March 14th, 2023 / By: Ruben Araiza / Published in: Blog
This post was originally published on this site
We live in a world where everything personal is for sale. Look at your social media feeds. The posts on your Facebook or Instagram timeline are personalized based on an algorithm that tracks your activities and identifies what engages you the most. As technology advances, almost every aspect of human life becomes more personalized.
We have become accustomed to receiving the information we need in the preferred format. Organizations have acknowledged this change in people’s psyches. In their efforts to attract and retain talent, organizations are rethinking their one-size-fits-all training approach and personalizing learning pathways based on an employee’s roles, responsibilities, and aspirations, with a clear focus on their individual preferences, goals, unique needs, and long-term growth plan. This enables an efficient and effective learning experience, increased motivation, and better learning outcomes. Achieving this level of personalization is possible only by using Artificial Intelligence (AI).
The Accenture report predicts that businesses using AI effectively may see a 40% increase in profitability by 2035. Furthermore, according to Research and Markets, the global AI software market is projected to reach $190.61 billion by 2025.
According to Stanford Researcher, John McCarthy, “Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”
In simple terms, AI aims to create programs that can mimic human intelligence. It enables true human-machine interaction, enabling machines to think, learn, and understand commands, make connections and decisions, use logic, monitor the environment, and make plans as humans do.
Machine Learning (ML), a subset of AI, uses algorithms and models to analyze data and make predictions or decisions without explicit programming. ML can identify data patterns and make predictions when there is new data. Netflix is a fine example of ML where it identifies the patterns of user search and online engagement behavior to recommend similar content.
Deep Learning (DL), on the other hand, is a specialized subset of ML, with more complex and sophisticated algorithms. It uses artificial neural networks with multiple layers that require less human input. However, it also needs larger datasets, often in the millions, to function effectively and to make precise interpretations due to its complex structure. Google’s AlphaGo is an example of DL. It is a computer program with a neural network that plays the abstract board game Go, learning through playing against professionals.
AI in Learning and Development
AI enables us to personalize learning and development based on a learner’s learning preferences and style. The following are some examples of how AI can be used in corporate learning and development:
- Learning Experience Platforms: Organizations are replacing traditional Learning Management Systems (LMSs) with Learning Experience Platforms (LXPs) to adapt to the digital, remote, and mobile work environment. LXPs use AI and data analytics to gather data and curate personalized learning content. It is employee-driven and promotes upskilling and reskilling by offering relevant learning content and giving them unrestricted learning options. The algorithms can analyze data on employee performance and predict areas for improvement. It can provide highly immersive learning experiences based on the analysis of job performance, skill gaps, and on-the-job competencies required of the employee.
- Intelligent Search and Auto-Tagging: An AI-powered learning platform can analyze new content, create tags for easier search, and facilitate intelligent search that accesses relevant content across an organization’s federated data silos, connects to peer communities, suggests experts for learning, and personalizes learning journeys. As more content is added, the AI system will improve at identifying learners who will find the content most useful, resulting in different search results for different employees.
Imagine a large organization that has a vast library of training videos and resources, covering a wide range of topics. With intelligent or deep search and auto-tagging, the organization can use AI algorithms to analyze all its training content and automatically label each piece of content with relevant tags. Employees can then use the deep search function to quickly find exactly what they’re looking for, even if they don’t know the exact title or keyword.
- Virtual Coaches/Chatbots: AI-powered virtual coaches/chatbots act as personal learning guides, recommending content, monitoring progress, answering questions, and sending notifications within a learning platform. Chatbots, like ChatGPT, respond intelligently to user input by using advanced natural language processing. They can assist employees at any time of the day, offer solutions, track progress, recommend courses, handle administrative tasks, provide real-time feedback for increased productivity, and offer a natural language interface for interacting with the LMS.
If an organization is launching a new initiative to upskill employees in the latest software development technologies, the comprehensive curriculum will cover programming languages, frameworks, best practices, etc. To support this initiative, the organization could leverage chatbots to facilitate access to information, onboarding, just-in-time training, self-assessment, and personalized recommendations.
- AI-based Content Generation: AI algorithms can analyze existing learning materials, such as videos and books, to identify key concepts and generate new content that is customized to the learners. They can create and deliver short, focused learning experiences that are designed to meet the needs of employees in real time. AI-powered microlearning can be used to provide employees with quick and effective ways to learn by transcribing and adding timestamps to video lectures, improving learning outcomes, and speeding up training for busy workplaces.
- Automated Assessment: AI can be used for creating automated assessments that can utilize ML algorithms to analyze and grade the assignments, quizzes, and exams submitted by employees. For example, in an online program for customer service representatives, the AI system could grade the responses of learners in a simulated customer interaction, considering factors such as tone, empathy, and problem-solving skills. This would provide learners with immediate feedback and enable supervisors or facilitators to identify areas where the learner may need additional support.
Overall, AI can enhance the effectiveness and efficiency of learning and development by providing a more personalized and data-driven approach to employee development.
Challenges in Implementation
Although there are many benefits to AI-based learning solutions, there are a few challenges as well:
- Data Security: Artificial Intelligence & Machine Learning (AI/ML) models rely on the availability of data and resources for training. Yet, data generated by millions of users globally can be used for malicious purposes, such as data breaches of personal information.
- Infrastructure: Replacing old infrastructure is a major challenge for organizations. AI solutions require high computational speed, so robust infrastructure and high-end processors are necessary.
- AI Integration into Existing Systems: Integrating AI into existing business systems is a common challenge. Businesses need to seek help from AI solution providers with expertise and experience to implement AI in their systems.
- Expenses: Another challenge of implementing AI in the workplace is the cost. Organizations should weigh in on the cost-benefit analysis and ensure that the benefits outweigh the costs before implementing AI.
- Niche skillset: Lack of proper AI skillset and expertise is a challenge for AI implementation and deployment. To overcome this, companies may need to consider investing in AI app development training, hiring AI talent, or buying capabilities from larger Information Technology (IT) companies.
Future in Learning and Development
Technology is rapidly transforming the training industry through advancements in AI/ML. In the future, AI-powered training will incorporate more 3D and Virtual & Augmented Reality (VR/AR) to create immersive and interactive learning experiences to upskill and reskill employees. The use of these technologies has unlocked a new era of opportunities for talent development.
AI can ensure that the employees are more focused by developing a personalized experience to better hold their interest and make them feel more connected to their workplace. It will also be used more frequently as virtual mentors in the coming years, using experiential learning to improve employee comprehension and retention and create the ideal learning experience to meet business and performance objectives. The next generation of LMS will bring rule-based engines and data analytics together for a learning experience that is intuitive and enjoyable. Through techniques like microlearning, gamification, and adaptive microlearning, AI will enable a variety of learning styles, such as problem-based learning, group-based learning, and blended learning.
With the rapid advancement and incorporation of technology in various fields, most of the current workforce skills will become redundant in the next 5 years. According to the World Economic Forum (WEF) report, for the workers who stay in their roles, the share of core skills that will change by 2025 is 40%, and 50% of all employees will need reskilling.
This is where AI can be leveraged to augment an individual’s knowledge and skills development. For this, effective change management is crucial to ensure employee adoption of AI-enabled LMS/LXP. This will not only save time by streamlining an individual’s learning but will also improve efficiency by enabling learners to focus on their improvement areas, resulting in better post-training performance.
DL can support an organization’s AI-based learning initiatives by personalizing learning experiences, improving content generation, automating assessment, making predictive analytics, and analyzing text data for insights.
There is no denying that AI has become an essential part of our lives and has the potential to revolutionize the Digital Learning industry and change the learning landscape for the better. Reach out to us today so we can help you design and develop AI-driven personalized learning pathways for your skilling initiatives.
Fast-growing tech companies partner with Encora to outsource product development and drive growth. Contact us to learn more about our software engineering capabilities.