AI in Learning and Development: Personalizing the Employee Learning Experience

Written by Dieter Veldsman, Ioanna Mantzouridou Onasi
8 minutes read

AI in learning and development already has many applications, such as providing learning recommendations, curating content, and improving analytical insights. Moving forward, we can expect higher levels of AI adoption and experimentation in learning, given the advances brought by generative AI technologies.

In this article, we explore four ways that AI is already changing how employees learn and propose an action plan for how HR can adopt AI in learning and development responsibly.

The current state of AI in learning and development
Four ways that AI is changing the employee learning experience
How HR can drive responsible AI adoption in learning and development

The current state of AI in learning and development

The Learning and Development function has pioneered the adoption of AI in HR. The online learning platform market size has increased exponentially over the past few years, and the estimated investment in the AI education market will grow to a compounded annual growth rate of 36%, reaching USD 32.27 billion by 2030.

AI has contributed meaningfully to learning management systems, learning experience platforms, and learning analytics

However, the adoption of AI has not been without its challenges. Firstly, the technology behind AI has not yet advanced enough for widespread use. Additionally, L&D teams have lacked the maturity to effectively integrate AI into their work.

Issues like bias, intellectual property rights, and the difficulty in combining AI with different learning platforms have also slowed the impact of AI on learning and development.

AI has been criticized as much of its adoption has benefited those creating learning content, with less of a focus on the learner and their experience. Amidst all the promise of learning personalization, AI has yet to deliver to its full potential.

These limitations are starting to become something of the past, especially with the new capabilities that generative AI brings to the table. A key focus not only on the process and management of learning and development but its actual application to the learning experience is starting to usher in a new era of AI-based learning.

We discussed the future of L&D with Ioanna Mantzouridou Onasi, CEO and Co-founder of the AI coaching platform Dextego. Watch the full interview below:

4 ways that AI is changing the employee learning experience

Let’s take a look at how AI has impacted the employee learning experience.

1. Generative AI is making learning accessible to all

Learning accessibility has always been a critical challenge, especially concerning the needs of neurodivergent employees.

Standards such as the W3C Accessibility Guidelines (WACG) have proposed principles to make learning perceivable, operable, understandable, and robust (POUR) to meet the needs of different users. In the past, achieving these standards has been difficult for learning vendors

Increased efforts to convert content into multiple formats, color schemes, and languages to accommodate different learning needs have been costly and often led to the exclusion of many of the workforce in online learning.  

Given the recent advances in AI, vendors can address these challenges more cost-effectively. A good example is braille translation software that converts text into braille and makes it accessible for sight-impaired learners. As with all AI, challenges remain in this area, yet there’s been significant progress in making learning content accessible to all employees within the organization.

More generally, AI has provided the ability to automatically transcribe audio and video content to text for hard-of-hearing learners and to use AI to drive auto-translation into various languages. This has opened new markets for learning vendors. Also, organizations operating across multiple geographies can provide a more consistent learning experience regardless of region while saving content development time for internal L&D teams.

2. AI is making learning personal and curated

Given the amount of new learning content generated daily, the biggest challenge for learners is often knowing which content is applicable and relevant to their specific needs.

Even though AI has been used to suggest and recommend learning content before, recent advances have improved its accuracy significantly. Today, AI uses various data sources, such as assessment data, learner interests, career goals, and past learning experiences, to propose personalized learning.

A good example is how Capgemini has applied Anderson Pink to curate content and drive personalized skills-based learning. Quuu, UpContent, and are good examples of content curation tools that help look at public domain content regarding a specific topic and incorporate key areas into organizational learning strategies.

This has already created new opportunities to adapt learning to individual development plans and career aspirations. Learning recommendations tended to be somewhat generic in the past, whereas AI-based suggestions enable a more curated learning experience across content types, catalogs, and libraries.

Especially in career development, platforms such as Fuel 50 have allowed organizations to rethink internal mobility and drive career ownership, as highlighted by their work with KeyBank.

3. AI is acting as a learning coach to improve the learning impact

Beyond content curation, AI has also become a learning coach that provides real-time feedback and suggestions to learners related to specific skills.

There are several ways AI complements or fulfills the role of a learning coach, including addressing queries, responding to learning challenges, and providing feedback and support throughout the learning process. Organizations are starting to implement safe and responsible AI-based coaching specific to learning utilization.

Players like Dextego focus on a fun and engaging learning experience related to human or soft skills for Gen Z learners.

Another example is Wondder, who uses AI and virtual reality to give feedback on scenarios such as performance discussions or DEIB situations. For example, they implemented a gender awareness program at Advance to give participants different experiences to raise awareness of gender inequality.

LinkedIn Learning is rolling out a new AI-powered coaching system that will enable members to seek guidance on specific business questions. Using a chatbot interface, it will act as a coach by guiding users through their learning journey, answering specific questions, and recommending related learning content based on the user’s job and situation.

As a learning coach, AI can significantly enhance the efficiency, personalization, and effectiveness of the learning experience. It provides a level of individual attention and support that can be difficult to replicate in conventional learning environments, especially at scale.

4. AI is becoming a powerful content creator

Even though this is still in its infancy, we have seen several new AI-based content generators. Specifically, in this application of AI, we extend a word of caution as the accuracy of newly generated AI content is not without problems.

Nonetheless, within the organizational context, AI can be a powerful tool as a content generator. For example, AI can be effectively used to create employee learning content for policy training or other operational processes, particularly when there is already substantial knowledge and documentation available. This provides a solid foundation for AI to learn from and generate content that is specific to the context.

Organizations such as Park+ and Preply have successfully used Narrato Workspace as an AI content creator. We expect more utilization of AI content generators in the future for both in-house and open-source content.

The opportunity for AI in learning and development will contribute to a more engaging and personalized learning experience. However, adopting AI should be done responsibly to ensure concerns such as ethics, relevance, and privacy are respected.

A step by step process for HR professionals to adopt AI in learning and development.

How HR can drive responsible AI adoption in learning and development

HR has a crucial role to play in successfully integrating AI into learning and development. Let’s have a look at six steps HR can take to ensure responsible adoption of AI solutions within the learning and development domain.

Step 1: Understand what AI needs to achieve

First, HR needs to understand what the purpose of the AI application is. For example, approaching AI as a coach for employees versus AI as a content creator will have different implications and implementation approaches.

As a starting point, draw up a clear business case of what is in and out of the scope of the AI application. This should also include looking at:

  • If existing technology infrastructure, such as hardware, software, and network capabilities, needs upgrades or additions to support AI learning tools.
  • How the AI learning tools will integrate with existing L&D systems and tools like Learning Management Systems, HR systems, and workflow tools.
  • What skills your team might need to implement and manage AI-driven learning initiatives, for example, understanding AI and data analytics.

Step 2: Find the right partners and vendors

Next, you should thoroughly familiarize yourself with the partners you will work with. 

Unfortunately, AI has become a buzzword, and vendors often use this terminology in their marketing efforts without it translating into actual solutions. Do thorough due diligence and ensure that you include someone with technology knowledge to aid you in vendor decision-making.

Choosing partners who demonstrate a commitment to ethical AI practices is essential. This includes transparency in how their AI systems work, handle data, and address potential biases. For example, if the algorithm is trained mostly on data from a specific demographic (e.g., male employees in tech roles), it might be less effective or relevant for female employees or those in non-tech roles.

Step 3: Pace your adoption of AI and start within a controlled environment

Ensure a responsible pace of the AI implementation that allows you to answer a few questions:

  • Are we clear about where AI gets its data, and do we trust the source?
  • Do we understand how AI learns?
  • How will we monitor AI outputs to ensure quality, accuracy, and relevance?
  • Which use cases are we prioritizing?
  • What can the AI not do?

Step 4: Socialize the idea of using AI with your employees

You must be transparent with your employees on how and where AI is incorporated into your learning experience.

Be open about using AI and create a FAQ document with the basic questions for learners who want to know more. It is important to specifically focus on how data is gathered, used, collected, and stored, as well as the guarantees regarding data privacy that you can provide to learners.

Step 5: Implement key control and monitoring governance

Ensure that there is transparent governance regarding how use cases will be monitored, how you will collect feedback from employees on the applicability and impact of using AI, and how to link its use to the measurement of learning effectiveness.

Step 6: Optimize the use of AI over time

Last, you can increase the utilization of AI to additional use cases and audiences over time once you are comfortable that the AI is responsibly delivering the expected value.

This incremental approach allows for careful monitoring and fine-tuning of AI systems, ensuring they meet your employees’ and organization’s learning needs effectively and ethically.

Wrapping up

AI will continue to change the learning and development landscape, and HR and L&D professionals have the exciting opportunity to lead this transformation. By implementing AI responsibly, they can continue to enhance the employee learning experience, making it more accessible, effective, and relevant to a wide range of audiences.

It’s crucial to navigate this journey cautiously. Only responsible adoption will enable long-term value while mitigating the current risks related to bias, privacy, and property rights.

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Dieter Veldsman

Chief HR Scientist
Dr. Dieter Veldsman is an Organizational Psychologist with 15+ years of experience across the HR value chain and lifecycle, having worked for and consulted with various organizations in EMEA, APAC, and LATAM. He has held the positions of Group Chief People Officer, Organizational Effectiveness Executive, Director of Consulting Solutions, and Chief Research Scientist. He is a regular speaker on the topics of Strategic HR, Future of Work, Employee Experience, and Organizational Development.

Ioanna Mantzouridou Onasi

Ioanna is an ambitious and purposeful Community Builder, Talent Development Strategist, and Applied AI Evangelist. She is the Co-founder & CEO of Dextego. Dextego is an AI Coaching Platform for reducing top talent attrition by half via personalized soft skills training. Ioanna is passionate about utilizing the power of technology as a tool to help people hone their essential skills and tap into their inner brilliance. Before co-founding Dextego, she was the VP of People & Chief of Staff at Aptivio, a B2B SaaS Revenue AI startup in NYC, building the Aptivio Ecosystem.

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