Artificial intelligence (AI) has become a powerful tool for enhancing digital learning, but it also has its limitations. How can you overcome these challenges and make the most of AI in your online courses? Here are some tips and strategies to help you.
AI can do many things, such as personalizing learning paths, providing feedback, generating content, and analyzing data. But it cannot replace human interaction, creativity, or critical thinking. AI is not a magic solution, but a tool that needs to be used wisely and ethically. You need to understand what AI can and cannot do, and how it works, to avoid unrealistic expectations, biases, or errors.
A good starting point is to consider where can AI enhance the advantages of digital learning, and where can it help overcome some of the challenges in digital learning. Digital learning allows each learner to choose their learning path, the pace, and types of activities that best fit their goals and learning styles. AI can take this further, for example by adapting the learning activities, levels, and types of exercises to the identified learning patterns of the individual. Based on data, it can offer insights and observations to the learners about things the excel in or struggle with, and offer relevant strategies. Other applications of AI could help with the challenge of keeping content up to date, while instructors focus on interactions.
AI is amazing and terrifying, all at the same time. With many people using social media as their source for news and information, it is vital that we verify our understanding. For you to be able to truly appreciate AI and how it might fit into your learning creation, you must know what the tools really are, and what their capabilities really are. Just reading a commentary about someone's opinion does not qualify. Yes, this takes work on your part, and it takes real effort to distinguish between "really cool" and "really valuable and "a real waste of time". If AI is something you want to bring into your content creation and learning journey, then take a few hours every week. Explore, experiment, and push your own envelope.
There are many AI tools available for digital learning, but not all of them are suitable for your learning objectives, audience, or content. You need to evaluate the quality, reliability, and relevance of the AI tools you use, and how they align with your pedagogical approach and learning outcomes. You also need to consider the costs, benefits, and risks of using AI, and how to balance them.
One consideration people often neglect to consider is the complexity of developing and maintaining AI applications. On the seemingly faster and simpler end of the scale, you could embedded existing AI tools with APIs that become more and more abundant and powerful, and in many cases can even add context and content that the AI tool should use as reference without any development effort. On the other end of the scale is developing propriety AI applications, custom made to your data and powered by your own trained machine-learning models. These to extremes, and any other options you may choose between them, will have a big impact on your strategy, benefits, costs, and exposure to risks.
A great starting point is digital literacy. By demonstrating that digital literacy is grounded in using the technology we have to become better individuals and by extension a better society, we empower students to take initiative and learn how AI works and the various things it can accomplish. Essentially, how AI can augment digital learning for the better. Once this is achieved, then it is easier to navigate the various AI tools based on the learning goal. Once the right tools are selected, learning becomes more focused and the goal is easier to achieve.
AI can enhance learner engagement, motivation, and autonomy, but only if learners are aware of how it works, why it is used, and how it affects them. You need to inform learners about the AI tools you use, how they collect and use their data, and what choices and rights they have. You also need to involve learners in the AI process, by allowing them to customize their learning preferences, give feedback, or co-create content.
Transparency is key when implementing AI in your digital learning tools, and something that should not be hidden or hard to make sense of in some legal lingo. Your learner should have a very clear idea of what you are doing with their data, have simple controls that let them turn features that impact them on and off, and the ability to provide feedback on how it is working out for them. If you are using learners to contribute content, you should also clarify the question of ownership and possibly build a model that recognizes their contribution. I see room for new models being developed here in the future, maybe along the lines of Open Source movements.
AI can provide automated and adaptive support, but it cannot replace the social and emotional aspects of learning. You need to complement AI with human interaction, by creating opportunities for learners to communicate, collaborate, and connect with you and their peers. You also need to provide human feedback, guidance, and mentoring, to supplement the AI feedback, and to address any issues or concerns that learners may have.
AI can generate a lot of data and insights, but you need to monitor and evaluate its impact on your learning outcomes, learner satisfaction, and ethical implications. You need to use data analytics and assessment tools to measure and improve the effectiveness and efficiency of your AI tools, and to identify any gaps or problems. You also need to ensure that your AI tools are transparent, fair, and accountable, and that they respect the privacy and security of your learners.
When monitoring AI applications, there are specific thing you need to pay attention to, in addition to the normal things you would monitor in any application. AI tools are based on machine learning models, which are in turned, trained on certain data. Making sure that data is not biased, monitoring how you may need to adjust it over time, and continuing to evaluate how it influences learners, learning paths, and may unintentionally "favor" certain types of content, language, or role models, is something we all need to build awareness and knowledge on with regards to AI tools use in digital learning solutions.
AI is constantly evolving and improving, and so should you. You need to keep learning and improving your skills and knowledge about AI, and how to use it for digital learning. You need to stay updated on the latest trends, research, and best practices, and to experiment with new tools and methods. You also need to seek feedback and input from your learners, peers, and experts, and to share your experiences and challenges.
While some AI applications have been in use for decades now, other forms and models are only at a very early stage. New discoveries and tools are announced weekly. I believe that definitely at this stage of the game, you should not try and build the perfect or final version of any tool using AI in digital learning. You should frame your efforts as experiments that allow you to discover what's working and what's not, build you team's skills, knowledge, and confidence when implementing AI as part of the digital learning tools, and establish an understanding of how does the AI complement and enhance the human elements in your solution.