Artificial intelligence (AI), defined as the ability of a computer or robot to perform tasks that would normally be completed by a human, including, but not limited to, visual perception, identifying patterns in data, speech recognition, decision-making and language translation, is a rapidly advancing technology with many applications in learning and development (L&D).
AI-driven technologies can help learning leaders maximize their time and resources. As a result, they can deliver more efficient and effective learning at scale.
It’s important that L&D professionals have robust AI skills to utilize these tools effectively. Experimenting with AI tools, learning how to write effective prompts, keeping up with industry-specific news and innovations, and/or taking a course or workshop, can help training professionals build their AI skill set.
Identifying AI Use Cases
Learning professionals also need to be able to identify AI use cases in their job role.
To determine which tasks to automate, look for recurring challenges or inefficiencies in your workflow, such as time-intensive content creation, building skills assessments or scaling personalized learning. Tasks involving handling sensitive data or that require strict adherence to compliance standards are often better suited for a human.
Some of the most common use cases for AI in training include:
Content development:
Generative AI enables training professionals to create content quickly and efficiently. AI-powered tools can help:
- Generate course outlines, modules and multimedia elements, like interactive graphics or videos to be used in an eLearning course.
- Design realistic scenarios for role-plays and simulations.
- Support adaptive learning by creating content tailored to diverse learner needs.
Learning leaders will need to adapt AI-generated content according to their unique business needs and feedback from subject matter experts (SMEs), but generative AI can help streamline the content creation process significantly by providing a “good enough” outline to work from. This can save significant time that can be allotted to other L&D processes or tasks.
Translation and localization:
L&D professionals working for global organizations can use AI to create training programs that are accessible to diverse, multilingual workforces. AI-powered translation and localization tools can help translate training materials into multiple languages, adapt content according to cultural nuances and norms, and automate updates across localized versions of training materials to ensure consistency.
Personalization and learning pathways:
AI can help learning leaders create scalable, highly personalized training experiences.
Key personalization capabilities include:
- Curating personalized learning pathways based on learners’ needs and preferences.
- Recommending targeted resources and courses based on assessment results and identified skills gaps or career goals.
Delivering adaptive content that evolves as learners progress.
This level of customization is hard to achieve without the help of AI-powered technology. By leveraging AI for personalization, learning leaders can deliver more engaging, tailored learning that is directly applicable to learners’ job roles and short- and long-term goals.
Coaching and feedback:
AI-powered coaching tools can uncover data-driven insights into a learner’s skills and, as a result, deliver personalized suggestions and areas for improvement. These tools work by analyzing video, audio and text to evaluate a learner’s skills.
Many AI coaching tools offer interactive role-play simulations with AI avatars, where users can practice their skills in realistic scenarios. For example, a manager could practice having a difficult conversation with a team member and then receive real-time feedback with suggestions and tips.
Responsible AI Use
Although AI offers significant benefits for training, there are important ethical concerns to consider regarding data privacy, bias, transparency, proprietary content and more.
Responsible AI use requires being aware of the risks involved with using an AI-powered tool and adopting best practices for responsible using AI for L&D.
These risks and best practices include, but are not limited to:
- Mitigating Biases: Regularly review and evaluate AI-generated training materials or recommendations for signs of bias, and always use human oversight to ensure fairness and inclusivity in training content and processes.
- Delivering SME-Level Insights: You’ll also want to avoid using AI when delivering training where people are paying for your expertise. In these cases, people are paying for SME-level insights that cannot be generated by an AI technology or tool.
- Maintaining Data Privacy: Sensitive employee data must be carefully handled to ensure privacy and security. As a result, human oversight is crucial when using AI-powered tools or systems. Be sure to review and fact check AI outputs.
- Adhering to AI Use Policies: As a best practice, always review any AI policies or regulations your company has in place before using AI for training-related processes or tasks. Adhere to these policies or regulations when using AI for L&D in your organization.
- Protecting Proprietary Content: Generative AI risks misusing proprietary content by replicating or pulling from unlicensed or proprietary materials, leading to potential intellectual property infringement. Avoid inputting confidential or sensitive data into AI tools.
- Awareness of Environmental Concerns: As a result of the increasing demand for AI technologies, data centers are multiplying rapidly and it’s been reported that their storage capacity is expected to grow significantly. Generative AI’s greater energy requirements — ranging from 300 to over 500 megawatts — will also require more energy efficient designs and locations. These larger-scale AI-related concerns are important to be aware of.
By building AI skills and maintaining human oversight, learning leaders can effectively leverage AI for more efficient and scalable learning across the business.