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How Professionals Are Using AI in eLearning

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The boom in artificial intelligence is touching nearly every industry, and eLearning is no exception. From content creation to instructional design, industry professionals and organizations are finding innovative ways to apply AI in elearning. Whether creating training modules for corporate employees or crafting educational programs, professionals are making AI part of their standard toolkit. 

 

At Teamed, we have the privilege of working and speaking with people from all over the elearning industry including instructional designers, content developers, and organizations considering how to apply it for productivity and the benefit of learners. AI and machine learning come up frequently in these conversations. Here’s what we’ve learned and observed about AI in elearning from experts in our industry. 

 

A Quick History of AI in eLearning 

Before we talk about how people are using AI in eLearning today, let’s take a quick look at how we got here. Around the 1960s, researchers started exploring AI applications across a range of industries, including education and employee training. 

 

By the mid-80s, we had expert systems, AI tools designed to simulate human knowledge about a specific topic. The most commonly known example of this type of AI is the Deep Blue supercomputer. This chess-playing expert system was the first to win a match against a world champion. 

 

Some organizations were also exploring intelligent tutoring systems (ITS). These systems focused on providing individualized instruction and guidance to learners. They typically simulate a human tutor and provide step-by-step feedback, explanations, and personalized hints to help a learner master a specific subject. 

 

Over the next two decades, these tools evolved. They brought us adaptive learning, which provided even more tailored content and personalized instruction. Adaptive learning leverages learner data to make adjustments to the learning experience. Below is a comparison of the two advances. 

Overall, intelligent tutoring systems provide personalized instruction and guidance, simulating human tutoring, while adaptive learning encompasses broader techniques to adapt the learning experience based on learner data and analytics.

ITS and Adaptive Learning Comparison

 

Intelligent Tutoring Systems (ITS)

Adaptive Learning

 

Focus

Provide individualized instruction and guidance to learners. These systems typically simulate a human tutor and provide step-by-step feedback, explanations, and personalized hints to help learners master specific subjects or skills. A broader concept that encompasses various approaches to personalize the learning process.
 

 

Approach

ITS systems often rely on domain-specific knowledge models and expert systems to provide personalized instruction. They use predefined rules and algorithms to assess the learner’s knowledge, identify areas of weakness, and deliver appropriate feedback and guidance. Adaptive learning employs data-driven approaches and machine learning algorithms to adapt the learning experience. It leverages learner data, performance metrics, and analytics to dynamically adjust the content, difficulty level, or learning path based on individual learner profiles.

Interaction

ITS systems typically offer interactive feedback in real time, simulating the interaction between a human tutor and a learner. They can provide detailed explanations, ask questions, and guide learners through problem-solving processes. Adaptive learning systems often focus on adapting the learning material or environment rather than providing detailed feedback like a human tutor. They may adjust the content, pace, or sequencing of learning materials based on the learner’s performance.

Technology

ITS systems often rely on predefined instructional models, rule-based reasoning, and expert systems.   They use a combination of domain knowledge, pedagogical strategies, and predefined rules to guide the learner. Adaptive learning systems leverage data analytics, learner modeling, and machine learning algorithms to dynamically adapt the learning experience. They can analyze learner data, track progress, and make data-driven decisions to optimize learning.

 

 

Introduction of Generative AI 

 

Then, in the early 2020’s enhancements in computing power and digital storage created the perfect environment for more sophisticated AI. These resulted in generative AI tools like ChatGPT and Dall-E, which can create new content based on user inputs. These new models took the world by storm, creating new learning opportunities as well as ethical and legal challenges for eLearning organizations.

 

Generative AI, such as ChatGPT, focuses on generating human-like responses and engaging in dynamic conversations. It aims to simulate natural language interactions and generate coherent and contextually appropriate responses. They do not provide explicit instruction or feedback but aim to engage in dynamic conversations.

 

Generative AI complements previous advancements and supports learning by allowing users to explore their understanding and develop meaning in a way that previous tools did not enable – through conversations, clarification questions, research, and more. ITS and adaptive learning are still essential foundations in creating learning systems that work for individuals – generative AI is another powerful tool that aids in the process of learning. 

 

How eLearning Organizations are using Generative AI

 

As AI tools become more robust and accessible, more eLearning organizations are experimenting with ways to apply them. Based on our conversations with eLearning organizations and their leaders, here are a few ways eLearning organizations are using AI: 

  • Learning products – AI-empowered learning products can make the learning experience more personalized and responsive for learners. For example, many organizations are creating custom chatbots, enabling users to synthesize or level materials, creating on-demand extra practice, more adaptive feedback by analyzing learner responses, and more. These tools may make online learning environments more robust or supplement in-person instruction.
  • Employee productivity – AI tools can make work more efficient for eLearning employees. Generative AI could be especially valuable in speeding up the content development process by drafting lessons, item writing, developing a learning strategy, writing scripts and case studies, or creating facilitator guides and activities, etc.

These are just a few of the ways eLearning organizations are using AI. Some organizations are advancing carefully, mindful of the copyright issues at play and how AI could impact jobs. Others are going all-in with AI, in hopes that this new technology can help ease the hiring market pinch and improve learner outcomes.

How eLearning Professionals are Using Generative AI

 

eLearning professionals are also using AI tools both within and independent of organizations. Many appreciate how generative AI can speed the creation of written course content, graphics, video and even voiceover. In speaking with one developmental editor with over 20 years of experience, she proclaimed, “I really enjoy using AI. It helps me focus on the more advanced aspects of what I do and enjoy. I’ve been able to create mountains of content in a third of the time and cut out the labor intensive pieces such as copy editing and applying style guide standards which AI can do incredibly well.”  

 

Generative AI like Chat GPT and Dall-E can be used to: 

  • Write learning outcomes
  • Create topic outlines
  • Brainstorm ideas and examples
  • Write sections of drafts
  • Revise existing content
  • Create supplemental images
  • Add voiceover to an instructional video

 

Some professionals even use it to get feedback on how well a piece of content answers a question or fits certain standards of quality. Overall, AI is streamlining and simplifying daily work for many eLearning professionals. It supplements their skills in ways that allow them to apply their knowledge and creativity, while spending less time on the mechanics of a task.

 

The Future of AI in eLearning

Like many new technologies, AI has attracted both critics and devotees. Some praise AI’s ability to handle tedious tasks and boost productivity. Others worry that AI will become smarter than humans or fear that reliance on AI could stifle creativity and intelligence.

 

The reality is probably much less extreme. At the moment, language-model based AI tools are only factually accurate about 80% of the time. Image generators often struggle to make human figures and their output is often bizarre if you look too closely. 

 

AI is unlikely to put out of work eLearning professionals like content writers, video editors, and instructional designers. Today, and for the foreseeable future, the human element is still essential. We need human creativity and insight, not just to double-check facts, but to make the learning experience personal and accessible. 

 

For help finding the right humans for their eLearning roles, organizations turn to Teamed. We offer full-service recruitment, a talent marketplace of expertly vetted professionals, and an industry-specific job board to put your open roles in front of the right applicants. Contact us to get started. 

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