How Generative AI Is Transforming Learning and Development

Generative AI is reshaping how we design programs, analyse needs and deliver learning experiences. I’ve just completed Dr Terri Horton’s Applications of Generative AI in Learning and Development on LinkedIn Learning, and I came away with a better knowledge of what is possible for our profession.

The course shows how AI augments the work of L&D teams by helping us think, design and deliver with greater clarity and efficiency. It is built around a simple idea: generative AI can expand our capability to create targeted learning solutions that meet organisational needs while improving learner engagement.

Below are the key insights that stood out for me.

gen ai & Needs analysis

Needs analysis remains the heart of any good learning intervention. What changes with AI is the speed and depth at which we can uncover patterns.

Generative AI allows us to upload quantitative data, qualitative feedback, survey results and interview transcripts, then receive a coherent analysis of skill gaps and capability themes. Instead of spending days sifting through spreadsheets, L&D teams can identify trends in minutes and refocus their energy on interpretation and strategic planning.

This is particularly valuable in complex organisations where performance data is dispersed across systems. AI supports us by unifying the information and making themes transparent. In other words, it strengthens the human decision-making that follows.

A stronger foundation for learning objectives

Once the capability gaps are clear, AI becomes a thought partner in defining learning objectives. AI should not write objectives blindly. We must provide the parameters, constraints and strategic goals, then refine its suggestions.

The result is a set of behaviour-based, measurable objectives that link directly to organisational priorities. This is such a great help, especially when working under time pressure or with large stakeholder groups.

Designing learning journeys with more depth and creativity

I found the section on program design particularly valuable. AI is not a replacement for instructional design but an accelerator for it. With well-written prompts, it can:

• compare learner groups and their preferred modalities
  
  

• generate draft program structures
  
  

• propose interactive elements such as scenarios and microlearning
  
  

• outline entire sequences for multi-module or multi-day programs
  
  

AI enhances learner-centred design by helping us explore more options than we might have time to generate on our own. This is especially true when designing blended solutions where balancing asynchronous content, workshops, coaching and workplace practice is essential.

Producing content faster without sacrificing quality

A significant portion of the course is dedicated to content creation and curation. Here, AI genuinely transforms the workload. It can:

• produce slide structures and drafts
  
  

• generate video scripts
  
  

• create graphics, diagrams and text-to-audio outputs
  
  

• summarise industry research and curate recent insights
  
  

For L&D teams managing multiple projects, this reduces cognitive load and frees up time for the creative and relational parts of our work. More importantly, it helps us maintain quality and consistency across programs.

Deepening engagement through simulations and role plays

AI can now draft branching scenarios and convert them into conversational practice activities that learners complete dynamically.

This has enormous potential for behavioural and communication-based training, especially in leadership, customer service and compliance. Instead of static examples, learners experience evolving situations that respond to their decisions. It is a shift from passive consumption to active skill rehearsal.

Designing meaningful assessments with AI

Finally, the course reinforces the role of AI in building more coherent and purposeful assessments. It can draft quiz questions, scenario-based tasks, rubrics and workplace application activities that map back to our objectives.

Again, this doesn’t replace instructional judgment. It strengthens it by offering structured possibilities that we refine based on context. The result is assessment that is more aligned, more transparent and easier to scale.

Why this course matters for L&D practitioners

The real value of this course lies in how clearly it shows that AI is not an add-on. It touches every stage of the L&D workflow, from needs analysis to assessment. It expands what L&D teams can deliver and allows us to focus on strategy, learner experience and organisational impact.

More importantly, it reinforces a key principle: AI is most powerful when guided by human insight. It helps us work faster and think more broadly, yet the ultimate decisions still rest with us.

Completing this course has strengthened my understanding of how generative AI can support modern L&D practice. It has also given me practical frameworks I can apply immediately to real projects, from needs analysis to module design. For anyone working in L&D, especially those exploring blended learning, capability frameworks or high-volume content development, this course is an excellent starting point for integrating AI into your practice.

This post is informed by the “Gen AI in L&D Steps” workflow I created while going through the course. The workflow can be downloaded here.