There is a growing fear that smart computers and robots will replace humans in the workforce. While that is a discussion worth having, right now, the reality is that AI and machine learning are helping people achieve more with more effective time and resources — this is particularly true in online and e-learning.
AI-based learning platforms and Learning Management Systems (LMS) are making cloud-based employee and client training more productive for students and supervisors. This is happening by:
To top it off, automating these functions reduces costs and human resource demand, makes e-learning significantly more scalable for training, and increases learner performance.
AI, or Artificial Intelligence, simulates human intelligence processes, potentially allowing programs to perform tasks that typically require human understanding and problem-solving capabilities. In e-learning, this typically resembles insightful pattern/behavior recognition and decision-making.
Machine learning, a subset of AI, involves algorithms and statistical models that enable computers to learn from and make predictions or decisions based on historical trends, without explicit programming.
So what exactly does this look like, and what does it mean? Let’s use an example to help us to better grasp it.
Sophia, an architect, is studying French as a second language through an online program in order to boost her career prospects in the Canadian government.
The AI on her course’s LMS analyzed her performance in real time and recognized that she excelled in vocabulary but struggled with verb conjugation. The system automatically adjusted her learning path to provide additional practice exercises and resources on conjugation rules.
Furthermore, AI identified Sophia’s preference for auditory learning and triggered changes to her course material to prioritize content that features audio-based exercises and resources. This helped her better absorb and retain information, making her study sessions more productive and enjoyable.
Thanks to the AI enhancements, Sophia made significant strides in learning French. The personalized learning path, coupled with adaptive content and AI-driven support, enabled her to master challenging concepts and achieve her language goals more efficiently and enjoyably.
By leveraging the capabilities of AI and machine learning, an LMS can provide personalized learning experiences, analyze skill gaps, and curate detailed learning reports.
AI and machine learning can implement automated management functions to prioritize learners’ performance, engagement, and preferences to create customized learning paths. This ensures learners receive the most relevant content and resources tailored to their specific needs and skill levels, improving their overall learning experience.
A personalized learning path:
Online learning has typically been a boilerplate and analog method of online training due to the nature of limited instructor-student interactions and hands-on management. This means e-learning has really only been a viable option for specific learning styles.
With the personalization capabilities through AI integration, e-learning is making training more scalable and accessible for more people.
When integrated into an e-learning LMS, AI and machine learning can help identify individual user skill gaps, provide insights into learning deficiencies, and make data-driven decisions to ensure learner success. Here’s how the process typically unfolds:
Data Collection — Algorithms continuously collect and analyze user input data and behavior, such as time spent on modules, engagement with various types of content, assessment performance, and any feedback provided.
Identifying Skill Gaps — The models can then distill this data to pinpoint specific learner patterns and areas where they struggle or excel. Skill gaps are automatically gauged against peer-related benchmarks and learning objectives, as well as by observing their progress over time.
Insights on Learning Deficiencies — These models generate insights into these learning deficiencies by matching performance issues with complex user data. For example, the algorithms may determine that a learner is struggling with a specific topic, concept, or skill, or that they are having difficulty understanding certain types of content or engaging with particular learning formats.
Personalized Interventions — Based on the identified skill gaps and insights into learning deficiencies, the AI-driven LMS can make decisions to tailor the learning experience to the learner’s needs. This may include adjusting the content, format, or difficulty level of the learning materials, as well as providing additional resources, exercises, or support to address specific challenges.
Continuous Improvement — These AI models and learning algorithms will continually monitor learner progress and engagement following automated interventions in order to ensure the proper adjustment were made, and to further adjust learning paths as needed to ensure ongoing improvement.
By providing real-time feedback and adapting to the learner’s evolving needs, the system helps learners overcome skill gaps and achieve their educational objectives.
AI & machine learning are valuable in the e-learning market because of the clear benefits they provide to end-users and training hosts.
As previously alluded to, this includes things such as:
Let’s explore why these benefits are setting AI-powered LMS apart.
Automation can significantly improve efficiency for both learners and training managers in an e-learning environment.
LMS platforms with AI-driven personalized learning models can increase learner engagement, reduce time to competency and improve overall learner satisfaction.
A 2021 study published in the Institute of Electrical and Electronics Engineers found the use of AI-driven LMS platforms significantly improves the quality of education in several dimensions by adapting to the distinct characteristics and expectations of each learner such as personality, talent, objectives, and background.
Automating various routine administrative tasks, such as tracking learner progress, generating reports, and scheduling training through AI can greatly improve efficiency and scalability bandwidth for training managers. These technologies are capable of analyzing large amounts of user data to provide actionable insights.
This not only saves time but also allows teachers and trainers to focus on more strategic-level goals and more personalized support to learners when needed.
By improving efficiency and streamlining administrative tasks, AI and machine learning can lead to cost savings for both end-users and training hosts.
Because AI-driven personalized learning paths lead to faster time to competency, learners are not only sure to clear courses on their first attempt but also do so faster. This means less wasted time and money while achieving learning goals.
For managers, a higher turnaround of learners through AI-powered benefits means a better market reputation and larger scalability for increased student body sizes.
AI and machine learning can help proactively support learners in several ways: anticipating needs, identifying potential issues, and providing real-time assistance.
Early Warning Systems — Detecting early signs of disengagement, confusion, or struggling with specific content, is a key to reducing learner churn for LMS platforms. AI and machine learning makes tracking these indicators and reporting them much easier through automation.
Through real-time data analysis, LMS platforms can pinpoint these issues proactively and trigger alerts or interventions, such as additional support from an instructor, supplemental resources, or adjustments to the learning content.
Goal Setting and Progress Tracking — Based on data collected and tracked by AI, e-learning platforms can assist users in setting personalized learning goals and meter their progress. By visualizing their progress and celebrating milestones, users can stay motivated and engaged in their learning journey.
Push Engagement — AI can send personalized reminders and recommend certain push notifications to users based on personal progress, schedule habits, and goals. These tailored recommendations can prompt users to complete tasks, review materials, or participate in discussions, ensuring they remain actively engaged with the course.
Platforms can even use AI best to format these push notifications depending on how the user interacts when they are sent, identifying the best formatting and motivational message. This can include things like peer competitive benchmarking or personal goal reminders — whichever is more effective and triggers learner response.
Don’t let your organization fall behind in the rapidly evolving world of employee training. Harness the potential of AI and Machine Learning to unlock new levels of success in your training initiatives. Techinnov’s cutting-edge e-learning development solutions can help boost your company’s training infrastructure by embracing the future of learning with AI and machine learning models and elevating your employee training programs.
Contact Techinnov today and discover how our innovative solutions can revolutionize your organization’s learning and development strategies.
AI, or Artificial Intelligence, is a software quality that empowers programs to perform tasks typically requiring human interaction.
Machine learning (ML) is a subset of AI and uses algorithms and statistical models to learn from historical trends and data to make predictions and trigger actions without explicit programming.
AI and ML is used in e-learning to create personalized learning experiences, provide proactive support, and enhance efficiency for both learners and training managers.
AI and Machine Learning can lead to improved efficiency, cost-effectiveness, and increased ROI for both learners and training managers. These technologies can streamline administrative tasks, reduce time to competency, increase learner satisfaction, and allow teachers and trainers to focus on more strategic-level goals and personalized support when needed.
Developers leverage AI and ML in EdTech for a variety of reasons, aiming to enhance the overall learning experience and create more effective educational environments. This includes personalization, adaptive assessments, early-warning reports, automating administrative tasks, and optimizing user engagement with ML-driven push notifications and prompts.