Eye on Design: Gamification AI
Distance learning is full of technologies and tools that can detect plagiarism, transcription technology, tools to check grammar and spelling, mind mapping tools, assessment scoring trackers, rapid design platforms and so many more. These tools are time savers and help immensely in allowing instructional designers the ability to provide learning events that are available in asynchronous and synchronous virtual environments.
There will probably come a time when all of these tools could be navigated automatically with AI. This will not replace instructional designers, but it will probably impact how instructional designers approach some of their design strategies. Impactful learning events are often catered and personalized to a specific learning audience. We are still a ways off from all of this becoming the norm, but we’re also close enough to start exploring and educating ourselves on the different ways we can leverage artificial intelligence for instructional design.
So what is AI? Artificial Intelligence or AI is the term used for creating computer systems that can perform tasks that typically require human intelligence, such as decision-making or problem-solving. Generative AI is a subset of AI that focuses on creating models that generate new content. This may include text, images, or music using neural networks or deep learning. In other words, generative AI is a specialized area within AI focused on generating creative content.
And what is gamification? Gamification is the application of game mechanics (elements added to a system of play; e.g., board game pieces in play during a board game) to a non-game learning event. Not to be confused with Game-based learning (using a game to drive the learning). Gamification AI is the integration of artificial intelligence technologies with gamification strategies.
Gamification AI provides multiple advantages when compared to traditional gamification. Here are some examples.
Data-Driven Insights – gamification AI can be used to gather a lot of data around user interactions. This data can highlight trends and findings around user preferences and behaviors. This can be helpful when looking to refine your gamification strategies.
Predictive Analytics, Real-time Adaptability and Personalization – AI can leverage predictive analytics to forecast user behavior and anticipate needs. This allows for gamification systems to offer challenges, rewards, and or content that will resonate with learners proactively.
Continuous Improvement – AI can iterate and improve gamification elements by leveraging the learnings collected from learner interactions and feedback.
Enhanced User Experience - This learning environment can be more immersive for the learner. Using chatbots, virtual assistants, or dynamic challenges that can respond to user inputs, makes the learning experience feel a bit more lifelike.
Want to learn more about gamification AI and how to leverage it for your own design initiatives? Check out the Gamification AI Design Planner template and course.