The rapid development of computer technologies has changed the world as we know it in just half a century, but progress doesn’t stop here. Computer technology is still developing at a dizzying pace. Gamification and AI have been around for a while, but the opportunity of using these two technologies is just around the corner.
AI-powered gamification can be used to predict important business outcomes and answer strategic questions. Here’s how.
What is Gamification?
Gamification is the process of adding game elements to concepts that don’t have a gaming context. The main reason why gamification is so valuable is that it boosts user engagement. It makes tasks that people usually consider challenging more fun and, therefore, easier.
However, gamification can also be used as a set of rules and systems for solving problems through gaming principles. It isn’t anything new – it’s just that we failed to put a name to this powerful tool until recently. For example, it’s been used for centuries in education by giving students various points and trinkets to showcase progress.
For example, Boy Scouts have always included badges to show the achievements of their members. They also use badges to distinguish members who excel in certain activities. In the same vein, gamification uses gaming mechanics to encourage people to act in a certain way.
Gamification Concepts
We can use hundreds of game concepts that can be used for gamification. Here are some of the more prominent ones to give you a general idea about how they work:
Badges: players get badges when they achieve a particular goal or get a desired number of points. These badges represent the achievement made by players and reinforce positive behavior.
Game Points: points represent basic measurement units in gamification. For example, each connection on LinkedIn counts as a point.
Scoreboards: scoreboards or leaderboards bring together badges and points. Players see how they are ranked against each other and get the motivation to do better.
What is AI?
Artificial Intelligence is a buzzword that is closely connected to gamification. Artificial Intelligence is a computer science branch that focuses on developing programs and machines that display human-like capabilities. Popular AI can be defined as computer programs that learn from experience, through repetition, by recognizing objects and patterns, understanding language, and making decisions based on those observations to solve problems.
Even though we are far from AI that is identical to human intelligence, these solutions have become a part of our everyday lives. For example, AI development has made it possible to gather and process data accurately and much faster than humans.
Machine Learning
There are many different types of AI, but ML is the most developed and used subset of AI. A Machine Learning model is a set of algorithms focused on a single task. These algorithms are fed with structured data that they use to perform the desired task.
These programs learn from this data through repetition and make countless mistakes. After some time has passed, the program learns how to get to the desired output, and that’s when the testing phase ends.
AI in Games
Since we are talking about the correlation between AI and gamification, let’s go straight to the source – video games. More specifically, we’ll talk about the example of “OpenAI,” an artificial intelligence program designed for the strategy game called Dota 2.
Yes, we’ve seen examples of computer programs defeating chess world champions. However, this type of game doesn’t have many variables and complexities that would showcase AI’s real power.
In Dota 2, OpenAI takes the role of a player in the game. This general-purpose machine learning program has played over 10,000 games against itself to learn about the Dota 2 game. Through this repetition, the program has learned to play the game with all its game mechanics, including different heroes, experience, levels, talents, spells, items, map mechanics, movements, etc.
After just 10,000 hours of playing, the program has learned so much about the game that it was able to beat the best Dota 2 professional players in 1v1 matches. Bear in mind that Dota 2 is a highly complicated strategy game with an incredible amount of variables. Yet, this machine learning system was able to use its mechanics better than humans do.
Using Gamification to Improve AI or The Other Way Around?
AI and gamification have become popular in business. Companies use gamification in many different processes to improve results and keep people engaged. On the other hand, they use AI solutions to give their employees more time to focus on core tasks and provide automation tools.
However, the combination of these two approaches can also lead to some incredible results. AI and gamification can converge in different ways. We mentioned earlier the example of Dota 2 and OpenAI. In this example, the AI works within the gamification environment and uses its mechanics to become better at the game.
Simply put, that’s where gamification mechanics are actually used to improve AI. However, AI can also be used to develop the best gamification practices for a particular task. It could assess results based on data input and use it to create new variables to improve gamification outcomes.
How ML and Gamification Converge
This combination of machine learning and gamification is highly complex, but it isn’t impossible. We can already see examples of their application. There’s also more research on the subject and the potential for such implementation popping up each day.
One of the biggest issues with gamification is that there is no long-term alignment with the end goal of users. That’s why machine learning can be used for personalizing gamification to keep the level of engagement high over more extended periods.
Simultaneously, there is potential for using AI for predicting user behavior in the future throughout a gamification task. With these predictions, it’s possible to adjust gamification and increase motivation.
Applying Gamification in AI for Business Purposes
AI and gamification can be used in business very narrowly or as wide as needed. For example, a company might use a program to learn about the best ways to boost revenue. The gamification approach can be used to better define and explain the values of business revenue, while AI can do the hard work of determining the best approach to increase revenue.
On the other hand, solutions can be used with a narrower approach, which is far more realistic at the moment. The same method can be used for perfecting a single business process. Instead of focusing on complex values like revenue that come with hundreds of variables, this could be used to improve sales or training.
In every aspect of business where gamification and AI are already used, there’s clear potential for combining them. When implemented together correctly, they can speed up various processes and data-related tasks while engaging humans on their end of the job.
AI & Gamification for Assessing Job Candidates
Traditionally, recruitment practices haven’t had a lot of success in finding the right job candidates for the job. In fact, over half of new employees fail at the job. That costs companies and recruiting agencies that get hired for the job a lot.
Most candidates are assessed through tests, interviews, and resumes, but this hasn’t shown great results. That’s why companies are already using AI gamification to improve their assessment and hiring. For example, Scoutible has created a short game to determine whether a candidate would be the right fit for a specific company.
Instead of testing the work skills and requirements, the game tests personalities, how people think, and their decision making. The performance in the game is mapped and applied to the metrics required for a particular job to find the overlapping qualities.
AI & Gamification for Training and Education
Education is probably the field where gamification is applied the most. At the same time, Artificial Intelligence is used to personalize education, create better learning content, automate tasks within courses, help with tutoring, engage learners, assess training results, etc.
The goals, functionalities, and applications of these two technologies in education complement each other, which is why we expect the first major application to be in this field. Gamification can encourage learners to go through their courses, gain rewards, achieve new levels, and motivate them.
On the other hand, AI can be used to implement gamification in a personalized fashion and create courses relevant to the education program or industry training needs.
Conclusion
Gamification and AI are already working their magic across the globe, but their development and implementation don’t stop here.
Yes, there are challenges in implementing the right machine learning models for these tasks, but the tech community is already working on new solutions. It’s not a matter of inventing new technologies – it’s about combining two existing ones to get better results.
