The term “artificial intelligence” is applied when a machine mimics “cognitive” functions that are associated with the human mind, such as “learning” and “problem solving.
Artificial intelligence was founded as an academic discipline in 1956, and in the years since, has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an “AI winter”), followed by new approaches, success and renewed funding.
Major technologies used in AI
Text analysis
Text Analysis, what you might call Natural Language Processing, is an important technology to understand a customer’s intent. Its major application area is providing personalized ad offers/discounts, and finding relevant content with respect to search queries.
Natural Language Generation
It involves creating human-readable text out of computer data. This technology is mainly used in customer support, report generation, business intelligence, and delivering point-of-need smart alerts.
Speech Recognition
Speech Recognition is a technique which transcribes human speech into a machine-readable format so that it can be used across a wide range of computer applications. Its major applications are interactive voice response systems and mobile applications.
Computer Vision and Image Recognition Algorithms
This includes analysing picture content to find someone in particular, check for movements in video or analyse posture. Some high-end applications of this technology encompass Self-Driven Cars, Smarter Security Cams for Video Surveillance, or even a laser-firing system for exterminating mosquitos.
Virtual Agents
One hears of Amazon’s Echo, Google Assistant, and Microsoft’s Cortana, that have the capability to network with humans. These are currently being used in customer service and support and also as a smart home manager to track household essentials.
The Future of AI in Training and eLearning
Probably a significant effect of AI systems and machine learning would be felt in Training and eLearning industries, particularly those into LMS space. With all the tools mentioned above, AI, in the future, will help us to focus solely on eLearning content creation, without having to take care of the tedious tasks of reviewing charts and statistics to detect hidden patterns. It will also provide the learners immediate personalized eLearning feedback to steer online learners in the right direction, without any human interventions. Other benefits include:
Personalized eLearning Content
An online learner’s history might reveal that they prefer tactile eLearning activities. Thus, the system would automatically adjust its eLearning course map to include more complex and advanced games and eLearning simulations that are kinaesthetic by nature. Likewise, online learners who exhibit a particular skill gap would receive targeted recommendations in real time that build the required talents and abilities. Also, for more advanced learners, it can skip several eLearning modules, or, otherwise, recommend to start at the basics.
Better Resource Allocation
AI driven online learners would receive just the right level of resources, that too rapidly, to achieve their learning goals, thus resulting in less seat time and training payroll hours. Another benefit is better resource allocation for the L&D team which would now be able to spend more time developing powerful eLearning content, rather than analysing graphs and LMS metrics. Also, based on the learners’ specific skills or past experiences, the system would better match mentors to online learners for maximum benefit.
Automated Scheduling and Content Delivery Process
Artificial Intelligence (AI) may be able to take over the coursework scheduling for online learners or delivering online resources based on their eLearning assessment results or simulation performance in the near future, making it possible to automatically generate unique eLearning course maps for every online learner who enrols in your eLearning course.
Improve eLearning ROI
Less online training time, greater personalization and the power to deploy your online training resources where and when they’re required, results into a broader profit margin.
Improve Learner Motivation
Since online learners receive an individualized experience instead of a generic (and often irrelevant) eLearning course, and that too by dedicating less time, they are more likely to stay motivated to engage with the eLearning content and reach their true potential. Also, they are able to progress at their own pace and participate in only those eLearning activities that resonate with them.
AIaaS and eLearning
The advent of AIaaS or “AI as a Service” allows eLearning developers to purchase or license algorithms and AI tools and components, thereby saving time and expense involved in developing their own.
These solutions offer benefits, such as the ability to add “standard” AI tasks to your toolbox. A standard AI task is one that is a part of many well-researched and frequently used solutions. This could be something like identifying specific objects in photos (dogs, for example) or certain logic and decision-making tasks that use similar processes.
Many AIaaS products are cloud-based, potentially requiring that developers—and possibly learners—be able to access the cloud whenever they use the tools. Several well-known tech giants, like Microsoft, IBM’s Watson, Google’s TensorFlow and Amazon Web Services, offer AIaaS tools and platforms.
With advancements in AI learning models, future learning systems can and will be able to create an interactive environment for each student. That will increase the chances of students completing the course exponentially, and will also ensure that the student gets a better learning experience.
Author: Prachi Pant
