AI in Training and Development
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    Artificial Intelligence (AI) is rapidly transforming global businesses across industries and verticals. Training and Development are no exception, and there are several ways AI will intensify Enterprise Learning.

    But first, What is Artificial Intelligence or AI?

    Minsky and McCarthy, who are the founding fathers of Artificial Intelligence, define “AI” as intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality.

    AI broadly comprises Machine Learning & Deep Learning.

    In Machine Learning, a computer program is trained to recognize patterns such as identifying a human face or object. Training the system requires it to be exposed to as many variables for completing a task, using different input data. These computer programs build their algorithms from the data they collect and use it to make correct “decisions.”

    Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data.

    Similarly, the deep learning algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome. We refer to ‘deep learning’ because the neural networks have various (deep) layers that enable learning.

    AI eventually leads to process automation and the reduction of error. AI thus enables enterprises to economize on operational costs and increase revenue.

    In the 2019 CIO survey conducted by Gartner, 37% of organizations have implemented AI in some form. That’s a 270% increase over the last four years.

    For instance, Customer Support has benefited greatly by using AI Chatbots, which nowadays offer personalized customer service round the clock with immense efficiency. The Marketing department has been able to harness the power of AI to generate and nurture leads.

    The power of intuition and prediction makes AI a powerful ally that can be used by Learning and Development teams.

    Let us take a brief look at how AI can improve Training and Development.

    Indexing

    Nowadays, most LMS providers have vast learning libraries that comprise content across different types (video, text, pdf, slides, SCORM) and many authors.

    One of the main functions of an L&D Administrator is to be a content curator. Content curation is a painstaking process of continually finding, filtering, and sharing the most relevant content to the right audience.

    Fortunately, AI can curate relevant content after considering the exceptional skill level, timeline, course, and group that the individual belongs to. For example, Machine Learning can analyze factors such as how many shares the articles received. More specifically, the algorithms can look at how many participants of a network or a team shared the reports, review how many upvoted the item, or commented on the article. In this way, algorithms can help determine if the content is more likely to be relevant and interested to learners. 

    The L&D administrator can use AI to automate repetitive tasks in content filtering and aggregation and instead focus on more impactful activity.

    Personalization of Content

    A modern LMS personalized for an organization builds the company’s brand identity and gives the employees a sufficient learning experience according to their role, skill, and experience level. LMS customization will improve training outcomes and hence employee productivity.

    A personalized LMS platform and a user-friendly UI with an AI-powered recommendation engine closely resemble platforms like Netflix and YouTube. User skills are mapped to job roles, which are in turn linked to relevant content. The AI crawls and understands the required content, thereby suggesting appropriate learning paths, modules, courses and creates suitable assessments.

    The more data the system processes, the more Deep Learning analyses and understands an individual learner’s needs, turning the LMS platform into a continuous improvement engine that grows alongside them.

    The AI algorithm also personalizes the formats that each learner prefers (from YouTube videos to games) and curates the style of the course to mirror the learner’s experience and completion level. These features, coupled with gamification and social learning, truly make for an immersive employee training experience.

    Digital Coaches

    AI-assisted programs like Chatbots are not passive sources of information but can be potentially active assistants.

    The modern employee is mostly constrained for time and hence finds it hard to meet his or her training goals. These bots act like “digital coaches” and help employees stay on track with timely and personalized reminders.

    For example, LMS platforms can use Chatbots during onboarding, in which new hires can ask questions about their team, their role within the team, and how that role is changing. 

    These bots can reach new hires and take their feedback. If there are some challenges, then bots can also assist with suitable solutions to address the same.

    For current employees, the AI bots can provide refreshers on different training pieces, including Safety, Compliance, Human Resources, and Operations. This ensures a high level of workforce engagement and satisfaction.

    Furthermore, these LMS bots also serve as a single point for all learner needs. They can diagnose weaknesses in the learner’s understanding and help bridge any knowledge gaps in real-time.

    In the past, personal assistants and career coaches were reserved only for senior management. With the exponential growth of AI, we could see these learning Chatbots being used across the organization to democratize the training process.

    Analytics & Reporting

    Erik Duval’s Weblog, 30 January 2012, defines learning analytics as follows, “Learning analytics is about collecting traces that learners leave behind and using those traces to improve learning.”

    AI achieves incredible accuracy through deep neural networks, which can assist with micro-level Reporting

    • Learners Activity Report, 
    • Problem Report, 
    • Progress Funnel report, 
    • Cluster Report showing low and high performers in a training program.

    The most significant use of Descriptive Analysis in AI-powered LMS is to track Time and Engagement Metrics, such as the learners’ average number of actions, the progression of users through the training, learner retention metrics, etc.

    Additionally, AI-based data analysis can align LMS training to Employee assessments/appraisals and changing regulations.

    Predictive Analytics is another way of using Learning data to create predictions about future learner progress using data mining, machine learning, and predictive modeling. It enables L&D Managers to study KPI’s such as content consumed, learner progress, and course completion to optimize the budget and thereby maximize ROI. 

    Wrapping Up

    Amazon uses machine learning to recommend products to you based on a user’s purchase history. Apple’s Siri and Microsoft’s Alexa use Artificial Intelligence heavily in natural language generation and Big Data processing. Similarly, AI is not just an add-on in a modern-day LMS. It is the primary engine for an enterprise’s learning strategy, while data acts like the fuel which drives it.

     In summary, AI has numerous ways to enhance Training and Development- ranging from how the course content is customized, consumed, and analyzed.

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