Online learning was in any case evolving at a rapid pace when it received a major push by the recent coronavirus pandemic. As country after country imposed restrictions and lockdowns to contain the virus, work-from-home and elearning became everyone’s favourite buzzwords overnight. However, elearning is not without its set of challenges, and schools, colleges, and organisations ran into a few roadblocks. For example, teachers and students alike have had a hard time migrating to a tech-based elearning environment. Teachers and trainers have also found it challenging to engage the interest of learners, sustain motivation, detect plagiarism in assignments, and track performance.
But one of the biggest challenges they faced was teaching based on each learner’s skills, ability, and existing knowledge, which meant that learners who could not keep up with the pace of the class were left behind.
While technology is not a panacea for all learning challenges, artificial intelligence (AI) and machine learning (ML) can significantly improve learning outcomes with personalised learning. Let’s take a look at how an AI- and ML-driven approach can benefit training and development for all learner types – employees (new and existing), accountants, teachers, expats, sales teams, managers, college students and more.
- Individualised Learning: A modern workforce or classroom is made of up learners from different backgrounds with unique interests, skillsets, knowledge, and learning needs, making it important for the instruction to be as diverse as they are. It is impossible for instructors to tailor their teaching as per every individual, but with a little help from AI, learners have the freedom to set their own pace. Learners can blaze through topics they are proficient in and immerse themselves in topics they aren’t familiar with, without experiencing any shame or fear associated with holding up the other learners. As this approach gives learners a sense of autonomy, it empowers them and piques their interest, which more often than not leads to better learning outcomes.
- Adaptive Learning: A learning management system that leverages AI and ML can tailor content based on the existing knowledge of each learner rather than provide a one-size-fits-all approach. Adaptive learning is a data-driven approach, which constitutes continually tracking each learner’s performance, using ML algorithms to predict outcomes, and modifying the material to reflect the knowledge of that particular learner. So, if an employee’s learning profile mentions proficiency in communication, the learning management system (LMS) will not allocate modules related to that skillset to cut down on training time. And if a learner is attempting to master a new module, the system will keep altering the material and difficulty levels based on their progress, until they have grasped the concept. Paving a custom learning path for each learner with relevant topics not only enhances time efficiency but also boosts their motivation levels.
- Advanced Analytics: When it comes to grading tests or assessing an employee’s knowledge, teachers and trainers have obvious limitations – it is time-consuming, and the results don’t really offer any insights. On the other hand, an AI-based system can quickly analyse humongous amounts of data as well as highlight patterns and trends. Instructors receive information about each student’s performance, strengths, problematic areas, and even issues related to attendance. This makes it easier for instructors to decide on a course of action and intervene before the learner loses the confidence to continue the course or just drops out. Advanced analytics can also be used to determine the effectiveness of the course material and identify any gaps in the content.
- Immediate Feedback: Feedback is a critical component of learning, but if the feedback is too generic or critical, it can have a detrimental effect on learners. In an elearning environment that uses AI, students receive immediate feedback during the learning process, which transforms passive learning into an active learning experience. There is no perceived criticism as the learner receives targeted feedback privately and is given a chance to strengthen their skills and remedy their errors in a stress-free manner. Making learners aware of their performance with real-time feedback helps them understand exactly where they’re going wrong and accordingly adjust their approach.
- Real-time Q&A: AI-powered chatbots are increasingly being used in online learning to immediately address any problems or queries learners may have. So, instead of worrying about interrupting a training session with a “stupid” question, learners can directly ask an intelligent chatbot and receive a real-time response. This helps learners stay engaged even while learning advanced material, as they have the freedom to keep asking questions at every step. And unlike human instructors, these virtual tutors will never get tired of fielding the same queries, boosting the confidence of learners and cementing their knowledge.
By leveraging the power of AI and ML, organisations can achieve personalised learning at scale to save the time of both trainers and employees, allowing them to focus on more mission-critical tasks. Allotting resources to only fill gaps also drastically reduces training payroll hours, saving firms money without compromising on learning outcomes. And thanks to the benefits we discussed above, such as individualised learning, adaptive learning, and real-time feedback, employees are more engaged and keener on learning, which translates to better outcomes and improved productivity.
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