3. The 7 Artificial Intelligence Use Cases for Learning and Development

In a recent episode of the Training Impact Podcast, Jeff Walter, founder and CEO of LatitudeLearning, explores the transformative potential of artificial intelligence (AI) in the learning and development (L&D) industry. Drawing historical parallels to past technological revolutions such as steam power, electricity, the internet, and personal computing, Walter sets the stage for AI as the next major wave poised to reshape all facets of life—including education and workforce development.

Understanding AI's Trajectory

Walter cautions that like all past revolutions, the current wave of AI is accompanied by overhyped expectations and potential setbacks. However, its long-term implications are undeniable. As with the internet, which fundamentally altered commerce and communication, AI will redefine how learning is delivered and consumed. Walter suggests that rather than fearing AI, L&D professionals should focus on the practical applications emerging today while maintaining a cautious and strategic approach.

Seven High-Impact AI Use Cases in Learning & Development

1. Chatbot Support AI-driven chatbots can revolutionize job aids and learner support by offering instant answers based on a curated body of knowledge. Instead of scouring internet sources, learners interact with bots trained on specific internal data. Walter emphasizes the importance of controlling the knowledge base to ensure consistency and accuracy. At LatitudeLearning, chatbots are already assisting administrators, with plans to extend this functionality to clients.
2. Content Creation AI is dramatically increasing productivity in content creation. Tools now embed AI to help draft courses and learning materials, especially beneficial for custom, company-specific training content. This is a game-changer for organizations that previously found content development too niche or expensive. AI allows creators to maintain control while enhancing efficiency, whether through text generation, multimedia content, or videos via tools like Synthesia.
3. Assessment Generation Creating high-quality assessments is time-intensive and often deprioritized. Generative AI now helps craft comprehensive tests with well-constructed distractors and meaningful evaluations. Walter envisions a future where assessments might precede training to pinpoint knowledge gaps, ensuring learners only train on what they don’t already know. Though still in development, this shift could lead to more efficient and targeted learning.
4. Role Playing for Skill Development Walter highlights the difference between knowledge acquisition and skill development using the analogy of a driving test (written vs. road test). While L&D has excelled at knowledge training, AI is enabling scalable role-playing simulations that mimic real-world interactions. Learners engage with AI avatars that respond dynamically and evaluate performance using predefined rubrics. This supports development of soft skills like interviewing, sales, and conflict resolution.
5. Simulation for Hard Skills While simulations have long existed in high-stakes environments like aviation, their prohibitive cost limited broader adoption. AI-driven tools are now slashing development costs, potentially bringing simulations to industries like automotive repair or surgical training. These advancements will make it economically feasible to deliver high-quality, hands-on technical training in diverse environments.
6. Advanced Analytics A persistent challenge in L&D is demonstrating return on investment. Traditional statistical methods are complex and often underutilized. AI can automate multivariate analysis, revealing which training programs impact performance metrics. This not only validates L&D investments but also elevates the strategic role of training by tying it directly to business outcomes.
7. Individualized Learning Plans Building on analytics, AI can tailor learning paths at the individual level. By analyzing a learner’s background, role, assessments, and performance data, AI creates a personalized development plan. Though still nascent, some Fortune 500 companies are already implementing this approach to align training with career aspirations and job performance.

Implementation Considerations and Final Thoughts

Walter advises L&D professionals to take a measured approach to AI adoption. High-stakes training environments should be particularly cautious, especially when AI generates outputs without human review. Use cases like content creation and assessment generation allow for human oversight and control, while real-time applications like chatbots and role playing demand robust knowledge management and ethical safeguards.

He emphasizes the importance of starting small, experimenting, and learning how to best integrate AI tools into existing systems. By doing so, organizations can benefit from increased productivity and effectiveness without risking quality or learner trust.

Conclusion

AI is more than a trend—it's a foundational shift in how learning is designed and delivered. With thoughtful application, AI has the potential to significantly enhance training impact across industries. Whether it’s through chat support, simulations, or personalized learning plans, Jeff Walter envisions a future where L&D professionals harness AI to drive efficiency, effectiveness, and engagement. The key, he stresses, is to walk before you run—adopt AI carefully, control its inputs, and always ensure a human touch in high-impact learning environments.

 

Chapters
00:00 The AI Revolution in Learning and Development
03:03 Chatbot Support: Enhancing Learning Experiences
05:54 Content Creation: Boosting Productivity with AI
08:51 Assessment Creation: Improving Knowledge Evaluation
11:46 Role Playing: Developing Soft Skills with AI
17:03 Simulation: Cost-Effective Skill Development
20:02 Analytics: Proving Training Impact
22:53 Individualized Learning Plans: Tailoring Education with AI