White Paper: Impacts of generative AI on learning

Impacts of Generative AI on Learning & Development Opportunities, risks and skills to be developed

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As a European and international leader in Learning and Development, the Cegos Group is actively involved in the world of work and business and constantly monitoring new market developments. Our mission goes beyond the purely economic framework: we train individuals and support organisations to meet their development challenges in a constantly changing world.

In view of the major transformations facing our societies, the commitment we make to our customers is now more relevant than ever: we promise to turn skills into performance.

With this in mind, our teams provide advice and support to help companies implement their transformation and growth strategies, driving the personal and professional development of individuals worldwide. Consequently, all the solutions we provide (off-the-shelf training, tailor-made solutions, blended and digital learning, outsourcing and learning services, etc.) are designed to deliver a unique learning experience and to turn skills into performance.

With almost 100 years’ experience as a “global Learning & Development partner”, we remain at the cutting edge of innovation, in order to support changes in society, the economy, and jobs; we help our clients embrace innovations and technological breakthroughs by reconciling ethics and performance; we move forward to engage learners in a journey full of meaning and efficiency.

As part of the digital transformation that we initiated with our customers almost 25 years ago, we have been testing and exploring the use of different AI applications in our activities over the last few years. As such, in 2021, we published an initial white paper on "AI & Learning".

More recently, the emergence and increasingly widespread use of generative AI (GenAI) has obviously led us to further analyse the opportunities, risks and questions raised by this new development.

As always, our teams decided to step back and take a critical, positive and practical approach to analysing this issue. Today, I am therefore very happy to share our latest findings and ideas with you.

I hope you will find the key points addressed in this white paper useful and that this will help us move forward together in our important mission, which aims to support individual, collective and societal transformations through skills development.

Benoit Felix, CEO of the Cegos Group

Impacts of Generative AI on Learning & Development

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Introduction A very brief history and definition of AI... and generative AI The study of artificial intelligence (AI) began in the 1950s. At the time, the first researchers suggested that every facet of learning or intelligence could, in theory, be meticulously described to enable the creation of machines that could then simulate these capabilities.

Today, the scope of AI covers a wide range of techniques, such as machine learning and knowledge-based approaches, as well as various areas of application (computer vision, natural language processing, speech recognition, intelligent robotic systems, etc.).

AI thus represented a vast field of study including systems and algorithms designed to mimic human intelligence.

Gradually, as AI becomes more sophisticated and diversified, new techniques and applications are emerging. It is therefore becoming increasingly difficult to provide a strict definition of AI, especially when attempting to draw the illusory line between AI and non-AI.

The AI landscape then evolved, reaching a milestone in the 2010s, with the emergence of generative AI (GenAI), which led to rapid advances in large language models (LLMs) like OpenAI's Generative Pre-trained Transformer (GPT) series, leading to the release of GPT-3 in June 2020.

After lengthy discussions, the OECD countries still managed to reach a consensus on the definition of "AI" and, more specifically, what is known as an "AI system".

01 1950s - 1960s The term "artificial intelligence" is coined, and early AI research focuses on symbolic reasoning and problem-solving.

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02 03 1980s - 1990s Expert systems and rule-based AI become prominent.

Late 1990s-early 2000s Machine learning techniques, like neural networks, experience a resurgence, leading to breakthroughs in AI.

The OECD definition “An AI system is an automated system that, for a given set of human-defined goals, can make predictions, recommendations, or decisions that affect real or virtual environments. (...) AI systems are designed to operate at varying levels of autonomy.”

This definition of an AI system emphasises its dynamic nature while acknowledging the role of human input in its development, as well as the system's ability to adapt and evolve during its deployment, especially in machine learningbased systems.

The outputs of AI systems are generally classified into three categories, depending on the degree of direct AI interference on the environment and the degree of human action: predictions, recommendations, and decisions.

Previously, conventional AI systems were very good at performing tasks such as image classification or speech recognition, but they lacked the ability to create new content.

What sets GenAI apart is its ability to understand and mimic human creativity, to generate realistic images, write stories, compose music, or even create compelling deep-fake videos. It then uses learned models to generate new outputs, rather than replicating information or simply analysing or categorising existing data.

Content-generation systems have grown in popularity and are therefore now recognised as a separate output category. Indeed, although text generation could be perceived as a serie of decisions or predictions, its significance justifies it being considered as a separate output category in AI.

06 05 04 2000s Generative models emerge, allowing machines to generate data.

Late 2010s-early 2020s Mid 2010s Early versions of LLMs like GPT (Generative Pretrained Transformer) are introduced.

The development of much larger LLMs, including GPT-3, with billions of parameters, revolutionises natural language understanding and generation.

07 2020s Models like ChatGPT, based on the GPT-3 architecture, become popular for chatbot and conversational AI applications.

They leverage the advancements in LLMs to generate human-like text in real-time conversations.

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The major impact of GenAI on skills development and training One of the OECD's Core Principles for the Promoting Innovative and Reliable Use of AI aims to "build human capacity and prepare for labour market transformation" (Principle 2.4). It serves as a call to action, in order to: - Equip individuals with the essential skills needed to effectively use AI solutions.

- Implement long-term training programmes to ensure a fair transition among all employees through the integration of AI. - Advocate for the responsible use of AI in the workplace to encourage entrepreneurship and improve productivity.

These recommendations are aimed first and foremost at governments, which have a responsibility to “leave no one behind.” However, it is important to include all stakeholders in the reflection process to ensure a fair and sustainable transformation. Learning & Development (L&D) is of course part of this process and has an essential role to play in this major development.

As pointed out by Fosway1, the transformative impact of AI on L&D is already clear, driving significant changes in both design, deployment, and the training experience delivered.

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Players involved in the field of education and training therefore resolutely need to adapt by integrating AI into their offers and practices to remain competitive and efficient. This adaptation requires a concerted effort in order to foster trust and take responsibility for the ethical use of AI technologies. For their part, organisations are already experiencing the early impacts of adopting AI and GenAI; these effects may be felt by the organisation itself or by their teams in many professions – such as Human Resources – and in many areas, such as skills development.

In the specific field of skills development, the challenge is to take advantage of GenAI to optimise the impact of training, in often tight budgetary situations, while respecting an ethical framework that is essential for the individual as well as for the organisation.

With this in mind, in this white paper, we decided to explore the transformative impact of GenAI on training and skills development. We look into the opportunities it presents across the entire learning value chain, from personalised learning to content creation and the development of new tools or pedagogical practices.

Through critical analysis, practical ideas and examples, we aim to clarify and share how this technology can improve the learning experience and its impact.

Fosway Group (2024), Fosway 9- Grid Digital Learning, March 2024 - https://www.fosway.com/9-grid-2/ © CEGOS 2024

Techniques used in generative AI Machine learning (ML)

A type of AI that uses data to automatically improve its performance.

Artificial Neural Network (ANN)

A type of ML that is inspired by the structure and functioning of the human brain (e.g. the synaptic connections between neurons).

Text generative AI

Image generative AI

General-purpose transformers

A type of ANN that is capable of focusing on different parts of data to determine how they relate to each other

Large language models (LLM)

A type of general-purpose transformer that is trained on vast amounts of text data.

Generative pre-trained transformer (GPT)

A type of LLM that is pre-trained on even larger amounts of data, which allows the model to capture the nuances of language and generate coherent context-aware text.

Generative adversarial networks (GANs) Variationnal autoencoders (VAEs)

Type of neuronal network used for image generation.

Source: UNESCO (2023), Guide for Generative AI in Education and Research

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Leveraging GenAI throughout the skills management value chain

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Using GenAI to adapt and boost the human capital in every organisation Skills management is a strategic issue for organisations, which must constantly adapt the profiles and needs of their employees to market and technological developments.

GenAI offers unprecedented opportunities to facilitate and improve this process, by making it possible to collect, analyse and exploit data concerning employees' skills. This new input can help organisations develop highly qualified and versatile teams.

Over the last few years, AI has already been used to improve the personalisation of training paths and provide real-time feedback to learners.

Thanks to the rapid development of GenAI, the designers of these training paths now have more tools at their disposal, which will ultimately benefit learners.

Exploiting GenAI throughout the skillsmanagement value chain also opens up new avenues for strategic planning of jobs and skills: - In particular, GenAI can analyse job descriptions and performance data to identify specific skills needed within the organisation.

- It can also organise, propose, or generate tailored training content, in line with the profiles and preferences of each learner, to ensure that the training is relevant, engaging and effective.

- Finally, it can be used to develop predictive models that identify emerging aptitudes and competencies required in the future, allowing organisations to proactively adjust their training programmes and stay one step ahead of trends in the industry.

In short, GenAI is a powerful tool for optimising skills management within companies and reducing the risks of skills shortages, obsolescence or overqualification, by providing innovative, efficient, and personalised solutions at every stage of the value chain.

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A few examples of the uses of GenAI in the skills-development value chain - The first example concerns strategic jobsand-skills planning. GenAI can anticipate emerging and strategic skills, establish a plan concerning the skills and jobs needed to achieve the organisation's goals, taking internal and external constraints and opportunities into account, and make the appropriate recommendations.

- AI can also be used to create dynamic and personalised skills maps, allowing you to visualise the strengths and weaknesses of each employee, each team, and each department.

- GenAI can then simulate different scenarios and assess their impact on employee performance, productivity, satisfaction, and commitment.

- This automated data analysis greatly optimises the process for identifying and measuring skills gaps in real-time, so that personalised learning and assessment solutions can be provided as part of the workflow, generated by the AI system.

- This enables the AI system to generate a variety of content, methods, and training aids, such as videos, podcasts, games, quizzes, case studies, and more. It can also provide individual follow-up and support, giving feedback, encouragement, reminders, advice, etc.

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As a result, employees can become more involved in the training process and take responsibility for their own career path.

AI therefore opens up new perspectives for adaptive learning and the generation of multimedia training resources. It has enormous potential to personalise skillsdevelopment pathways and recommend training solutions at the right time, in line with each individual’s performance and goals and the organisation's strategic priorities.

At the end of the value chain, artificial intelligence can also be used to optimise the allocation of human resources, by proposing missions, mobility, recruitment or outsourcing solutions, according to the skills that are available and required for certain missions, projects or stretch roles.

According to the 2023 Cegos international "Transformations, Skills and Learning" barometer

63% of international HR managers plan to use AI to meet the challenge of personalised training.

However, only

11 %

of them have already used AI, particularly in companies with 500 to 1,999 employees.

This low take-up rate is primarily due to the absence of a real “data culture” and the fact that HR and training data is not exploited throughout the skills-management value chain.

In addition, the implementation of adaptive learning today focuses solely on the asynchronous digital offer, which is less appreciated by employees and only represents a part of the value chain and development solutions available.

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GenAI, helping to improve the design of training programmes

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New opportunities for L&D professionals The integration of AI is reshaping the role of L&D professionals, who can develop more personalised, efficient, and innovative training methodologies.

5 key opportunities to increase the added value of training solutions: - Efficiency in content creation: GenAIbased tools can automate certain aspects of content generation, such as the creation of training materials, quizzes, and interactive scenarios. This automation allows training and skills development professionals to focus on more strategic aspects of programme design and delivery.

- Enhanced personalisation: GenAI helps create learning experiences tailored to each participant's needs, preferences, and learning types. This personalisation improves the learners’ engagement, retention, and results.

- Data-driven insights: the use of GenAI in training-data analysis provides L&D professionals with deeper insights into learners' progress, engagement, and areas for improvement. This data-driven approach supports the development of more effective training strategies.

- Scalability of training programmes: GenAI technologies make it easier to create and deliver scalable training content, making it easier to reach a wider audience without compromising the quality of the learning experience.

- Innovative learning experiences: thanks to GenAI, more engaging and interactive training is possible (simulations, gamification, etc.), thus enhancing the attractiveness and effectiveness of training solutions.

5 major changes for L&D professionals: 1. Improving technology skills: this is a requirement in order to be able to effectively design and implement AIaugmented training solutions.

2. Changing roles: new skills are needed, and new expert profiles are emerging, such as data analysts, technology integrators, and training experience designers.

3. Increased collaboration: greater interaction between L&D professionals, AI experts, and IT departments to ensure the seamless integration and deployment of AI-based training solutions.

4. Ethical considerations: issues related to data privacy and the biases that can appear in AI-generated content mean L&D professionals must remain constantly vigilant.

5. Continuous learning and adaptation: since GenAI is developing and evolving very quickly, L&D professionals must constantly adapt their skills (new tools and techniques, knowledge of best practices, etc.).

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Using GenAI to improve every phase of training-content design Training must be easy to access, attractive and adapted to the needs of the learner. GenAI is proving to be useful for optimising the training courses’ design, with tangible applications, such as: - Establishing or fine-tuning learning objectives.

- Structuring content and creating engaging storytelling.

- Generating discussion topics that encourage critical thinking.

- Creating quizzes and interactive elements or contributing to their production.

- Finding the best examples and use cases to better illustrate the content.

- Proposing training plans based on learning objectives.

In particular, learning Designers will be able to use GenAI to improve productivity, creativity and efficiency in the production of their training content: - Video production: today, many tools allow you to automatically generate videos with avatars. This saves a significant amount of time and money.

While the results are not yet optimal (the final rendering sometimes lacks authenticity), rapid advances in AI will solve these problems and lead to more impressive results.

- Generation of specific images based on the user's prompts ("young learners discussing around a tablet in an industrial environment"): for example, DALL-E is a popular tool that produces surprisingly realistic results, competing directly with photo stock searches.

- Translation: many AI-powered tools provide translations, whose accuracy is constantly improving. This greatly speeds up the translation process, with AI detecting speech and voice patterns in a relatively authentic way. The Learning Designer can therefore focus on his/her added value: a critical review of the content generated.

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Some examples of AI-powered learning design tools and platforms AI is increasingly being integrated into everyday professional tools (such as Microsoft Copilot) in a cross-functional way. At the same time, specific tools and platforms, which are useful for designing training courses, are being developed: Tools for creating and designing educational programmes (such as Articulate 360 and Adobe Captivate) include a comprehensive suite of solutions that make it easy to create interactive, mobile-friendly e-learning modules. In this case, AI will suggest content, accessibility features, provide voice-activated navigation and captions, propose adaptive learning paths, improve the design process and learner engagement, and more. Its AI-powered features streamline content development and make it accessible to a wider audience.

Video creation platforms, such as Synthesia, allow users to create videos from texts using virtual avatars, in several languages, thus enhancing the training experience with personalised video content. Other tools also analyse text requests to automatically suggest relevant images, video clips, and music, making it much easier to produce impactful videos.

Learning Management Systems (LMS) use AI to organise and structure personalised content, automate administrative tasks, and improve key learner-monitoring and engagement metrics. Other platforms even enable you to build personalised training courses, with content recommendations. Skills assessment systems help L&D professionals optimise the management of their organisations' skills requirements, in terms of upskilling or reskilling. Predictive analytics also promote a more engaging and impactful learning environment.

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GenAI, a new assistant designer for trainers GenAI opens up new opportunities for trainers. They can use these tools to enhance and diversify their range of training techniques, stimulate creativity, critical thinking, and engagement in their learners... and save time and efficiency when designing and assessing training courses.

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Using GenAI to increase productivity in the training development cycle At the beginning of a design process, pedagogical designers may experience "writer's block." GenAI can help them initiate each step of their design process.

1

Define the Criteria

2

Select and Organise the Content

3

Define Training Objectives

4

Scenario Development

5

Create Supports

- Define the criteria and indicators for assessing the training course, in line with the training objectives and the needs of the learners.

- Design appropriate assessment tools, such as questionnaires, tests, case studies, portfolios, etc.

- Analyse results using statistical, qualitative, or mixed methods from different sources.

- Draw conclusions and recommendations from the assessment, identifying strengths and areas for improvement.

In short, the pedagogical designer can save valuable time and increase the efficiency of the training development cycle by delegating some repetitive, time-consuming, or tedious tasks to GenAI, such as writing, translating, proofreading, formatting, searching, summarising, indexing, distributing, and assessing content.

Source: Cegos Group

The first step, "Define the criteria", corresponds to the request that the pedagogical designer (trainer, instructional designer or consultant) makes to the GenAI. To get relevant answers in the next steps, the trainer must first specify the target audience, topic, duration, and method of training, and give the AI the role of an experienced instructional designer.

As an assistant designer, GenAI will then be able to structure the content to be covered (step 2), define the learning objectives (step 3), propose ideas for exercises and interaction with the participants (step 4), generate visual aids to illustrate the content and formalise all the materials, from summary sheets to slideshows and tutorials (step 5).

In this way, working with GenAI, the pedagogical designer does not have to start with a blank sheet of paper and can save time at each stage of the design process.

GenAI can also help produce assessments and measure the results of training, which is another important role of the pedagogical designers. AI can help them to: Impacts of Generative AI on Learning & Development

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Using GenAI to expand the range of training techniques GenAI also opens up new opportunities for pedagogical designers in their training approaches, thanks to new ways of interacting with learners, both face-to-face and remotely. GenAI can: - Produce a real-time collective learning summary, compiling participants' contributions.

- Work in parallel on the same exercises as the participants to compare the results and orchestrate a sort of "human-machine" feedback process, while developing critical thinking at the same time.

- Provide access to a large amount of data and information on a particular topic during the training.

- Boost learners' creativity and engagement by challenging them to create content on their own using GenAI. - Imagine new simulation experiences using avatars capable of taking on the role of a customer, an employee, a negotiator, etc., and propose a larger-than-life dialogue to practice conducting interviews and getting feedback in real time.

- Generate discussion topics that encourage the group to debate and exchange ideas.

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Providing pedagogical designers with support to adapt to the changes linked to the use of GenAI Since they are the only ones who can interact with several forms of intelligence, whether situational, emotional, or technical, pedagogical designers will remain at the heart of training systems and will always play a key role.

However, they will no longer be the sole owners and producers of content that is used for training. They need to take on the role of facilitator-trainers, providing tools and resources to encourage social learning, co-creation of knowledge, high-level reflection, interaction, and human values.

Trainers should also consider the potential of GenAI (and more broadly digital technology) as an opportunity to enhance their training practices rather than as a potential threat.

Of course, this means questioning oneself and requires constant learning to become familiar with these new tools and use them in a relevant way, to serve a genuine pedagogical objective. Finally, the time saved by using GenAI will allow trainers to focus on higher valueadded activities such as pedagogical innovation and learning support.

As a result, a new key skill is becoming essential for all pedagogical designers: intellectual curiosity and constant monitoring of the latest developments in training technologies to improve their practices.

Technology is progressing so quickly that it is often difficult to keep up. With more than 1,000 AI tools created every day, defining a structured and targeted monitoring strategy, taking the time and equipping oneself with the tools to do so, is no longer an option!

Naturally, to reap the benefits of GenAI and avoid its pitfalls, pedagogical designers must be supported through this change. They need to be trained in how to use and assess GenAI tools, made aware of legal and ethical issues, assisted in adapting their training methods and skills, and reassured about their added value and their future.

Below is a roadmap for training managers, who are on the front line in terms of supporting trainers through this new stage: 1- Define the vision and objectives of the change and create a positive dynamic to help trainers boldly reinvent themselves, drawing on their successes and the value generated.

2- Lead by example by adopting new tools that trainers will need to use themselves.

3- Measure the impact on trainers' working methods and activities: define use cases in all the processes and facilitate their implementation by making generic prompts available.

4- Build momentum through training, workshops, webinars, tutorials, communities of practice, mentoring and coaching for trainers.

5- Mitigate biases and risks by creating a formal code of conduct to govern the uses of AI (data confidentiality, cognitive biases, legal requirements).

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GenAI, a (r)evolution for learners In today’s volatile, complex, and uncertain world, organisations face a daunting challenge: just-in-time training. This requires greater commitment and self-determination in the training process.

In fact, there are very few "serial learners" in organisations.

The challenge is therefore to make everyone aware of the need for continuous learning. This means creating the conditions to encourage learning and training.

First, ensuring access to useful "on demand" content is crucial and GenAI is a powerful tool for achieving this.

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GenAI: new opportunities for learning and training GenAI opens new opportunities for anyone who needs to adapt to an ever-changing work environment. Here is what it can do for the learner: - Improve the personalisation and differentiation of the learning path, with content adapted to the needs, preferences, and level of each individual.

- Diversify learning sources and formats so that each individual can choose the content that works best for them.

- Facilitate access to and the dissemination of information for training anytime, anywhere.

- Broaden your spectrum to acquire new skills, notably cross-disciplinary skills.

- Promote the value of experience and expertise by contributing to the creation and improvement of content.

- Optimise the retention and transferability of knowledge in your workflow, thanks to micro-learning content that helps people remember, understand and apply concepts.

- Be assisted in real-time in your workflow by using a chatbot to answer questions or get feedback.

However, all these opportunities must be seized while considering the risks associated with the learner's own autonomous use of GenAI: - A loss of trust or responsibility due to content that may be misleading, biased, or manipulated.

- A lack of critical thinking and analytical skills in the face of content that may be simplistic, superficial, or erroneous.

- A loss of creativity and originality in the face of content that may be redundant, predictable, or plagiarised.

The emergence of GenAI is therefore changing the position of those who are being trained, inviting them to become more active and autonomous in their learning. This means they must be more flexible, curious, and discerning.

The trainer therefore plays an even more crucial role in encouraging, facilitating, and regulating these new ways of learning.

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Towards increasingly self-determined learning The training process can no longer be based solely on prescribed courses, pushed by the organisation – often with long implementation times – but must also be "driven" by the individual, who thus acquires greater autonomy in his or her development process. Consequently, we are moving towards an increasingly self-determined way of learning.

Heutagogy (from the Greek "Heutos" meaning "Self" and "Agogus" meaning "to guide" – "to guide oneself") was defined by Hase and Kenyon in 2001 as the study of self-determined learning.

This study took place in the context of the democratisation of the Internet, and its relevance seems even more important today with the advent of GenAI. Heutagogy is based on the principle that learning is an active and proactive process, that the learner is "the main agent of his own learning", which results from his or her personal experience.

In a heutagogical approach, the trainer is therefore a facilitator who provides advice and resources but gives full ownership of the learning path and process to the learner, who negotiates the learning and determines exactly what he or she will learn and how.

If we consider pedagogy as a level-1 learning style (for children), andragogy as a level-2 learning style (for adults), then we could position heutagogy as a level-3 learning style.

The higher the level, the greater the maturity and autonomy required from the learner and the less need for control by the instructor and course structuring. (+) Maturity and autonomy required of learners

(-) Level 3 : Eutagogy

Trainer control and structured courses required

Level 2 : Andragogy

Level 1 : Pedagogy (-)

(+)

Source: S. Hase, C. Kenyon. Moving from andragogy to heutagogy. researchgate.net. January 2001

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The other key concept of heutagogy concerns double-loop learning and self-reflection (Argyris & Schön, 1996, cited in Hase & Kenyon, 2001).

In double-loop learning, learners examine the problem as well as the action and its results while considering the problem-solving process and how it affects their prior knowledge. This thought process then facilitates problem-solving and learning in unknown and changing contexts.

DOUBLE-LOOP LEARNING AND SELF-REFLECTION Preexisting knowledge and actions

Problem

Action

Double Loop Single Loop

Results Reflective practice helps learners to become lifelong learners and develops their critical thinking skills.

Source: Argyris & Schön, 1996, cited in Hase & Kenyon, 2001

To put this process into practice, we recommend, providing reflective-learning notebooks (produced with the help of GenAI!) to document the learning pathways and enable learners to reflect on the content and discussions of the training course while exploring new ideas.

Continuous, personalised assessment and a feedback culture also contribute to developing these reflective practices.

To summarise, GenAI is a promising technology for adult learning since it opens new possibilities for creating and using original and adapted content.

However, it also comes with risks and limitations, which require vigilance and regulation. Adult learners therefore need to be aware of the impacts, opportunities and changes that GenAI entails for their learning, and develop the skills needed to get the most out of it.

The current limitations... or what GenAI is not yet able to do: Empathy and emotional understanding: AI systems can analyse emotional data, but empathic understanding and managing human emotions remain complex challenges.

Consequently, GenAI is still not able to recognise, respond to, and adapt to the learner's personal, emotional, and social needs.

Creativity and advanced critical thinking: although AI systems can generate creative content, the ability to develop highly complex and innovative critical thinking remains a human prerogative. Equally, GenAI cannot acquire specific knowledge without a suitable prompt (in other words, how can I ask what I do not know?)

A review of current state of GenAI and its interactions with learners While live feedback is not new in digital learning, the latest developments in AI show that it can now be used to coach... although still in a rudimentary way.

For example, tools such as Orai can analyse expressions, tone of voice, and language to provide feedback on a learner's presentation skills. Other tools can produce role-playing scenarios where the user interacts with a virtual avatar, programmed to ask the right questions and respond to whatever the user says, as well as the way they say it.

AI is unlikely to replace coaches and trainers anytime soon, but these tools give learners the ability to put general skills into practice and get personalised feedback, exactly when they need it and without taking up someone else's time.

Deep experiential learning: humans often learn through deep personal experiences.

AI can simulate scenarios, but the true understanding of direct experience remains a human skill.

Adapting to complex social dynamics: the nuances of social interactions, including gestures, tones of voice, and complex cultural contexts, represent a challenge for AI. As such,GenAI is not yet able to provide personalised advice that takes a person's particular circumstances into account.

Ethics and personal values: balancing ethical decisions and personal values is an inherently human feature, which can be difficult to fully replicate through artificial intelligences, so GenAI cannot make judgments that require moral or ethical reasoning.

Performing physical actions in the real world: certain types of behaviour or actions require training through examples and interaction with real objects. GenAI systems can assist trainers, but they still need trainers to observe their behaviour.

These limitations will gradually be removed as AI becomes more sophisticated. The challenge, therefore, is to find a balance between the effectiveness of AI and the unique value that human skills bring to the learning experience. With this in mind, there are ongoing discussions about setting legal limits, to prevent AI from becoming so powerful that it would be difficult to control.

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The ethical imperative: providing a framework to ensure effective management AI's ability to "simulate" and reproduce human intelligence raises questions about the place of humans in a world that is increasingly being shaped by technology.

While AI opens up tremendous opportunities, its use must be strictly regulated and defined. This ethical issue is an imperative for all organisations, with a particular focus on the most likely and real risks in the short term:

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The risks associated with GenAI in training Will GenAI replace trainers or learning designers?

What guarantees can be put in place to protect privacy if AI is used to collect and analyse massive amounts of personal data? How can we fight against bias and algorithmic discrimination, which could perpetuate inequalities in training? Which decisions could we accept to delegate to AI?

shape societal norms, and have a profound impact on the lives of individuals (in terms of rights, freedoms, well-being). In particular, the deployment of AI in sensitive areas such as health, education, training, justice, or finance raises ethical dilemmas in terms of accountability, transparency and consent. If appropriate, shared ethical guidelines and regulations are not put in place, there is a great risk that Ais will be deployed in a detrimental, discriminatory, or unethical manner.

Like any technological leap, the rapid development of GenAI solutions represents risks that should not be overlooked: they could potentially influence decision-making processes,

Some of the risks identified include:

Impact (positive/negative)

- Personal data, privacy, business confidentiality. Large Language Models (LLMs) are very large deep learning models that are pre-trained on large amounts of data; they rely on existing human-produced content to generate new, original documents. This raises the question of the intellectual property of the original content creators, who (quite rightly) fear that their work will be reused for free and without their copyright being acknowledged.

Since the training data used to "muscle up" the AI is not always publicly available, a person who exploits AI-generated content, without being aware of any copyright issues, cannot be exonerated from those rights on the grounds that the content was suggested to them by an AI and that he/she did not know the source; good faith is not legally acceptable in this case.

In the United States, several class actions or proceedings brought by rights holders (such as Getty or the New York Times) have been

Runaway AI and extinction

Democratic demise

launched against companies developing GenAI solutions. Similarly, LLMs sometimes capture sources of information that are unreliable or even false: when using AI to generate content, fact-checking and data is imperative.

- The risk of diluting accountability in the event of decisions are delegated to AI systems; to mitigate this risk, the notion of explainability is essential. This is the ability to relate and make understandable the elements considered by the AI system to produce a result.

- The major issue of AI-induced biases: these come either from the data use to train the AI models (which will then perpetuate or even amplify these biases), or from algorithms (they can produce biases that are independent of the training data, leading to unfair or even discriminatory decisions and reduced cultural diversity).

AI solving scientific challenges

Work conditions and jobs loss

Economic growth

Bias and discrimination Environmental impacts of AI

Likelihood Source: Sætra, H.S., Danaher, J. Solving the battle of short-term and long-term AI risks.

Ethics of AI (2023). https://doi.org/10.1007/s43681-023-00336-y

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The ethical imperative: implementing safeguards to foster trust, promote fairness and protect the interests of the organisation as a whole To foster fairness, transparency and accountability and thus foster a culture of ethical behaviour in the development and use of AI, responsible governance is required as of today.

For example, to mitigate biases and prejudices in AI systems and manage these technologies responsibly, effective strategies can be put in place: - Continuous assessment: periodic ethical reviews of AI used in training are crucial to identify and correct biases.

- Diversity of data: it must be representative and diverse to avoid biases related to gender or ethnic origin, for example.

- Ethical design: integrating ethical principles into the design of algorithms and processes actively prevents bias.

- Raising awareness among users and professionals and providing them with training concerning the critical thinking and ethical dimension of AI, including the risks related to bias and privacy.

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Ensuring the responsible use of AI: an overview of existing frameworks and guidelines around the world Over the past few years, especially since the pandemic, several organisations in different geographic regions of the world have sought to create a framework for the development and use of AI: OECD Principles on AI (2019) This OECD recommendation is the first cross-jurisdictional standard on AI. Based on this, at the Osaka Summit in June 2019, the leaders of the world's twenty largest powers endorsed the G20 AI Principles. Key principles and associated recommendations were defined, focussing on how governments and organisations can shape a human-centred approach to AI: Inclusive Growth, Sustainable Development and Well-being; Human-centred and equity-centred values; Transparency and explainability; Robustness, security, and safety; Responsibility.

The OECD Framework for the Classification of AI Systems (2022) Directly correlated to the OECD AI Principles, this Framework is a fundamental resource: it promotes a shared and structured understanding of the characteristics of different AI systems and thus facilitates the comparison, assessment, and management of associated risks.

AI systems and applications are categorised here according to several key dimensions:

The Bletchley Declaration Signed by 30 countries following the AI Security Summit in the UK (November 2023) The Blueprint for an AI Bill of Rights (2022) Presented by the U.S. White House Office of Science and Technology Policy (OSTP), it provides practical guidance for the design, use, and deployment of automated systems based on five key principles: security, fairness, transparency, privacy, and accountability.

President Biden's Executive Order on Safe, Secure, and Trustworthy Development and Use (2023) It directs federal agencies to set new security standards for AI systems and requires developers to share their security test results and other critical information with the U.S. government.

The creation of ISO/IEC 42001 The world's first AI management system standard, ISO/IEC 42001 specifies the requirements "for establishing, implementing, maintaining and continuously improving an artificial intelligence management system (AIMS) within an organisation. It is designed to enable organisations offering or using products or services that require AI to ensure the development and use of AI systems in a responsible manner. ”

- People & Planet (impact of AI systems on people and the environment).

- Economic context (assessment of the economic implications of AI deployment).

- Data and input, data quality, privacy, and security.

- AI models (technical aspects of algorithms and AI models).

- Specific tasks performed by AIs and their results.

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The AI Act, an initial European strategic response In March 2024, the European Parliament passed the AI Act, which is the world's first comprehensive continental regulation on AI. It aims to protect fundamental rights, democracy, the rule of law and environmental sustainability from the risks of AI, while positioning Europe as a leader in this field.

- Certain applications are prohibited (e.g. socialrating systems or emotion recognition in the workplace), certain exemptions are specified for law enforcement forces, so-called high-risk AI systems are subject to more obligations (risk assessment and mitigation, keeping records of use, ensuring transparency and accuracy, human oversight), and artificial or manipulated content (deep fakes) must be clearly presented as such. The requirement for transparency is also specified, in particular on copyright and AI training data. The AI Act also promotes innovation: real-world testing will be set up at national level to help SMEs and startups develop and train innovative AI technologies before they are brought to market.

- More specifically, regarding education and training, the law recognises that the use of AI

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can provide key competitive advantages and states that "the deployment of AI systems in education is important to promote high-quality digital education and training and to enable all learners and teachers to acquire and share the necessary digital skills". However, it also points out that AI systems used in education or training can have a significant impact on the individual (bias, personal data, etc.) and are therefore considered high-risk. This high-risk classification involves the implementation of a quality management system documented by the organisations themselves.

Also in March 2024, the United Nations General Assembly adopted a resolution that highlights the imperative to respect, protect and promote human rights, online and offline, in all facets of AI. It also recognises Ai’s potential to accelerate the achievement of the UN's 17 Sustainable Development Goals. The resolution also calls for collaborative efforts between states, private entities, civil society, research institutes and the media to design regulatory mechanisms, reduce digital disparities between nations, and ensure universal access to AI technologies.

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A few ideas for mitigating risks and promoting ethical AI practices within the context of learning and training UNESCO's "Recommendation on the Ethics of Artificial Intelligence", adopted by the 193 Member States in November 2021, focuses on the broader ethical implications of AI systems in UNESCO's core areas: education, science, culture, communication and information.

The Recommendation aims to establish a globally accepted standard-setting instrument, based on the articulation of values/principles and their practical implementation. The principles set out are: proportionality and duty to do no harm, fairness and non-discrimination, sustainability, privacy and data protection, human oversight and determination, transparency and explainability, responsibility and accountability, awareness and literacy, governance and multi-stakeholder and adaptive collaboration.

These principles are reflected in the following UNESCO recommendations to Member States in the field of education: - AI literacy education: member States should collaborate with international organisations and educational institutions to provide comprehensive AI literacy education globally.

This initiative aims to empower individuals and address digital disparities resulting from the widespread adoption of AI. - Prerequisite skills: promote the acquisition of foundational skills that are essential for educating people concerning AI, including basic literacy, numeracy, coding, digital skills, media literacy, critical thinking, and AI ethics skills, especially in regions where education gaps are significant.

- Outreach programmes: develop general awareness-raising programmes on AI developments, data literacy, and the impact of AI systems on human rights, including children's rights, accessible to technical and non-technical groups.

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- Research initiatives: encourage research on the responsible and ethical use of AI in education, teacher training, and online learning. Assess the quality and impact of AI technologies in education and ensure that they empower students and teachers while respecting privacy and ethical standards.

- Inclusive participation: promote the participation and leadership of under-represented groups, – including girls, women, people from diverse ethnic backgrounds, persons with disabilities, and marginalised persons – in AI education programmes and share best practices globally.

- AI Ethics Programmes: develop AI ethics programmes for all levels of education, incorporating technical skills and the humanistic, ethical, and social aspects of AI education. Ensure the accessibility of resources, including indigenous languages, and take the diversity of learning environments and needs into account.

- Support for AI research: invest in AI research, in particular research on the AI ethics, and encourage collaboration between the public and private sectors to develop and promote ethical AI practices. Train AI researchers in research ethics and demand that ethical considerations are taken into account in their work.

- Access to data for research: encourage privatesector companies to facilitate access to data for AI research, particularly in low- and middleincome countries, while respecting privacy and data protection standards.

- Interdisciplinary research: promote interdisciplinary research on AI beyond STEM fields, incorporating disciplines such as cultural studies, ethics, law, and psychology. Ensure rigorous and independent scientific research to critically assess AI developments.

The Cegos Group's AI Code of Conduct A leader in Learning & Development, the Cegos Group has always been fully committed to the ethical application of technology in personal and organizational development. The same applies to the use of AI systems.

Several years ago, the Cegos Group started testing and exploring different applications of AI in its business, and organizing workgroups to reflect on AI’s impact on the way we work and deliver our services to our clients.

Our Cegos Group’s AI Code of Conduct serves as a foundational guideline to ensure ethical, transparent, and responsible use of AI technologies within our organization, training offer, solutions and services. It applies to all Cegos Group entities around the world.

We outline the Cegos Group commitment to fairness, accountability, privacy, and the safety of any use of AI solutions, emphasizing the protection of our stakeholders’ data and ensuring that AI-driven solutions do not perpetuate biases or inequalities.

With this code of conduct, we provide a clear framework for decision-making and behavior for employees, ensuring that all AI-powered initiatives are aligned with the organization’s values and are used to augment and enhance the learning experience in a manner that is both innovative and ethically sound.

Our AI Code of Conduct is available on our corporate website cegos.com/en

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Developing the skills needed to get the most out of GenAI in every way The question of how AI – and in particular GenAI – will impact the essential skills needed (both today and in the future) has become a crucial issue for organisations.

In its publication "Employment Outlook 2023: Artificial intelligence and jobs", the OECD indicates that AI not only has an impact on the level of employment and the quality of jobs, but also on the organisational structure of work and the nature of the tasks performed, thus changing the skills required.

This change in skill requirements stems from two key factors: firstly, the ability of AI to mimic a wide range of cognitive and manual skills, and secondly, the increased demand for skills related to the development and use of AI.

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GenAI and its impact on skills In view of the many debates that are currently taking place on this subject, we have been able to identify 4 main groups of skills required to take full advantage of the AI-related opportunities.

1- AI-related IT and digital skills: these are specifically relevant to IT professionals and computer engineers.

2- AI-powered productivity skills: these are relevant to all professionals, since AI transforms practices within organisations. As such, everyone should be able to leverage existing AI tools to increase their efficiency and productivity.

3- AI-augmented business skills: these skills make it possible to transform specific existing business practices using AI. For example, designing a training course with AI, preparing a presentation with AI, producing content with AI, prospecting with AI, etc.

4- "Power skills" required to handle AI: these cross-cutting, behavioural skills linked to personal development have become more necessary than ever in order to use AI without being subject to the risks associated with that use. Examples include critical thinking, emotional intelligence, taking cognitive biases into account, etc.

Type of skills Skills to develop and maintain AI systems

Specialised AI skills

Examples General knowledge of AI (such as Machine Learning) Specific knowledge of AI models ("decision trees", "deep learning", "neural network", "random forest", etc.)

AI tools ("tensorflow", "pytorch", etc.) and AI software ("java", "gradie", "galaxy cluster", etc.)

Data science skills

Data analysis Software Programming languages, in particular Python Big data Data visualisation Cloud computing

Other cognitive skills

Creative problem solving

Transversal skills

Social skills Management skills

Skills to adopt, use and Elementary AI knowledge Principles of machine learning interact with AI applications Digital skills Ability to use a computer or a smartphone Other cognitive skills

Analytical skills Problem-solving Critical thinking Judgement

Transversal skills

Creativity Communication Teamwork Multitasking

Source: Lassébie, J. (2023), "Skills Needs and Policies in the Age of Artificial Intelligence", in OECD Employment Outlook 2023: Artificial Intelligence and the Labour Market, OECD Publishing, Paris, https://doi.org/10.1787/638df49a-en.

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Key focus points to help you get to grips with every aspect of GenAI Going beyond this general classification, and in a context where tools that incorporate AI are evolving at a dizzying speed, we feel it is important to bear in mind three key focus points: - Firstly, it is crucial to develop curiosity, experimentation, analysis, perspective, collaborative work, and stress management... rather than specialising in a tool that could be obsolete in three months.

- Secondly, it is essential to continue to develop the skills needed to work, communicate, and collaborate with AIs, in order to respond to future challenges rather than simply follow the path of technological augmentation.

Focus on 3 key approaches when working with GenAI - Master the art of prompting: think about the question before thinking about the solution. This is not that easy because we have always been taught to find solutions rather than ask good questions...

- Do not settle for the first few answers. Iterations are important, so be resilient when you encounter mistakes.

- Always validate the results obtained: are you sufficiently equipped to think critically about the proposed content?

- Finally, in order to work with AI as a partner, we will need to focus on the skills that make us more human (such as empathy, emotional quotient, and adaptability).

The 8 "Power skills" you need to get the most out of GenAI We have identified 8 "Power skills" – in other words, the key skills that make the difference in terms of employability – you need to work in a professional world that now includes GenAI as a real stakeholder.

4 Power skills to address the risks of AI

4 Power skills to work more effectively with AI

- Critical thinking, i.e. the ability to discriminate - The ability to learn how to learn, to memorise and filter different types of information based on better, to adopt a thought process and to their importance, veracity, or timeliness. develop our ability to transpose our skills into changing and unfamiliar contexts.

- The ability to reduce the cognitive biases that we may be subject to when using AI, such as conformity or familiarity bias, i.e. the "illusion of knowledge".

- The ability to consider the ethical consequences of using AI and act with responsibility in order to minimise the risks.

- Resilient in the face of accelerating technological progress: 1,000 AI systems are created every day!

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- Creativity, i.e. the ability to generate new concepts and solutions through the crosspollination of human and artificially generated ideas.

- Emotional intelligence, since AI can give us more time to focus on what makes us more human.

- Cross-disciplinarity, or in other words our ability to understand and make connections between different disciplines.

Focus on the skills that need to be developed among training professionals To incorporate and handle GenAI, training professionals also need to develop their own skills.

For them, the aim is to improve the learning experience by leveraging AI to its full potential, while retaining the human touch that meets learners' unique needs and preferences.

With this in mind, here are several types of skills that will be particularly useful to training professionals: - Fundamental AI knowledge: acquire a solid understanding of the basics of artificial intelligence, including the operational principles and functionalities of common models. It is essential to recognise the limitations of AI and its potential for tailored applications.

- Technical review skills: develop the ability to critically review and refine AI-generated content, ensuring it is in line with the programme’s objectives or documentation requirements.

This skill is essential for personalising content to effectively meet the needs of participants.

- Psychopedagogical overview: understand the dynamics of human-AI interaction, including the management of expectations and biases.

Trainers need to be able to guide AI to tailor content that caters for various learning styles and requirements.

- Advanced communication: master the art of translating complex requirements into AIunderstandable guidelines, ensuring that AI contributions are precisely aligned with the training objectives. Effective communication with AI systems is key to generating relevant and context-specific content.

- Training process management: learn how to integrate AI-generated materials into training programmes. This involves planning and coordinating sessions to ensure a consistent learning experience where AI elements are seamlessly integrated.

- Ethical sensitivity and bias awareness: cultivate an awareness of potential biases in AI results and implement strategies to mitigate them.

Taking an ethical approach to the use of AIgenerated content is fundamental.

- Monitoring and assessment skills: implement strategies to assess the impact of AI-generated content on training results. Adjustments based on participant feedback and human judgment are essential for the continuous improvement of AI contributions.

- Agility in continuous learning: continuously learn about AI advances. Staying agile and adapting to new developments in AI ensures that training practices remain at the cutting edge of technology.

- Strong sense of technology integration: understand how to integrate AI tools into existing training frameworks, enhancing the training ecosystem with AI capabilities without replacing valuable traditional tools.

- Focus on the learner experience: ensure that AI-generated materials are not only informative, but also engaging, which improves the overall learning experience. Continuous monitoring and adaptation based on learner feedback are essential in order to maintain an effective and enjoyable training experience.

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Our solutions to support your transformations through skills development The Cegos Group Learning Collection Our Cegos Group Learning Collection is organised into 5 main areas of expertise, with 20 associated core skills required for both today and the future.

This collection presents a wide range of solutions to develop fundamental cross-cutting skills for interacting with AI, such as creative thinking and innovation, communication, teamwork, sustainability, and inclusion.

In particular, the field of "Organisation and Business Transformation" covers the skills needed to "navigate" through change, innovate, and drive sustainable transformation. This includes AI, digital and data skills.

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In 2023, the Cegos Group launched the Digital Skills4All collection, with several videos aimed at non-IT professionals. Designed by experts in their respective fields, these short videos aim to inspire and empower individuals to actively participate in the ongoing digital transformation.

How can you make digital work more responsible?

How can you optimise digital collaboration with SaaS tools?

Digital Collaboration

How can you encourage Diversity, Equity and Inclusion in digital collaboration?

How can you share and co-edit a file with others?

How can you master the art of prompting?

How can you use data to build a convincing storytelling?

Data Literacy

How can you determine that a piece of data is relevant and reliable in order to make a decision?

How can you present data effectively?

Digital Well-being

How can you manage infobesity?

Digital Security

How can you avoid phishing attacks?

How can you overcome "digital addiction"?

How can you distinguish between what is fake and real online?

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How Cegos designed this new Digital Skills4All collection with GenAI… and what we learnt from that experience Digital Skills4All is the first complete Cegos training offer to draw on GenAI: - Definition of the scope of the training collection: here, Cegos experts used ChatGPT3 to fine-tune the training objectives and ensure that the key content was properly covered.

ChatGPT3 provided additional support to "strengthen" the narrative structure of the course.

- Quiz creation: ChatGPT3 was used to build the first version of the storyboards, which was then submitted, improved and validated by our experts.

- Production phase: since this collection addresses digital skills, it made sense that the videos would be narrated by an AI avatar and not by a real actor.

- User testing: although using an AI avatar is very effective for creating content in more than 22 languages, such as the content proposed

by Cegos. A user test was carried out, which provided certain ideas to improve the narration, posture and tone of the AI; consequently, the AI avatar is well accepted by learners and is not a distraction or an obstacle to learning.

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6 lessons learned from this first experience: - Dig deeper into the art of prompting to optimise the quality of AI-generated results.

- Be wary of sensitive data: the data we provide will feed the algorithm for other users. Keep an eye on intellectual property and always bear in mind that the results may be biased!

- Do not take AI's words at face value: ChatGPT was not programmed to tell the truth. Always review and approve any content before posting it. Actively work to reduce and eliminate bias and ensure that AI models do not reinforce societal biases or engage in unfair discrimination.

- Be transparent and always indicate when the learner is interacting with an AI agent.

- Learn to write for AI avatars: they have limited expressiveness, and their interactions are weak and artificial, which can result in unsettling and distracting reactions. Writing for an AI avatar is not the same as writing for a real actor!

- Costs are still high: the most powerful AI tools still cost a lot to use. It can be assumed that the democratisation of these tools will make them more affordable in the future.

Whatever your challenge in terms of skills development, the Cegos teams are here to support you.

Increasingly digital, increasingly cross-cutting, increasingly open…

Your organisation is in constant motion, just like the environment in which it exists.

Your teams’ ability to rapidly adapt to the market and environment has become a key factor in your company’s competitiveness, since skills are an increasingly strategic intangible asset.

Developing your teams' professional skills to improve collective performance

Providing support for upskilling or reskilling requirements linked to transformations

We help your organisation adapt to changes that affect its professions and business lines, from mastering fundamental skills through to supporting the vocational development of your teams.

Whether the transformations involve specific professions, behaviour, management practices, digital technologies, L&D, CSR or tools, etc.

Deploying your training projects internationally Cegos teams can work alongside you anywhere in the world in order to help you roll out your projects, create an international catalogue or provide direct support to L&D teams.

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Contact

Design: Yannick Verkindere - Photos credits: Getty Images

Corporate Communications Mathieu Cadot – mcadot@cegos.fr Alexandra Cavanna – acavanna@cegos.fr