Think about how you typically learn. While textbooks and formal lectures are valuable, there’s a good chance that most of your knowledge comes from informal interactions with friends, colleagues, or mentors. These collaborative relationships are the fuel that help to power conversational learning.
As the poet Henry Wadsworth Longfellow once put it, ‘A single conversation across the table with a wise man is better than ten years mere study of books’.
Why is this? Well, a wise mentor can tailor their advice and guidance to your specific needs. They can also offer you feedback, encouragement, and support, while keeping you accountable. This back-and-forth adds depth and value to the learning experience.
And now, in the age of artificial intelligence (AI), conversational learning has reached new heights. So, let’s set aside the small talk and examine this revolutionary learning approach in depth. We’ll explore its benefits, limitations, and implementation tips.
Ready? Then let’s talk.
What is Conversational Learning?
Conversational learning is what happens when people talk and learn together. The Socratic Method, a dialectical approach to knowledge discovery, is a classic example of this. However, our understanding of conversational learning has evolved over time.
In our new AI-powered world, we’re not solely reliant on human-to-human interaction. Educational conversations with artificially intelligent agents are now a reality. As such, it’s time to redefine what we mean by conversational learning. Here’s our attempt to do that:
Conversational learning is a modern instructional approach that leverages artificial intelligence and natural language processing (NLP) to create personalised and interactive learning experiences.
By simulating a conversation, conversational learning creates a natural and engaging environment for learners. After all, they now have the opportunity to ask questions, seek clarification, get feedback, review recommendations, and more.
An Example of Conversational Learning
To further your understanding, let’s look at an example. Imagine you’re a new employee joining a customer service team. To get started, you’ll need to learn about the company’s products, services, and customer support policies.
Typically, employee onboarding involves reviewing lengthy handbooks or static eLearning modules. However, with a conversational learning approach, the experience is markedly different:
- Onboarding Chatbot: To begin with, you’re paired with a virtual mentor or assistant. By engaging in conversations with this chatbot and asking questions, you can quickly gather essential information about the company, its products, and relevant policies.
- Scenario-Based Learning: Following this introductory phase, the chatbot presents you with a series of real-world customer service scenarios. This provides you with an opportunity to put what you’ve learned into practice in a safe environment.
- Feedback & Coaching: Based on your performance, the AI-powered assistant can identify areas where you need more support. It can then offer tailored coaching, in the form of additional resources or further practice sessions.
- Continuous Reinforcement: Learning should never be a one-off experience. As such, throughout the conversational learning journey the chatbot can reinforce key concepts and provide reminders about important information.
The Technology Behind Conversational Learning
Conversational learning is primarily driven by artificial intelligence. AI is essential for enabling systems to interpret and respond to human language in a natural and engaging manner. This is achieved through natural language processing, alongside the following mechanisms:
- The Data Lake: A data lake serves as a centralised repository for storing large volumes of structured and unstructured data. This includes external information and knowledge bases, alongside historical user conversations, interactions and feedback. Your data lake will expand over time improving your conversational learning output.
- Natural Language Processing: This field of artificial intelligence enables systems to understand, interpret, and respond to human language. NLP algorithms process text or speech input from learners and then generate appropriate responses as an output. These outputs will be based on the information available in your data lake.
- Machine Learning: This branch of AI involves training algorithms on extensive datasets to identify patterns and make predictions. It’s used to train conversational learning agents by feeding them vast amounts of data, enabling them to continuously learn and improve over time.
- Conversational Interface: The interface is the actual chatbot or voice assistant itself. This is how your learners interact with your trained data set through natural language processing. It provides a user-friendly format for learners to ask questions, seek clarification, and receive feedback.
Of course, there are further technicalities to consider. For example, your chatbot solution may use speech recognition software to understand spoken language and text-to-speech technology to generate human-sounding speech from text.
How learners access your conversational interface will depend on your specific needs. For instance, it can be accessed through a standalone website or software application. Alternatively, you can integrate it into your existing learning ecosystem, using platforms like learning management systems or learning apps.
Chatbots vs Conversational AI
At this stage, we should stop to draw a distinction. The terms ‘chatbot’ and ‘conversational AI’ are often used interchangeably, but there is a key distinction that’s worth noting.
Traditional chatbots are programmed to follow specific inputs or keywords. They excel at basic tasks but may struggle with nuances. They also won’t improve over time, unless their programming is manually reviewed and updated.
Conversational AI, on the other hand, is powered by artificial intelligence and uses natural language processing to engage in more complex and natural conversations. This allows it to interpret and respond to a wider range of inputs.
While both solutions are typically delivered through similar interfaces, only one of them uses artificial intelligence to interact and engage with your learners.
Studies of Conversational Learning
AI-powered conversational learning is a relatively new field that’s evolving at a rapid pace. Consequently, there’s a need for more robust data in this area. With that said, existing studies have demonstrated promising outcomes.
Even as early as 2013, a study indicated that tradeoffs in learning performance ‘favour’ the ‘conversational learning approach compared to those obtained from conventional instruction’. And it’s fair to say that conversational learning has made significant strides since then.
So, let’s fast forward. A 2020 study of Chinese college students found that learning with a ‘pedagogical agent’ (or chatbot) was ‘more interesting’ than learning without one. Additionally, the study found that using a pedagogical agent increased mental effort.
Indeed, increased motivation seems to be a common theme in research on this topic. For example, another 2020 study of first-year students demonstrated that those in a chatbot learning group exhibited ‘significantly higher intrinsic motivation’ than their counterparts in the traditional learning group.
Still need more evidence? Studies show that conversational learning can be used to support language acquisition (Belda-Medina, 2022), civic engagement (Schouten, 2022), and mental health awareness (Han Li, 2023).
And there’s more to come. Given the rapid growth of AI-powered learning solutions, you can expect to see new research emerging on a regular basis.
The Benefits of Conversational Learning
Having reviewed the research into conversational learning (alongside personalised learning and adaptive learning), we are now in a position to highlight several key advantages of this approach. For example:
- Increased Engagement: Conversational learning systems are designed to tailor interactions to each individual user. As you might expect this helps to make the experience more relevant and engaging. Furthermore, the ability to receive real-time and focused feedback can also boost motivation and understanding.
- Improved Flexibility: Your learners are busy individuals. Fortunately, conversational learning experiences can be accessed at their convenience. Learners can even ask the conversational agent to adjust content delivery in line with their schedule and availability.
- Cost-Effectiveness: Another key advantage is the scalability of conversational learning systems. This means you can easily accommodate large numbers of learners. Traditionally, coaching at scale has been prohibitively expensive, but conversational learning offers a cost-effective solution.
- Scalable Support: It’s rare that learners receive this sort of personalised support. Indeed, conversational agents can serve as more than just tools for delivering learning content. They can also act as virtual mentors, offering tailored guidance and support throughout a learner’s career journey.
- Better Outcomes: As we’ve seen from the studies, the interactive and personalised nature of conversational learning often leads to enhanced learning outcomes and business impact. In particular, conversational learning is well-suited for fostering the development of critical thinking and problem-solving skills.
What Are The Limitations of Conversational Learning?
As we’ve seen, conversational learning offers a range of different benefits, both for learners and for organisations. With that said, it’s not a one-size-fits-all solution to your learning challenges. It’s also important to acknowledge the following limitations:
- Emotional Engagement: As we know, conversational learning agents, or chatbots, are powered by artificial intelligence. As such, they may lack the emotional intelligence and empathy of human instructors. On occasion, they may also struggle to interpret social cues and nuances in human communication.
- Depth of Learning: Conversational learning excels in a variety of contexts. However, it may be less suitable for complex topics or subjects that require hands-on experience. For example, learning how to operate heavy machinery would benefit from an experiential learning approach.
- Ethical Constraints: Like any AI-powered solution, the effectiveness of conversational learning largely depends on the quality of its training data. As such, conversational learning agents may inadvertently perpetuate biases. You’ll also need to safeguard the privacy and security of user data.
- Hallucinations: According to The New York Times, even the most advanced large language models ‘make things up’ approximately 3% of the time. This is a phenomenon known as ‘hallucinations’. While this percentage may see low, presenting any incorrect information within a learning programme can have negative consequences.
- Technological Issues: As an AI-powered solution, conversational learning may be limited by factors such as internet connectivity and device availability. Considering the global digital divide, with approximately 3.7 billion people currently unconnected, this is a crucial factor to keep in mind.
Despite these limitations, conversational learning undoubtedly has a role to play in our future. While no one learning approach is perfect, the sky’s the limit for conversational learning as machine learning algorithms continue to break new ground.
How to Implement Conversational Learning
If you’ve reached this point in the article, then you’re likely eager to engage in your own conversational learning experiences. So, the next logical question is: how can organisations integrate this learning approach into their L&D strategies? Here are some tips to get started.
Pre-Launch:
- Identify Your Objectives: As with any other learning approach, you should start by clearly outlining the desired outcomes for your conversational learning programme. You’ll want to ensure that your learning objectives are easy to understand and are aligned with your organisation’s overarching strategy.
- Select Your Platform: Next up, you’ll need to select the right conversational learning platform for your organisation. To narrow down your choice, examine factors such as functionality, cost, scalability, and integration capabilities. Here are some AI-powered learning tools for you to consider.
- Fill Your Data Lake: To ensure your conversational learning experiences are rewarding, you’ll need to provide your agents with training data. Whilst this data will grow over time (through user conversations and interactions), you can also incorporate external knowledge bases to enhance their capabilities.
- Agent Configuration: With your data lake established, you can now focus your efforts on your conversational agents or chatbots. The right learning platform should empower you to customise their personality, instructional approach, and overall interaction parameters.
- Train Employees & Admins: Before you launch your programme, ensure that everyone is familiar and comfortable with the conversational learning approach. Provide in-depth training for your administrators and offer your learners a simple walkthrough or tutorial so they can hit the ground running.
- Launch a Pilot: We recommend starting with a small-scale pilot programme to evaluate the effectiveness of your conversational learning solution. Select a manageable group of users who can provide you with meaningful data and feedback upon completion of the trial experience.
Post-Launch:
- Measure & Evaluate: Set key performance indicators (KPIs) to track the success of your conversational learning programme. You’ll want to monitor learner engagement, completion rates, and knowledge retention. This data will help you to make ongoing improvements to your approach.
- Refine & Update: By following these steps, you’ll have successfully implemented a conversational learning programme within your organisation. Additionally, you’ll have gathered valuable data to refine your approach for a full-scale rollout. At this point, the real excitement begins!
The Future of Conversational Learning
Conversational learning is already an exciting field and it’s developing at pace. As a result, you can expect some exciting new developments in the not too distant future. One of the things that excites us most, is the potential for many-to-one learning experiences.
Traditional learning experiences are often one-to-many, with a single teacher or instructor delivering learning material. Conversational AI, however, makes it easy and affordable to deploy multiple agents specialising in different topics or areas.
This means each learner can benefit from multiple instructors, receiving a level of individualised attention that was previously unheard of.
And there’s a lot more to come. Expect customisable avatars to be integrated into conversational learning interfaces in order to create more immersive and engaging experiences. This will also help to bridge the emotional gap often encountered in AI interactions.
Finally, there’s the question of how conversational AI will utilise the data collected from user conversations and interactions. We envision this information being used to drive ‘organisational intelligence’ by identifying skill gaps, relevant trends, and opportunities for process optimisation.
Final Words
Like any meaningful conversation, conversational learning deepens and becomes more rewarding with continued engagement. Indeed, as machine learning algorithms advance, we anticipate enhanced emotional connections and improved learning outcomes.
With that said, conversational learning already has an important role to play in modern learning programmes. After all, this approach foregrounds interaction, collaboration, and a high degree of personalisation. What’s not to love?
Implementing conversational learning within your business may pose challenges due to its relative novelty. Fortunately, there are a range of tools and options available to support your efforts. With the right approach, transformative learning is only ever a prompt away.
So, where will your next conversation take you?
Thank you for reading. Conversational learning is one of many valuable learning approaches. To get the full breakdown and explore 165 top tips, download our ‘L&D Professional’s Handbook’ now.
