#Totara TXP

Can artificial intelligence provide solutions to elearning challenges?

15 de December de 2021
6 min


By Lidia Llera, Sales Executive of Actua Solutions

From a practical point of view, the mission of training is none other than to help us embrace the world around us and learn from it as quickly and as well as possible. Precisely, the objective of Artificial Intelligence (AI or AI) is to automate and accelerate a task as human and complex as obtaining information, processing it, reasoning about it and providing a solution to a problem. Based on these premises, AI and other associated technologies such as Big Data or Machine Learning are presented as great allies and will still have much to contribute to the training environments of companies in the challenge of processing tons of information in an efficient and relevant way.

But how does it translate into practice?

As a user travels through our LMS/LXP (such as Totara) we can measure their participation and performance based on different strategies: according to the decisions they make (navigation, access, etc.), according to evaluation results (tests, activities, surveys, etc.) or according to their interactions and contributions in informal learning environments. Moreover, the analysis can be carried out both individually and taking into account the behavior of a group of users with certain characteristics.

Accessing and segmenting data will allow us to predict behavior and design a more personalized experience. In other words, the system can be able to offer didactic content adapted to each learning style and automatically reinforce those aspects that are more complicated or require greater effort.

Suppose that, as users, we are faced with a task about which we need context information. We conducted a search on the subject on the corporate training platform. The system “knows” that we have a greater tendency to consume podcasts, over those reading resources. Let’s suppose more: the search engine “remembers”, even better than we do, the results of our evaluation. He knows the catalog perfectly and the exact spot on the virtual shelf to search. He is aware of the consultation needs of other users whom we barely put a face to and with whom we have never discussed the subject. Two clicks and we have exactly what we need to move forward. The interpretation and application of this information is up to us.

The IA can collect data on each user’s behavior, identify progress and challenges, and generate suggested content, additional material, or alarms for the expert/tutor/manager should they require your direct attention. Designing differentiated itineraries, analyzing the profile and performance of students, offering evaluations and exercises adapted to their difficulties; evaluating and accrediting effective learning by determining which are the most valuable and even generating new resources. Well-prepared AI systems can help extract valuable information and turn it into intelligent content for digital learning.

Let’s assume again that, now as administrators of a training environment, we can have access to much more objective and thorough feedback than would be the case, for example, in a face-to-face classroom. The analysis of interactions in an automated and unbiased way will make it possible to detect weaknesses: problems in the course delivery process resulting from errors in the didactic or technical design (unusual repetition of failures in an evaluation, long reading times on certain screens…) that will make it possible to identify needs for improvements in the didactic material or the LMS. The interpretation and analysis of the causes and possible solutions to these problems will depend on our human intelligence and will not be a magical attribute that we can grant to AI.

We have become accustomed to relying on web search engines and social networks to find the answers. AI can help by providing filters that provide recommendations and selection of content based on tagging and metadata. The collection and processing of data external to the courses, based on the queries made outside an LMS, makes it possible to enrich and complement the contents in a coherent and useful way for day-to-day use. Of course, this data can be collected and combined with each other to analyze learning in a holistic manner.

What about Chatbots?

One of the latest trends is the use of Chatbots to help the learner to filter and/or discover content or as evaluators by posing questions in the form of questionnaires to be carried out in a flexible and agile way. The bots use the data they collect from student behavior to respond instantly. An artificial intelligence-based online learning LMS platform (such as Totara) can act as a teacher and answer questions in real time. We are talking about learning by repetition: the more interactions, the more information; the more information, the better and more relevant results.

Another key to the success of virtual assistants lies in natural language processing: the more efficient their interpretation is, the more it facilitates communication and results in a more satisfying learning experience.

Can we trust elearning to Artificial Intelligence?

AI is a complement to knowledge management and not a substitute. The role of algorithms is to make learning more accurate and efficient. But for them to work properly they have to be based on pedagogical models that take into account ergonomics, values, skills and knowledge. Skills such as creativity, intuition, adaptability and innovation remain indispensable for success and, for the time being, impossible to predict and quantify. Neither Big Data nor Machine Learning will be able to address Instructional Design issues. They will not be able to evaluate the content other than on the basis of consumption. Success depends on talented, analytical, flexible, creative and motivated users at both ends of the LMS/LXP, who define their learning objectives based on meaningful values and competencies. Artificial intelligence needs to be nurtured by real intelligence.