with calls for strengthened partnerships between public and private sectors, emphasizing that collaboration is essential for creating sustainable, scalable solutions to workforce development challenges in the digital age.

Keypoints

## Major Discussion Points:

– **Rapid Skills Transformation and Future-Readiness**: The discussion emphasized that by 2030, approximately 70% of skills needed for most jobs will change, requiring new approaches to defining and achieving “future-readiness” in the workforce. This includes supporting innovative learning models and locally-generated but globally-relevant programs.

– **AI’s Impact on Learning and Education**: Participants explored both opportunities and challenges of AI in education, including concerns about assessment authenticity, the need for AI literacy frameworks, and the importance of maintaining human skills like critical thinking and communication alongside technical capabilities.

– **Policy Integration and Scaling**: The conversation highlighted the critical need for comprehensive national employment policies that integrate digitalization strategies, active labor market policies, and updated international labor standards to make skills development initiatives sustainable and scalable.

– **Bridging the Digital Divide and Reaching Underserved Communities**: Significant attention was given to the challenge of reaching rural and underserved populations who lack internet access and digital infrastructure, requiring resource-intensive community-based approaches and strong partnerships.

– **Partnership and Ecosystem Building**: Throughout the discussion, speakers emphasized that effective skills development requires building capacity not just in individual learners, but in the entire ecosystem of educational institutions, government agencies, and private sector partners working together.

## Overall Purpose:

The discussion aimed to explore strategies for developing digital and AI skills at scale, with particular focus on how international organizations, governments, and private sector partners can collaborate to prepare workforces for rapid technological change while ensuring inclusive access to training opportunities.

## Overall Tone:

The tone was collaborative and solution-oriented throughout, with participants building on each other’s insights rather than debating. There was a sense of urgency about the pace of technological change, but also optimism about the potential for partnerships and innovative approaches to address the challenges. The discussion maintained a practical focus on actionable strategies while acknowledging the complexity of the issues at hand.

Speakers

**Speakers from the provided list:**

– **Tom Wambeke**: Moderator of the session

– **Dorothea Schmidt-Klau**: Works for the ILO (International Labour Organization), focuses on employment policies and international labor standards

– **Anupama Shekhar**: Works with Microsoft, focuses on digital skills development and AI literacy programs

– **Susan Teltscher**: Works with ITU (International Telecommunication Union), involved in digital skills training and the ITU Academy

– **Maria Cristina Cardenas Peralta**: Government Affairs in Coursera for Latin America and the Caribbean

– **Nidhi Gopal**: International candidate with specialization in VLSI design and engineering (participated online)

– **Sarah-Jane Fox**: Dr. Sarah-Jane Fox from the Institute for Digital Culture

– **Participant**: Valeska Guerrero – Expert on sustainable infrastructure, also mentioned another unnamed participant who discussed unemployed graduates and AI academies

– **Gianluca Musraca**: Dr. Gianluca Musraca, described as a futurist working on AI governance and digital transformation projects, involved with UNESCO competence frameworks

**Additional speakers:**

None identified beyond those in the provided speakers names list.

# Preparing the Workforce for the Digital Future: A Comprehensive Discussion on AI and Skills Development

## Executive Summary

This comprehensive discussion, moderated by Tom Wambeke, brought together representatives from major international organisations and private sector leaders to address the critical challenge of preparing the global workforce for an AI-driven future. The main panel included Susan Teltscher from the International Telecommunication Union (ITU), Anupama Shekhar from Microsoft, Dorothea Schmidt-Klau from the International Labour Organization (ILO), and Gianluca Musraca working on AI governance and digital transformation. The session also featured active participation from audience members including Maria Cristina Cardenas from Coursera, Dr. Sarah-Jane Fox from the Institute for Digital Culture, and Nidhi Gopal participating online.

The discussion centred on the urgent need to address rapidly evolving skill requirements in an era of technological transformation, with particular emphasis on artificial intelligence’s impact on education, employment, and workforce development. A key finding highlighted by Microsoft’s research revealed that 70% of skills needed for most jobs will change by 2030, establishing the urgency that permeated the entire conversation.

## The Scale of Transformation: Understanding the 70% Skills Change

Anupama Shekhar from Microsoft opened the substantive discussion with a striking statistic that fundamentally reframed the conversation from theoretical future planning to immediate crisis management. LinkedIn data projects that by 2030, approximately 70% of skills needed for most jobs will change, requiring a complete reconceptualisation of what it means to be “future ready” in the workforce.

Shekhar emphasised that this transformation necessitates three critical strategies for effective response. First, organisations must support innovative models such as peer-to-peer learning platforms like Goodwall that enable young entrepreneurs to learn from each other in dynamic, collaborative environments. Second, there is a crucial need for locally generated but globally relevant programs that go beyond simple language translation to achieve true cultural localisation. Third, the focus must shift from building capacity in individual learners to strengthening the entire ecosystem of educational institutions and systems that deliver training.

Microsoft has responded to these challenges by partnering with one of the largest teachers’ unions in the U.S. to create AI academies for teachers, recognising that transforming learning environments requires working with educators rather than bypassing them. Shekhar also discussed the TeachAI framework, which provides competency definitions and policy considerations for educational implementation, moving beyond simple technical training to encompass broader capabilities including ethical AI use and critical evaluation of AI outputs.

## Scaling Through National Policy Frameworks

Dorothea Schmidt-Klau from the ILO provided crucial insights into the policy dimensions of skills development, arguing that inclusive national employment policies are essential to scale up and make digital skills initiatives sustainable. She made a particularly thought-provoking observation about the need for systemic transformation: “It’s not only the people who need to be future ready. It’s also our own international labour standards that need to be future ready… Every single labour standard needs to be checked, whether it is still relevant.”

This comment expanded the scope of ‘future readiness’ beyond individual skills to institutional and regulatory frameworks, recognising that the challenge extends far beyond training people differently to fundamentally restructuring the systems that govern work. Schmidt-Klau emphasised that international labour standards require updating to include digitalisation, digital skills, and lifelong learning components.

The ILO representative highlighted successful partnerships such as the Decent Jobs for Youth initiative partnering with Microsoft, demonstrating how public-private collaboration can effectively scale digital skills training. However, she also raised concerns about reaching the most vulnerable populations, particularly NEETs (not in education, employment, or training) who are completely disconnected from systems and whom “we don’t even know where they sit.”

## Regional Acceleration and Cross-Border Challenges

Susan Teltscher from the ITU addressed the practical challenges of scaling digital skills initiatives across regions and borders. She noted that the ITU Academy runs more than 150 courses annually for all countries, demonstrating the scale of international coordination required. However, she highlighted a critical implementation challenge: “Those that are in the underserved communities, they don’t have access to Internet at home. They don’t have a computer… So if you want to train them, how do you do that? You have to go out and reach them in their communities… This is very resource intensive.”

This observation grounded the high-level discussion in practical realities, highlighting the digital divide as a fundamental barrier to scaling AI and digital skills training. Teltscher emphasised the need for partnerships to reach underserved communities and stressed that lifelong learning approaches must recognise that not everyone has innate digital capabilities.

The ITU has been working on regional acceleration initiatives and cross-border knowledge sharing, recognising that skills development challenges transcend national boundaries and require coordinated responses. Teltscher also emphasised the importance of balancing AI tool usage with maintaining fundamental knowledge and soft skills.

## AI’s Future Impact and Governance Challenges

Gianluca Musraca, working on AI governance and digital transformation projects, introduced critical concerns about the pace of technological change and its implications for policy-making. He made a particularly striking observation: “Look at what happened with ChatGPT, we were not prepared for that and now this is already past. The agentic AI phenomenon is already changing completely our organisations… we may actually end in a very dystopian future that we should try to avoid.”

Musraca emphasised that policymakers often lack understanding of AI implications and need better engagement and education to make evidence-based decisions. He advocated for multidisciplinary approaches that extend beyond technical skills to include procurement, change management, and public service delivery. He also promoted modular, personalised training approaches with micro-credentials as necessary adaptations to diverse skill development needs in rapidly changing environments.

His work on the AI for Gov project demonstrates practical applications of these principles, focusing on helping government officials understand and implement AI technologies responsibly. Musraca stressed the importance of moving beyond superficial AI training to develop deeper, more comprehensive understanding of technological implications.

## Audience Insights and Practical Challenges

The discussion was enriched by substantial audience participation that highlighted practical implementation challenges. Valeska Guerrero raised important concerns about the balance between AI productivity tools and foundational knowledge, questioning whether increased efficiency might come at the cost of deep understanding.

Dr. Sarah-Jane Fox from the Institute for Digital Culture posed critical questions about assessment challenges in higher education, asking how institutions can effectively evaluate students when AI tools are widely used. She expressed concern about the risk that students might develop AI skills without acquiring fundamental knowledge and expertise.

Maria Cristina Cardenas from Coursera provided valuable data on gender disparities in AI education, revealing that only 32% of women enroll in AI courses globally. She also highlighted the challenge of keeping course content current, noting that platforms must constantly update their offerings due to rapid AI innovation.

One participant suggested creating networks to help unemployed graduates in developing countries create AI-based startups addressing Sustainable Development Goals, highlighting the potential for international collaboration to address both skills development and broader development challenges.

Nidhi Gopal, participating online as an international candidate specialising in VLSI design and engineering, inquired about opportunities with the ILO, demonstrating the global reach and interest in these initiatives.

## Key Areas of Consensus and Collaboration

Despite representing different sectors and perspectives, the panellists demonstrated remarkable consensus on several key principles. There was universal agreement that partnerships are essential for scaling digital skills initiatives, with speakers consistently emphasising that effective scaling cannot be achieved by individual organisations alone.

The panel also reached strong consensus that AI should complement rather than replace human capabilities, with particular emphasis on maintaining critical thinking, communication skills, and fundamental knowledge. This human-centred approach to AI integration emerged as a core value shared across all participants.

All speakers recognised that rapid technological change necessitates adaptive and modular training approaches rather than traditional static educational models. The emphasis on lifelong learning as a response to continuous technological evolution was another area of strong agreement.

## Proposed Solutions and Next Steps

The discussion generated several concrete proposals for moving forward. Participants agreed to continue partnerships between UN organisations and private sector partners for skills training initiatives, building on successful models like the Decent Jobs for Youth initiative.

There was support for developing and implementing AI literacy frameworks with defined competencies for educational policy, as demonstrated by Microsoft’s TeachAI initiative. The creation of modular, personalised training approaches with micro-credentials was proposed to adapt to diverse skill development needs.

The launch of initiatives like the AI Skills Coalition and Digital Transformation Center was discussed as ways to promote training and reach underserved communities. Participants also agreed to follow up with smaller group conversations in subsequent sessions to take actions forward and solve identified challenges.

## Final Reflections from Panellists

In closing remarks, each panellist offered their perspective on moving forward. Anupama Shekhar emphasised the need to “dream big and dream small” – thinking systemically about transformation while maintaining focus on individual learner experiences and outcomes.

Gianluca Musraca stressed the importance of multidisciplinary approaches and evidence-based policy-making, drawing on his experience with the AI for Gov project to emphasise the need for comprehensive understanding rather than superficial training.

Dorothea Schmidt-Klau highlighted the challenge of reaching disconnected populations and adapting to changing generational aspirations, noting that “young people are born with digital skills and have different aspirations that societies and teachers must learn to handle.”

Susan Teltscher reinforced the importance of lifelong learning and partnerships, emphasising that effective skills development requires recognising different starting points and learning needs across diverse populations.

## Conclusion

This comprehensive discussion revealed both the urgency and complexity of preparing the global workforce for an AI-driven future. The 70% skills change projection by 2030 established a clear timeline for action, whilst the various perspectives shared by panellists and audience participants highlighted the multifaceted nature of the challenge.

The strong consensus on key principles—particularly the need for partnerships, human-centred AI integration, and adaptive learning approaches—provides a solid foundation for collaborative action. However, the challenges around assessment, gender inclusion, reaching underserved populations, and policy development indicate that significant work remains.

The discussion demonstrated that effective workforce preparation for the digital future requires simultaneous action across multiple dimensions: individual skills development, institutional capacity building, policy framework updates, and international coordination. The collaborative tone and commitment to follow-up sessions suggest strong potential for translating these insights into concrete action that addresses both the technical and human dimensions of digital transformation.

Susan Teltscher: Dr. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schimdt-Klau, Dr. Dorothea Schmidt-Klau, Dr. Dorothea Schmidt-Klau, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Dr. Dorothea Schmidt-Klau, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Dr. Dorothea Schmidt-Klau,

Tom Wambeke: Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar,

Anupama Shekhar: Dr. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Dr. Dorothea Schmidt-Klau, Dr. Dorothea Schmidt-Klau, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Janelle Monae, Dr. Janelle Monae, interesting, but there’s definitely challenges that come with that as well. Another data point that we’re seeing is with LinkedIn, with our LinkedIn data, when we kind of scan all of the LinkedIn profiles and our LinkedIn economic graph team has put together that talks about these changes in skills that we anticipate. And what we’re seeing is that by 2030, we’re anticipating that for all the jobs, for most jobs, by 2030, we’re going to see a 70% reduction in skills. So 70% change in skills that are needed for most jobs by 2030. So 70% change in skills needed for most jobs by 2030. So that’s a significant shift and a significant, you know, when we say future ready, that definition is constantly changing as well. And so I think what it means in terms of strategies, which was your question, especially as we think about what private sector can do to lean in. One big aspect of that, I think, is supporting innovative models. And I think innovation is key to keep up with that change, to keep up with the readiness changes. One example of something we’ve done to support innovative models is we’ve worked with an organization called Goodwall that is working across multiple countries. In Africa, they’ve set up a peer-to-peer learning platform to help young entrepreneurs be able to connect with each other, to be able to learn, you know, AI skills and get credentialed access as well from that, and be able to share and build a community. Dr. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, locally generated programs, but globally relevant programs. So locally, you know, generated, globally relevant programs. An example of where we’re doing that, we’re actually, you know, we’ve developed a lot of free learning curriculum, free learning content around AI, but also digital skills more broadly, that we’ve made available around the world, and we’ve localized into multiple, you know, cultural contexts, not just languages. And we’re actually developing new signature content as well for teachers, for civil society professionals, for government workers, to the points that were brought up earlier, to help equip government employees as well to gain access to some of those skills, again, building and structuring to local context. And then the third thing that I would say is actually related to the point that I think Dr. Gianluca, you made earlier. All of this can only happen through, not just by going directly to the learners and supporting their capacity, but by building the capacity of the systems that are doing this work. So whether it’s educational institutions. Dr. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, local approaches, globally relevant approaches, and then the third is building the capacity of the systems that are doing the work.

Tom Wambeke: Thanks a lot, Anu, for giving a more nuanced view on what we mean by future-ready, and if I look at all these different efforts, I have a question for Dorothea. How could these efforts also be scaled up in national labor policies, according to you?

Dorothea Schmidt-Klau: Thanks a lot. I think, and I said it before, and other speakers said it in other sessions, to scale things up and to make them sustainable, you need policies. You need policies to back up individual actions and interventions, to make them lasting and to make them repeatable and scalable. And for that, as I already said, inclusive national employment policies are ever so relevant. We see, as you said, many countries now have digitalization strategies. And you were saying, who are the stakeholders, who need to be on the table? Well, all the stakeholders you mentioned are already on the table when we develop employment strategies, employment frameworks. So it’s so easy to connect, because at the end of the day, what you are saying is part and partial of what we need for a successful employment strategy. And we actually do see in many countries that ministries of digitalization, or whatever they are called, are linking with ministries of employment because of the close connection of the two. So I think, if you want to scale up, make sure that all relevant strategies come together. the umbrella of National Employment Strategies or Comprehensive Employment Policy Frameworks. Now you might ask, is this actually happening? And in this regard, and people are always asking for examples, have a look at the youth guarantee schemes that we work with the EU in EU countries, in many Balkan countries. They exactly take this approach. They really make an effort that all perspectives when it comes to digitalization are included in the way forward. And they are also included in what follows after an employment strategy, which is the active labor market policies. Active labor market policies translate your ideas into practice, especially for those people who not automatically find a job because of new developments. So here we are talking about the young people who find it still very difficult, despite the fact that they have digital skills, they find it very difficult in their transition into labor markets to find a decent job immediately. Now we talk about being future ready. It’s not only the people who need to be future ready. It’s also our own international labor standards that need to be future ready. When you work for the ILO, it’s all about creating international labor standards, but it’s also about updating international labor standards. And we put a lot of effort into updating these international labor standards, particularly those related to skills. And there are new elements that were not there when the standards were created, but need to be added. And for example, this is, of course, the whole area of digitalization, digital skills. It’s also a lot about lifelong learning, because it’s not the first time that we find that all of a sudden the older workers need some skills that they were never trained on. And this would not have happened if we trained them all along their life, because then automatically digital skills would have come in at one certain point. It’s also about, you know, as we said already, updating those international labor standards that are linked to public employment services, because it’s a different thing they do now than they used to do in the past. Every single labor standard needs to be checked, whether it is still relevant, because at the end of the day, these international labor standards set the frame for how the transition to the digital economy takes place. And to make sure that there is nobody lost on the way, that this digitalization does not only lead to more jobs, but also to better jobs, because that’s the ultimate goal. And I just want to stress that in this endeavor, there are two elements that are super important. The one was already mentioned, and that’s the knowledge we need to have. And our AI Observatory is such a platform where we actually try to gather the knowledge and gather the data so that we can make sure that everybody profits from the information, not just the rich countries, but more particularly also the not so well-off countries. And the other thing I want to mention is actually partnerships that are so essential, okay? We as the UN, we don’t create a single digital job. We don’t skill a lot of young people, maybe some in some projects. But our goal is to be there, to set the frame, to make sure that the development goes into a direction that we want it to go and that we drive the development and not the development drives what’s happening to people. So I think this is very important. And for example, when it comes to partnerships, it’s very important that we have these public-private partnerships. And for example, our initiative, Decent Jobs for Youth, that one of our major partners is actually Microsoft. That really shows that then how we can translate these updated standards to having an impact. on the Sustainability Perspective and on the scalability discussion.

Tom Wambeke: I have one follow-up question for Susan. If you look at the regional or the cross-border level, what is currently being done to accelerate scale?

Susan Teltscher: A lot in short, but in long. In fact, there are so many players, they are all now focusing on digital and digital skills. You have heard it a lot. You hear it more and more now in the events like here, the WSIS, etc. And from the UN point of view, many, many UN organizations are now involved in providing skilling and training in their respective fields. We have worked closely with ILO, Dorothea, you mentioned the Decent Jobs for Youth initiative. In fact, this was when the ITU-ILO digital skills campaign was launched, which was in 2018. So many years ago already, we were trying to promote digital skilling and Microsoft, thank you very much, one of the main partners here, who has committed to train millions of youth in digital skills. We have now launched in ITU the AI Skills Coalition recently with a focus more on AI. Again, it’s also to promote the outreach of training and skilling in this field. But even, or especially also from the implementing point of view in ITU, the ITU Academy was mentioned earlier. So we have been training policy makers and professionals in this field for decades, and we run more than 150 courses on ITU Academy. for all countries every year. So that’s also a contribution to this in terms of scaling. And I wanted to mention another initiative that we have, our Digital Transformation Center initiative. This is trying to reach the population in the rural communities, in the underserved communities. And when we talk about scaling, this is one of the main challenges that we actually have. It’s much easier to scale if people are already connected. You reach them online and you can train them, et cetera, et cetera. But those that are in the underserved communities, they don’t have access to Internet at home. They don’t have a computer. They may have a phone, but that’s about it. So if you want to train them, how do you do that? You have to go out and reach them in their communities. You have to work with local community leaders. This is very resource intensive. So you may have this in your national government digital skills strategy, but you have to put also the resources to it, to actually go and reach those vulnerable groups who still make up a large proportion of the population who is not connected. So for scaling, that requires resources and partnerships. I fully echo what you said before. Without partnerships, we can’t reach it. Thank you.

Tom Wambeke: Thanks a lot, Susan. I’m actually doing an AI-infused transcription of this dialogue, and I see one of the patterns, of course. We are also in parallel at the AI for Good Summit. AI is one of the recurrent patterns in the semantic analysis of what I see here. If I connect it then with learning, Anu, like that’s maybe again another new challenge. If we connect AI and learning and being a learning specialist myself, there are as many opportunities as there are challenges. You know, what would your perspective be on that topic?

Anupama Shekhar: Yeah. Thank you for that question, because I think it’s an important question. I think we often. Dr. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, which is one of the largest teachers’ unions in the U.S., focused on building an A.I. academy, supporting teachers in building their A.I. skills and knowledge. So I think programs like this can really help create and transform the learning environment and put A.I. in its place. I think one last thing I want to highlight on the policy side of things, we’re working with TeachA.I., which is a large, global nonprofit organization that has actually built an A.I. literacy framework. So if you’re looking to kind of understand, again, with some of your programs, how to support policy work for teachers, this creates, this essentially has a framework of competencies that define A.I. literacy. What are some of the policy considerations that go into defining those policies, those competencies? And then what are some tactical ways in which teachers and learning environments can be supported in bringing A.I.?

Tom Wambeke: Thanks a lot, Anu. And indeed, a lot of these A.I. discourses are sometimes a bit dominated by looking for efficiencies in learning, a lot of focus on productivity, but your reflections take A.I. literacy beyond that. You know, you mentioned human skills communication, but also critical thinking and so on. Gianluca, if I ask the question to you, it’s not only about digital skills development that we’re talking here, but about the future in a way. What does A.I. mean for the future of skills development, according to you?

Gianluca Musraca: Well, I mean, coming here this morning, I was thinking that, you know, 20 years have passed since Tunisia. I was in Tunisia at WSAS. and the world has completely changed for the good and for the bad and really technology is actually dominating our lives. But then, I mean, Dorothea mentioned rightly so that policy is the issue, policymaking, but which kind of policies? The problem, we don’t have really evidence about what are the implications, the impact of these technologies yet on our societies. We see a lot of risk, a lot of opportunities also, but the policymakers are not really often in the position to really take the right decision. So we really need to, as Susan was saying, we really need to engage them, making sure that they learn also what we are talking about, because sometimes we are really talking about this as something magic and we think things will be changed for the good, but actually they are creating more problems than we thought. So evidence is important, but also actually we’re talking about the future. I was also looking at the future is here lemma of the summit, but what kind of futures we want and actually are we really meaning future? I mean, I’m first and foremost a futurist and foresight is also an art that we need to make sure is part of the toolkit of the policymakers and everybody. So you mentioned competence frameworks, I’ve been working with UNESCO in refining and improving their competence framework exactly to integrate the AI part that is completely unknown. We talk about AI literacy, but we’re not really touching yet the top of what we are going to have in the next few months. Look at what happened with RGPT, we were not prepared for that and now this is already past. The agentic AI phenomenon is already changing completely our organizations. So we have to prepare ourselves for that big change in some industry, this will be completely, some industry will be completely transformed. Dr. Gianluca Musraca, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Ms. Anupama Shekhar, Dr. Dorotea Schmidt-Klau, Ms. Anupama Shekhar, Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar, Ms. Anupama Shekhar, Dr. Dorotea Schmidt-Klau, Ms. Anupama Shekhar, we may actually end in a very dystopian future that we should try to avoid.

Tom Wambeke: Thanks a lot, Gianluca. And given the fact that the future is always uncertain, I think it’s a very valuable suggestion to find synergies between AI literacy and futures literacy, foresight literacy. Now, AI is always based upon, let’s say, co-creation of collective intelligence. We have done it already based upon the four experts. We are with 60 people in the room, so I would like to open up the floor and tap into your collective intelligence. What you have heard so far from our four panelists, are there any specific, let’s say, comments, questions, feedback that you would like to add to the discussion? I have two questions here or comments at the back.

Participant: Thank you so very much. My name is Valeska Guerrero. I’m an expert on sustainable infrastructure, but also very interested in how AI can support intelligence. I’m more like in the data perspective, but at the same time, I have, well, I use, I would say, AI on a daily basis in the sense of improving, you know, how productive I am on a day-to-day basis. For instance, maybe a work plan that you would take, I don’t know, like an entire day of doing it, you can probably do it in less time. But at the same time, I’m actually, I mean, I agree a lot with what Dr. Gianluca, sorry, your last name, okay, but I mean, it’s really, really critical in the sense that we cannot just rely on AI. I mean, at the same time, I think our skills, our know-how, for instance, my background is on international business, but also project management. So I wouldn’t know how a project plan would look like if I wouldn’t have studied that before. Saying so, I think it’s really relevant that we use both the…

Tom Wambeke: Thank you. It just occurs to me that apart from obviously we want to train the young

Participant: people and so forth, if you look around, between 15 to 20% of graduates in most developing countries are unemployed because the governments have not been able to get their act together if you like. These are smart young people, bright people. This is what I’m trying to do with a network of people in about 34 countries at the moment, where we are sharing knowledge across borders. But one of the things we’re trying to do is to work with local AI academies, not necessarily government universities, but even lower level, to train young people and to train them not just about AI and learning how to do content management or something, based on the Sustainable Development Goals. In every one of the Sustainable Development Goals, there’s at least, I reckon, between five to 10 startups which could actually make a huge difference. They could be real businesses for these young people. And at the same time, it helps us to tackle the problem that we all have and we haven’t been able to solve. I’ve spent four decades trying to solve the problem. I retired and then AI came along. And I’ve dived back in again because I see that in five years, I’ll probably be able to do what I could not do in 40 years of trying. So I think there’s a great opportunity.

Tom Wambeke: Thanks a lot.

Sarah-Jane Fox: Hello, Dr. Sarah-Jane Fox from the Institute for Digital Culture. And just to pick up on a few comments, particularly from the lady behind me. Do you see or would you anticipate that there’s a difficulty, particularly when we’re looking at higher education, and our students who are rapidly using AI, sorry, but they are rapidly using AI, and it’s very difficult to assess them. So they may have the skills, knowledge of AI in the workplace, but not necessarily the background knowledge that you alluded to, with regards to knowing the expertise that they require, i.e. for a doctor, for business management, for being a lawyer, all those different skills that we can’t assess where the AI has come in and written the essay for them. So is there perhaps from a higher education perspective or from an AI tool, an indicator that we can put in where that has been used, you know, a way of detecting where AI has been utilised in education. Otherwise, what we’re going to get is actually a workforce that we’re assessing only for their AI skills,

Tom Wambeke: and not for their actual knowledge. Thanks a lot, which brings me to this corner.

Maria Cristina Cardenas Peralta: Hi, Cristina Cárdenas, Government Affairs in Coursera for Latin America and the Caribbean. And we have over 164 million students globally, 29 million belong to Latin America and the Caribbean. And we have 800 courses among 13,000 that we have for generative artificial intelligence. And we rotate the courses very often, like we have around 150 to 250 courses rotated every month from the same providers. And my question is, how are you doing to replace those in the ITU? Because the reason why they rotate the courses is because they have a lot of The reason why we update the courses is because the innovation is so fast that the producers of our content are replacing those contents. We have a lot of students demanding these courses, around 6.3 million students during the last year enrolled to Coursera to take those courses globally. And when the challenge is also the gender, we have only 32% of women taking those courses. So my questions are related to the replacement of courses here. How are you updating the courses that are changing all the time? And how are you doing to enroll more women in those fields? Thank you.

Tom Wambeke: Lots of questions and inputs and I’ve also been asked to close the digital divide as we have some people online here as well. So we have Nidhi Gopal who has also a final comment question before we go back to the panel. Nidhi, go ahead and unlock your microphone. Just unmute it as we don’t hear you right now. Yes, we can hear you right now. Yes.

Nidhi Gopal: Yeah, I just have one question. Does the International Labour Organization offer opportunities to the international candidates with a specialization in VLSI design, engineering and equipment with the latest skill set like AI and all and having six years of professional experience?

Tom Wambeke: Okay, thank you for this question, Nidhi. Targeted and to the point. So these were four or five different inputs and I’m also looking at the time here. We have about still seven minutes. So that allows more or less, let’s say almost two minutes for each panelist in which I would like to ask them if possible, maybe to address one or two of the questions that you have seen. But also we’re trying to extract something from this session. What are some of, let’s say, the key points that we need to take further into the conversations we already linked with some insights from the first session? I also would like to know which. key points we have to take further towards a call to action, what is urgent, what is needed. So these are the two entry points for your last two minutes pitch. And I would like to start with Anu and then

Anupama Shekhar: we just move on. Sure, thank you. So I want to address something that came up in terms of, you know, how do we help students, especially in higher ed institutions, how do we help them gain some of those responsibility skills to be able to use AI and use the tools in a responsible way. And also from an educator standpoint, how do we help them assess the students, you know, in the most accurate and safest way. I know that from, I think one big aspect of that is in building into some of these competency frameworks, building into the curriculum that we offer, the human skills, the responsible AI skills being a big, big proportion of what we teach students today. And then also what we’re hearing, we actually have a group of academic scholars that we work with. And what I was hearing from them is that the way that they’re assessing is also changing, they’re kind of adapting that in this new world. And there’s a lot more hands on assessment, there’s a bigger proportion of the sort of the grade that’s coming from the assessment. So we’re starting to see some trends in that direction as well. But I think there’s much more work to be done in this space. And I think there’s a lot of collective work that we need to do together to figure that out. I just want to I know you had sort of a question on call to action to the group. And I want to end by saying, I think for the best thing we can do is to probably dream big and dream small. And what I mean by that is, you know, what the point I was making earlier in terms of building the capacity of the the ecosystems, the systems that are doing this work, that’s the dream big part. And whether it’s through the skills coalition from the ITU, or some of the innovative programs that the ILO is running, like the Women in Digital Business Program, so dreaming big. But also Dreaming Small, thinking, you know, when we’re designing these programs, thinking about the individual teachers and the students and the learners that are going through the programs and designing the programs with each of their experiences in mind. And another way that I mean Dream Big and Dream Small is, you know, we have these big groups where we come together in panels, but let’s also follow up in Dream Small with smaller group conversations. Let’s take these actions forward and work towards solving some of these challenges and problems that we’re raising today.

Tom Wambeke: Thanks a lot, Anu. Gianluca, are you Dreaming Big, Dreaming Small?

Gianluca Musraca: Well, let me say, of course, I’m trying to link the different questions and comments. I fully agree with the colleagues on the back that, I mean, the issue here is about multidisciplinarity and it’s not just about the technical skills. So, you know, I was last week in a big conference and someone made a big announcement. We are training millions of civil servants on AI. They will all learn how to prompt. OK, so, well, maybe it’s not enough. We need to look at the procurement issues, how we manage the big change of the digital transformation. And this brings also to the comment on the back, also on the need to involve the youngest there, but also the startups. And there is a big movement about also GovTech when it comes to, you know, changing also the way we design and deliver public services and where artificial intelligence and other technologies can actually help a lot. But this is not enough. And I think Susan mentioned really the key word, Accelerate. And this is actually the name of my new project, AI for GovAccelerate or AI for GovX. That is, I mean, it’s a 20 million euro funded project by the EU, part of the half billion that the EU is funding on digital skills and especially on AI, but not only. And, I mean, these and other projects I’m involved in now are also… and Dr. Dorothea Schmidt-Klau. I’m going to start with a brief introduction of the AI. I’m going to start with a brief introduction of the AI. I’m going to start with a brief introduction of the AI. I’m going to start with a brief introduction of the AI. The first thing I’m going to do is to try to look at how we change the approach. I mean, the lady from Coursera is an example of how we should do things in order to have micro-credentials approach and exactly make sure that we can adapt with different modular approaches, the skills developments we have. We cannot just have, you know, a training that we use forever and for everybody. We need to be really, to have personalized training and AI can also help us in that. Then sometimes I say, oh, we are training people on AI. We have a 30-minute course. Okay, well, that’s maybe not enough. And that’s where we need also to make sure that academics are really involved and we have credentials and quality certifications. Otherwise, you risk to make more problems than create, you know, a better equipped workforce. Just to conclude on that, we also have to make sure that linking the interdisciplinary approach with also the new needs of governments and society, we can scale this up. So in our program, we are aiming at, and that’s maybe the dreaming, the small and the big. We had the AI for Gov pilot we have done now. We are entering the fifth edition, self-sustainable, and we don’t need any more funding from the EU. We have trained 200 people, but they’re all actually at the highest level. I don’t know, now working in the AI office of the EU or in a different government. And so they are creating the ecosystem. Now we are aiming at training thousands of people. But then now hopefully we’re partnering with ITU and UNESCO and we can try and train millions of people all over the world using the approach that we have that is also experiential learning. It’s not just a training on theories or on techniques, but actually how we use data. Someone mentioned the importance of data, otherwise AI is useless. We need to know how we use the data, which data to use, and then how we can use this data and artificial intelligence technologies to make a transformation of our governance and our societies.

Tom Wambeke: Thank you Gianluca for this. Insightful prompts for follow-up. Moving to Dorothea, your key takeaways.

Dorothea Schmidt-Klau: Thank you very much. And I tried to answer some of the questions. Unfortunately, the one coming from the online participant, I need to refer to HR, to our human resource department to discuss it further. But I can tell you that, you know, young and already experienced colleagues are driving our AI agenda and are pushing us older people to really make use of it. What I wanted to say is first to the question regarding the unemployment. Well, unfortunately, the unemployment is only the tip of the iceberg. The real, real problem is all these young people, the so-called needs that are not in education, not in employment, not in training. They are lost. We don’t even know where they sit. And it’s really, really dangerous that this highly qualified generation and especially this highly qualified female generation, because it’s much worse for women than for men, is lost somewhere and we are not using their potential. So I absolutely agree. And having young people that have the skills opening their own businesses is certainly one of the important approaches that is actually anchored in literally every employment policy. I also wanted to say something about your point, the teachers, you know, the importance of teachers. Well, we do have to equip them with the skills to teach what young people need to know. But we also have to equip them and societies with the means to handle a completely different new generation. The young people, they come, they are born with digital skills, sorry to say it. You know, they grow up with it. We now often have the situation that they have more knowledge on technology than actually the teachers have. And with this comes a complete new set of aspirations. Young people today have very different aspirations and we need to learn as societies and as teachers to actually handle these new aspirations. Ms. Susan Teltscher, Dr. Dorothea Schmidt-Klau, Ms. Anupama Shekhar Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau Dr. Anupama Shekhar, Dr. Dorothea Schmidt-Klau Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau Ms. Anupama Shekhar, Dr. Dorothea Schmidt-Klau

Tom Wambeke: Susan, you have the honor to close the loop.

Susan Teltscher: Great. So I think dreaming big is good, but dreaming by itself is not sufficient. So let’s look at what can be done. And maybe first to answer Christina’s question or answering, I mean, we can have a dialogue on this, but we are both in the business of delivering, so that’s good. We are actually doing things to train people. And yeah, it is definitely very important in terms of the updating. You referred to that of what we are training and the courses, et cetera. And this is certainly something that we do on ITU Academy. Most of our courses are not recurrent. We do have, but in fact, a lot of the courses are created when they are put up. So in terms of the adaptation and the updating, it’s a very important part. We are also looking into other ways of moving away from the traditional courses into micro credentials and modules and micro learning. So this is something we are also trying to do on the platform to scale and to reach out and to adapt to the ways of learning. But coming back to our discussion here, and there’s a few things I think are really good to also for us to take away at the end. One is on the lifelong learning that was mentioned by several people. I think you also talked again about everybody who is born today comes with digital skills. Okay, what does that mean? And what does it mean for those who are not born today? And we sometimes forget this other part of the population who needs to also catch up with what’s going on and actually may need a lot more attention than than what we think. So the lifelong learning is very important. The other one is also, there was a lot of talk about soft skills. And this is, I think everybody knows, it’s been raised in many, many forums, etc. But it’s important to not just look at AI skills, but actually, how does the use of AI impact on other skills and knowledge, especially what we have heard? And how do we should also not forget about that? Because by using the technologies, are we then forgetting other skills? Or do we need to retrain on other skills and the communication part? And where does where does the knowledge base go? So these are also important aspects, I think in the future, we’ll get a lot more attention, especially in the education system. So at the end, partnerships, we talked about partnerships, let’s also end with partnerships, because that’s something that has worked very well in the past, we are here also working together. And that’s what we at ITU do, as well. And if I may say that we have a session at two o’clock, where we talk more about the work in ITU on skills development. And if anybody’s interested in partnering, come and talk to us. We are very interested in, in expanding on that and reaching

Tom Wambeke: out. Thank you very much. Thanks a lot, Susan. And indeed, let’s continue the conversation. There’s also at two o’clock, some ILO colleagues will also present their things. So it’s a partnership. So I wanted to make sure that there was a balance in there. But let me also thank you, because as a moderator, it was a pleasure to do this. So far, I’m not yet replaced by my own digital twin, not yet. But I must admit that my digital twin helped me a lot to prepare this session. So Susan, Gianluca, Dorothea, and Anu, thanks a lot. I would say keep up the momentum and a round of applause for the panel and the participants. It’s going to be very interesting, my colleagues are managing it, so it’s going to be great to be able to do that.

Agreement points

Partnerships are essential for scaling digital skills initiatives

Speakers

– Anupama Shekhar
– Dorothea Schmidt-Klau
– Susan Teltscher

Arguments

Importance of building capacity in educational institutions and systems that deliver training, not just direct learner support

Public-private partnerships are essential, with examples like the Decent Jobs for Youth initiative partnering with Microsoft

Reaching underserved rural communities without internet access requires resource-intensive community-based approaches and local partnerships

Summary

All three speakers emphasized that effective scaling of digital skills development cannot be achieved by individual organizations alone and requires collaborative partnerships between public and private sectors, educational institutions, and community organizations.

Topics

Capacity development | Future of work | Digital access

Lifelong learning is crucial for workforce adaptation

Speakers

– Dorothea Schmidt-Klau
– Susan Teltscher

Arguments

Older workers need continuous skill updates throughout their careers to avoid sudden obsolescence

Lifelong learning is crucial for those not born into the digital age who need to catch up with technological developments

Summary

Both speakers agreed that continuous learning throughout one’s career is essential to prevent skill obsolescence and ensure all generations can adapt to technological changes.

Topics

Future of work | Online education | Capacity development

AI should complement rather than replace human capabilities

Speakers

– Anupama Shekhar
– Susan Teltscher
– Tom Wambeke

Arguments

AI should enhance rather than replace human skills, with emphasis on communication, critical thinking, and responsible AI use

Balance needed between using AI tools and maintaining fundamental knowledge and soft skills

AI literacy should extend beyond efficiency and productivity to include human skills like communication and critical thinking

Summary

There was strong consensus that AI integration should strengthen human capabilities rather than substitute them, with particular emphasis on maintaining critical thinking, communication skills, and fundamental knowledge.

Topics

Online education | Human rights principles | Future of work

Rapid technological change requires adaptive and modular training approaches

Speakers

– Anupama Shekhar
– Gianluca Musraca
– Maria Cristina Cardenas Peralta

Arguments

70% of skills needed for most jobs will change by 2030, requiring constant adaptation of “future ready” definitions

Modular, personalized training approaches with micro-credentials are necessary to adapt to diverse skill development needs

Course content requires constant updating due to fast AI innovation, with platforms rotating 150-250 courses monthly

Summary

All speakers recognized that the rapid pace of technological change necessitates flexible, modular, and continuously updated training approaches rather than traditional static educational models.

Topics

Online education | Future of work | Digital standards

Similar viewpoints

Both speakers emphasized the importance of creating frameworks that are locally contextualized but globally applicable, whether in educational programs or international standards.

Speakers

– Anupama Shekhar
– Dorothea Schmidt-Klau

Arguments

Need for locally generated but globally relevant programs with cultural localization beyond just language translation

International labor standards need updating to include digitalization, digital skills, and lifelong learning components

Topics

Online education | Cultural diversity | Digital standards

Both speakers highlighted significant gender disparities in digital skills and employment, with women being underrepresented and facing greater challenges in accessing opportunities.

Speakers

– Dorothea Schmidt-Klau
– Maria Cristina Cardenas Peralta

Arguments

Young qualified women face worse unemployment than men, representing lost potential in the workforce

Only 32% of women enroll in AI courses globally, indicating significant gender gaps in AI education

Topics

Gender rights online | Future of work | Online education

Both speakers stressed that effective AI integration requires comprehensive approaches that go beyond technical training to include broader competencies and proper evaluation methods.

Speakers

– Gianluca Musraca
– Sarah-Jane Fox

Arguments

Need for multidisciplinary approaches beyond technical skills, including procurement, change management, and public service delivery

Assessment methods in higher education must adapt to detect AI usage while ensuring students develop actual expertise, not just AI skills

Topics

Online education | Future of work | Digital standards

Unexpected consensus

Generational skills gap and role reversal in digital knowledge

Speakers

– Dorothea Schmidt-Klau
– Susan Teltscher

Arguments

Young people are born with digital skills and have different aspirations that societies and teachers must learn to handle

Lifelong learning is crucial for those not born into the digital age who need to catch up with technological developments

Explanation

There was unexpected consensus on the role reversal where younger generations often have more technological knowledge than their teachers, requiring educational systems to adapt to handle different learning dynamics and aspirations.

Topics

Online education | Digital identities | Future of work

Evidence-based policy making challenges

Speakers

– Gianluca Musraca
– Dorothea Schmidt-Klau

Arguments

Evidence-based policymaking is crucial as policymakers often lack understanding of AI implications and need engagement and education

Inclusive national employment policies are essential to scale up and make digital skills initiatives sustainable

Explanation

Both speakers unexpectedly converged on the challenge that policymakers often lack sufficient understanding of digital technologies to make effective policies, requiring better education and evidence-based approaches.

Topics

Future of work | Digital standards | Human rights principles

Overall assessment

Summary

The speakers demonstrated strong consensus on key principles including the need for partnerships, lifelong learning, human-centered AI integration, and adaptive training approaches. There was also agreement on challenges such as gender gaps, generational differences, and policy-making difficulties.

Consensus level

High level of consensus with complementary rather than conflicting viewpoints. The speakers represented different sectors (private, international organizations, academia) but shared similar values about inclusive, sustainable, and human-centered approaches to digital skills development. This strong alignment suggests a mature understanding of the challenges and potential collaborative solutions for digital skills development in the AI era.

Different viewpoints

Speed and depth of AI training for policymakers

Speakers

– Gianluca Musraca
– Anupama Shekhar

Arguments

Evidence-based policymaking is crucial as policymakers often lack understanding of AI implications and need engagement and education

AI literacy frameworks provide competency definitions and policy considerations for educational implementation

Summary

Gianluca criticizes superficial AI training (30-minute courses) and emphasizes the need for deep, evidence-based understanding, while Anupama focuses on structured frameworks and competency-based approaches that can be more systematically implemented

Topics

Digital standards | Future of work | Capacity development

Generational digital skills assumptions

Speakers

– Dorothea Schmidt-Klau
– Susan Teltscher

Arguments

Young people are born with digital skills and have different aspirations that societies and teachers must learn to handle

Lifelong learning is crucial for those not born into the digital age who need to catch up with technological developments

Summary

Dorothea assumes young people are naturally born with digital skills, while Susan questions this assumption and emphasizes that not everyone has innate digital capabilities, requiring more nuanced approaches to lifelong learning

Topics

Online education | Digital access | Future of work

Unexpected differences

Role of partnerships in scaling solutions

Speakers

– Dorothea Schmidt-Klau
– Susan Teltscher

Arguments

Public-private partnerships are essential, with examples like the Decent Jobs for Youth initiative partnering with Microsoft

Reaching underserved rural communities without internet access requires resource-intensive community-based approaches and local partnerships

Explanation

While both advocate for partnerships, they have different perspectives on their role. Dorothea sees partnerships as essential for translating standards into impact at scale, while Susan emphasizes the resource-intensive nature and challenges of partnerships, particularly for reaching underserved communities

Topics

Capacity development | Digital access | Future of work

Overall assessment

Summary

The discussion shows relatively low levels of direct disagreement, with most conflicts being subtle differences in emphasis and approach rather than fundamental opposition. Main areas of disagreement include the depth versus breadth of AI training, assumptions about generational digital skills, and implementation strategies for scaling solutions.

Disagreement level

Low to moderate disagreement level. The speakers generally share common goals around digital skills development, AI integration, and inclusive access, but differ on implementation strategies, assumptions about target populations, and the appropriate depth of training. These disagreements are constructive and reflect different professional perspectives rather than fundamental conflicts, suggesting good potential for collaborative solutions.

Partial agreements

Similar viewpoints

Both speakers emphasized the importance of creating frameworks that are locally contextualized but globally applicable, whether in educational programs or international standards.

Speakers

– Anupama Shekhar
– Dorothea Schmidt-Klau

Arguments

Need for locally generated but globally relevant programs with cultural localization beyond just language translation

International labor standards need updating to include digitalization, digital skills, and lifelong learning components

Topics

Online education | Cultural diversity | Digital standards

Both speakers highlighted significant gender disparities in digital skills and employment, with women being underrepresented and facing greater challenges in accessing opportunities.

Speakers

– Dorothea Schmidt-Klau
– Maria Cristina Cardenas Peralta

Arguments

Young qualified women face worse unemployment than men, representing lost potential in the workforce

Only 32% of women enroll in AI courses globally, indicating significant gender gaps in AI education

Topics

Gender rights online | Future of work | Online education

Both speakers stressed that effective AI integration requires comprehensive approaches that go beyond technical training to include broader competencies and proper evaluation methods.

Speakers

– Gianluca Musraca
– Sarah-Jane Fox

Arguments

Need for multidisciplinary approaches beyond technical skills, including procurement, change management, and public service delivery

Assessment methods in higher education must adapt to detect AI usage while ensuring students develop actual expertise, not just AI skills

Topics

Online education | Future of work | Digital standards

Key takeaways

70% of skills needed for most jobs will change by 2030, requiring constant adaptation and redefinition of ‘future ready’ skills

Successful scaling requires three key strategies: supporting innovative models, creating locally generated but globally relevant programs, and building capacity of educational systems rather than just direct learner support

Inclusive national employment policies are essential to make digital skills initiatives sustainable and scalable, with youth guarantee schemes in EU/Balkan countries serving as successful examples

International labor standards need updating to include digitalization, digital skills, and lifelong learning components to remain relevant

Public-private partnerships are crucial for impact, with examples like ITU-ILO digital skills campaign and Decent Jobs for Youth initiative partnering with Microsoft

Reaching underserved rural communities without internet access requires resource-intensive, community-based approaches with local partnerships

AI literacy must go beyond technical skills to include human skills, critical thinking, responsible AI use, and communication abilities

Assessment methods in education must adapt to the AI era while ensuring students develop actual expertise alongside AI skills

Course content requires constant updating due to rapid AI innovation, with some platforms rotating 150-250 courses monthly

Significant gender gaps exist in AI education with only 32% of women enrolling in AI courses globally

Multidisciplinary approaches are needed beyond technical skills, including procurement, change management, and public service delivery

Lifelong learning is crucial for all generations, especially those not born into the digital age

Resolutions and action items

Continue partnerships between UN organizations (ITU, ILO) and private sector partners like Microsoft for skills training initiatives

Develop and implement AI literacy frameworks with defined competencies for educational policy

Create modular, personalized training approaches with micro-credentials to adapt to diverse skill development needs

Establish AI academies for teachers through partnerships with teachers’ unions

Launch AI Skills Coalition and Digital Transformation Center initiatives to promote training and reach underserved communities

Follow up with smaller group conversations to take actions forward and solve identified challenges

Expand ITU Academy offerings and explore micro-learning modules for better adaptation and scaling

Integrate digitalization perspectives into national employment strategies and active labor market policies

Update international labor standards to include digital skills and lifelong learning components

Unresolved issues

How to effectively assess students in higher education when AI tools are widely used without compromising evaluation of actual knowledge and expertise

How to address the rapid pace of AI innovation that makes course content obsolete quickly, requiring constant updates

How to bridge the significant gender gap in AI education participation (only 32% women)

How to reach and support NEETs (not in education, employment, or training) who are completely disconnected from systems

How to balance AI tool usage with maintaining fundamental knowledge and soft skills

How to provide evidence-based policy guidance when the implications and impacts of AI technologies are still unclear

How to handle the different aspirations and expectations of digitally native young people in traditional educational and employment systems

How to scale resource-intensive community-based approaches needed to reach underserved rural populations

How to prepare for agentic AI and future technological developments that may transform entire industries

Suggested compromises

‘Dream big and dream small’ approach – building capacity of large systems while designing programs with individual learner experiences in mind

Hands-on assessment methods with higher proportion of grades from practical evaluation to adapt to AI-enhanced learning environments

Modular and micro-credential approaches rather than traditional long-form courses to accommodate rapid technological change

Integration of AI skills training with entrepreneurship development, particularly for unemployed graduates in developing countries

Combination of online and community-based training approaches to reach both connected and underserved populations

Multidisciplinary training that combines technical AI skills with domain expertise, soft skills, and responsible AI use

Experiential learning approaches that focus on practical application rather than just theoretical or technical training

By 2030, we’re anticipating that for all the jobs, for most jobs, by 2030, we’re going to see a 70% reduction in skills. So 70% change in skills that are needed for most jobs by 2030.

Speaker

Anupama Shekhar

Reason

This statistic is striking because it quantifies the massive scale of skills transformation required in just a few years. It moves beyond abstract discussions of ‘future readiness’ to concrete data that illustrates the urgency of the skills development challenge.

Impact

This comment fundamentally reframed the discussion from theoretical future planning to immediate crisis management. It established the urgency that permeated the rest of the conversation and justified the need for innovative, scalable solutions that other panelists then built upon.

We don’t have really evidence about what are the implications, the impact of these technologies yet on our societies… policymakers are not really often in the position to really take the right decision… We see a lot of risk, a lot of opportunities also, but the policymakers are not really often in the position to really take the right decision.

Speaker

Gianluca Musraca

Reason

This comment challenges the assumption that we can effectively plan for AI’s impact when we don’t yet understand its full implications. It introduces a critical tension between the need for immediate action and the lack of comprehensive evidence to guide that action.

Impact

This shifted the conversation from solution-focused to problem-definition focused, introducing a note of caution and complexity. It led to discussions about the need for foresight literacy and evidence-based policymaking, elevating the conversation beyond simple skills training to governance challenges.

Look at what happened with RGPT, we were not prepared for that and now this is already past. The agentic AI phenomenon is already changing completely our organizations… we may actually end in a very dystopian future that we should try to avoid.

Speaker

Gianluca Musraca

Reason

This comment introduces the concept of technological acceleration outpacing human adaptation, using concrete examples (ChatGPT, agentic AI) to illustrate how quickly the landscape changes. The dystopian warning adds urgency and ethical dimensions to the technical discussion.

Impact

This comment introduced a sense of technological vertigo to the discussion, shifting from optimistic planning to acknowledging the possibility of losing control. It prompted deeper reflection on the need for proactive rather than reactive approaches and influenced subsequent discussions about responsible AI development.

It’s not only the people who need to be future ready. It’s also our own international labor standards that need to be future ready… Every single labor standard needs to be checked, whether it is still relevant.

Speaker

Dorothea Schmidt-Klau

Reason

This comment expands the scope of ‘future readiness’ beyond individual skills to institutional and regulatory frameworks. It recognizes that the challenge isn’t just training people differently, but fundamentally restructuring the systems that govern work.

Impact

This broadened the conversation from individual capacity building to systemic transformation. It influenced subsequent discussions about the need for comprehensive policy frameworks and highlighted the institutional dimensions of the digital transformation challenge.

Those that are in the underserved communities, they don’t have access to Internet at home. They don’t have a computer… So if you want to train them, how do you do that? You have to go out and reach them in their communities… This is very resource intensive.

Speaker

Susan Teltscher

Reason

This comment grounds the high-level discussion in practical realities, highlighting the digital divide as a fundamental barrier to scaling AI and digital skills training. It challenges assumptions about universal access to technology-enabled learning.

Impact

This comment brought the discussion back to equity and inclusion concerns, tempering optimistic talk about digital solutions with recognition of infrastructure and access barriers. It influenced the conversation toward more nuanced approaches that consider different contexts and resource requirements.

We have only 32% of women taking those [AI] courses… the real problem is all these young people, the so-called needs that are not in education, not in employment, not in training. They are lost. We don’t even know where they sit.

Speaker

Maria Cristina Cardenas Peralta and Dorothea Schmidt-Klau

Reason

These comments highlight critical gaps in participation and reach, moving beyond aggregate numbers to examine who is being left behind. The concept of ‘lost’ youth who are invisible to systems is particularly powerful.

Impact

These observations shifted attention from general skills development to specific inclusion challenges, prompting discussion about targeted interventions for underrepresented groups and the need for more comprehensive approaches to reach disconnected populations.

Overall assessment

These key comments collectively transformed what could have been a routine discussion about digital skills training into a more complex, urgent, and nuanced conversation about systemic transformation. The 70% skills change statistic established immediate urgency, while Gianluca’s warnings about technological acceleration and lack of evidence introduced necessary caution and complexity. Dorothea’s point about updating labor standards broadened the scope to institutional change, and Susan’s emphasis on underserved communities grounded the discussion in equity concerns. Together, these comments created a multi-layered conversation that acknowledged both the transformative potential and significant challenges of AI-driven skills development, moving the discussion from simple solution-sharing to deeper problem analysis and systemic thinking.

How could digital skills development efforts be scaled up in national labor policies?

Speaker

Tom Wambeke

Explanation

This question seeks to understand how individual interventions and programs can be integrated into broader policy frameworks to achieve sustainable scale and impact.

What is currently being done at the regional or cross-border level to accelerate scale in digital skills development?

Speaker

Tom Wambeke

Explanation

This explores coordination mechanisms and initiatives that transcend national boundaries to amplify impact and share best practices.

How can we find synergies between AI literacy and futures literacy/foresight literacy?

Speaker

Tom Wambeke (based on Gianluca’s input)

Explanation

Given the uncertain and rapidly evolving nature of AI, combining AI literacy with foresight capabilities could better prepare individuals and organizations for future changes.

How can we develop indicators or detection methods to identify where AI has been used in educational assessments?

Speaker

Dr. Sarah-Jane Fox

Explanation

This addresses the challenge of assessing students’ actual knowledge versus their AI-assisted work, which is critical for maintaining educational integrity and ensuring students develop foundational skills.

How are organizations updating AI-related courses given the rapid pace of technological change?

Speaker

Maria Cristina Cardenas Peralta

Explanation

The fast evolution of AI technology requires constant curriculum updates, and understanding best practices for course maintenance is crucial for effective training programs.

How can more women be enrolled in AI and digital skills courses?

Speaker

Maria Cristina Cardenas Peralta

Explanation

With only 32% of women taking AI courses on Coursera, addressing gender disparities in AI education is important for inclusive development.

Does the International Labour Organization offer opportunities to international candidates with VLSI design engineering specialization and AI skills?

Speaker

Nidhi Gopal

Explanation

This specific inquiry about career opportunities reflects broader questions about how international organizations are adapting their hiring to include AI-skilled professionals.

How can we better support unemployed graduates in developing countries through AI-enabled entrepreneurship programs?

Speaker

Unnamed participant

Explanation

With 15-20% of graduates unemployed in developing countries, there’s potential to leverage AI training for startup creation aligned with Sustainable Development Goals.

How do we equip teachers and educational systems to handle a generation that may have more digital knowledge than their instructors?

Speaker

Dorothea Schmidt-Klau

Explanation

This addresses the challenge of role reversal in digital knowledge between students and teachers, requiring new pedagogical approaches and teacher training methods.

What evidence do we need to gather about AI’s impact on society to inform better policymaking?

Speaker

Gianluca Musraca

Explanation

Policymakers need concrete evidence about AI’s implications to make informed decisions, but this evidence is currently lacking due to the technology’s rapid evolution.

How can we develop personalized, modular training approaches using AI to adapt to different learning needs?

Speaker

Gianluca Musraca

Explanation

Moving away from one-size-fits-all training toward personalized, micro-credential approaches could improve the effectiveness and relevance of skills development programs.