Streamlining E-Learning With AI: 5 Use Cases

Optimize e-learning experiences with AI. Uncover five actionable use cases for streamlining e-learning with AI.

Providing comprehensive and impactful e-learning can be overwhelming.

After all, you must sift through a vast content pool, meet diverse learner needs, and ensure global relevance. Too many variables. Too many moving parts.

That’s why companies consider using artificial intelligence (AI) in e-learning.

Integrating AI can streamline the implementation of learning and development for HR professionals, training managers, instructional designers, and marketers. It can also bolster the learning experience for learners.

This article covers the use cases of e-learning AI, including how the technology adapts content for a diverse global audience and accelerates translation and production.   

Use Case 1: LLMs for Content Localization

Large language models (LLMs) are machine learning models trained on massive amounts of textual data to learn and generate human-like language. It is trained on extensive datasets that aim to understand patterns, structures, and contextual nuances within language.

LLMs can be trained to understand and recognize textual content within the e-learning images.

The capability facilitates image-based content adaptation to various cultural contexts, languages, or regions. It allows learners with diverse backgrounds to access and comprehend your learning material effectively.

Automatic image editing processes and solutions also use LLMs.

The models combined with image processing algorithms can automatically modify and edit images and alter or translate text.

It allows for seamless and contextual adjustments and image text translation into multiple languages.

LLMs incorporate language understanding and processing to localize and modify image-based learning content.

It provides a powerful solution to create a multilingual, adaptive, and up-to-date e-learning platform and environment that aligns with your organization’s training policies and goals. 

Use Case 2: AI in Synthetic Voice Technology

Synthetic voice technology generates computerized or artificial voices to turn written texts into spoken language.

The technology uses advanced linguistic processing and algorithms to produce human-like speech intonations, expressions, and patterns.

In e-learning, technologies such as AI-assisted synthetic audio can generate human-like voiceovers for training content.

It allows for voice personalization, including tone, pace, and accent, to accommodate various learning preferences, especially for auditory learners.

Modern AI-assisted synthetic audio technologies can have the following features and functionalities:

  • It uses advanced neural networks and is trained on human speech
  • Achieves high sample rates that match studio-quality recordings
  • Generates AI voices that modulate tone, speed, and pitch to deliver expressive and natural-sounding voices
  • Adapts to feedback, such as correcting mispronunciations over time
  • It offers diverse voices and captures various languages, accents, genders, and ages

AI-generated training content via synthetic voice technology can make e-learning more efficient, streamlining content production with rapid audio generation.

AI-assisted synthetic voiceover for e-learning is also cost-efficient, with faster turnaround times, cutting production time and expenses by half compared to human voiceovers.   

Use Case 3: AI for Simulations and Interactive E-Learning Materials

AI-driven simulations, interactive scenarios, and demonstrations can give your workforce hands-on practical learning experiences.

For example, AI-based platforms can leverage generative models to generate videos with realistic and dynamic visual content for e-learning.

Generative AI (GenAI) can help create branching narratives or interactive scenarios within videos, allowing e-learners to select their preferred language or follow specific paths aligning with their cultural context.

The technology can also generate customizable characters or avatars that provide information within the video.

It allows designing characters or avatars to represent diverse appearances and cultures, making content more relatable to a multilingual, global audience.

GenAI offers countless possibilities for e-learning video development.

However, human oversight and review are still crucial to ensure the generated content aligns with the learning objectives and meets the learners’ needs, especially in a multilingual workforce. 

Use Case 4: AI for Training Program Analysis and Enhancement

AI is crucial in training program analysis and improvement since it can analyze, track, and assess critical data such as learning progress.

For example, AI algorithms can analyze massive learner datasets during training and identify trends, correlations, and patterns to give you actionable insights into learner performance.

The algorithms can also unveil strengths and gaps, allowing you to promptly address weaknesses and refine your training program to optimize e-learning.

AI can also do the following:

  • Automate the assessment of quizzes, simulations, and assignments and give instant feedback so learners know their performance immediately. 
  • Use natural language processing (NLP) algorithms to analyze qualitative learner feedback. AI can derive learner sentiment, spot common themes, and give a deeper understanding of the learning experience.  
  • Power predictive analytics to predict individual learning paths and outcomes. AI can analyze historical data and suggest personalized learning journeys best suited to each learner for a more effective training program.   
  • Track learner engagement and determine issues in real-time, facilitating prompt interventions to support each learner’s progress. 
  • Optimize learning paths by dynamically adjusting them based on learner progress. AI can detect areas of struggle and modify the curriculum or suggest additional resources to address specific needs, optimizing the training experience. 

Use Case 5: AI for Code Development Learning

AI in code development learning offers innovative methods and tools for programming training and education.

GenAI tools can automate the generation of code-learning content, including explanations, tutorials, and examples of programming languages and concepts.

For instance, tools such as ChatGPT allow natural language interaction to write code development course content.

Educators or trainers can go into dynamic conversations with the AI model to instantly generate course content, including quizzes, tasks, and coding challenges in multiple languages.

AI, including GenAI-based models, can suggest and autocomplete code, helping e-learners write code correctly and efficiently while implementing programming best practices.

AI models can also provide learners real-time assistance, giving immediate feedback and addressing learner queries on code snippets.

The models can analyze common learner errors and guide e-learners to understand programming concepts and overcome challenges effectively.