As generative AI propels gamification to unprecedented levels, it’s set to revolutionize customer interactions, making it an undeniable force in the business landscape. Prominent companies have cleverly woven game mechanics into their products. Take McDonald’s and its success with the McDonald’s Monopoly game, or how LinkedIn added progress bars, profile statistics and the ability to endorse other users to increase user engagement.
The intertwining of gamification and generative AI becomes as multifaceted a tool as a swiss army knife for those seeking to connect with their audience and consistently deliver beyond expectations. The evolution of game elements that product development teams can harness is like switching from the original Nintendo to the new PlayStation 5; it’s an entirely different platform.
It’s time for leaders to acknowledge this shift. Let’s explore three opportunities for elevating gamification using generative AI.
3 Ways Gen AI Boosts Gamification
- More personalized game loops with less effort.
- Realistic simulations and role-playing for students and professionals.
- Streamlined health and wellness platforms and health insurance processes.
Automated and Personalized Game Loops
A successful gamification strategy relies on constantly evolving gameplay and excitement. Previously, this required extensive manual effort to craft personalized content, quests and challenges suited to each user’s skills, preferences and goals.
Generative AI can now automate and customize the entire game loop for each individual, tapping into their intrinsic motivations. AI can analyze input patterns to adjust game controls and make them more intuitive for the player. Additionally, players can adjust their difficulty setting at will, keeping themselves engaged by making the game challenging without veering into frustration. In testing scenarios, we commonly observe that correctly answering questions and progressing to more challenging questions generates appropriate tension tailored to the player’s performance.
For example, an e-learning platform could use generative AI to analyze data about a student’s strengths, weaknesses and interests. It could then generate personalized math quizzes, puzzles and bonus missions to help reinforce concepts using storylines and characters that resonate with that particular learner and focus on where they need the most help. Each student gets a unique gamified curriculum.
You can also apply the data for personalization to rewards systems, adjusting them to maximize user engagement and satisfaction. For instance, Starbucks built an AI program called DeepBrew and deeply integrated it into its rewards program. DeepBrew sends out personalized menu recommendations based on preferences and order history, and rewards members receive bonus stars for completing personalized challenges.
Realistic Simulations and Role-Playing
AI-powered conversational agents can participate in immersive role-playing scenarios and training simulations, adapting their dialogue and reactions to the user’s choices. This level of realism and customization provides engaging, true-to-life simulations.
Consider a medical training simulator that could generate various patient scenarios for a medical student to diagnose and treat. The virtual patient powered by generative AI could respond differently based on the learner’s bedside manner, questions, examinations and treatment plans, creating varied simulations.
In human resources, employing AI technology can profoundly augment conflict resolution training. An AI agent can be programmed to act as an antagonist in simulated conflict scenarios, providing a realistic training ground for employees. The model can be prompted to mimic various conflict situations, testing and honing employees’ conflict resolution skills in a controlled setting.
Generative AI can enable health and wellness platforms to provide customized fitness plans, nutrition recommendations, medication adherence and mental health exercises tailored to each user. Gamifying the experience with quests, levels, rewards and reminders can motivate people to stick to their plans and develop healthy habits.
For instance, the Weightlifting.AI app uses performance in specific lifts and user input to adjust programming and loads. Users enter a set of preferences, such as frequency and duration of training, experience and initial strength measures, which the algorithm uses to recommend an initial program. The program adjusts during a training session as the user completes lifts.
In health insurance programs, generative AI can drive applications that remind users of necessary health appointments based on their medical records. It can assist in medication adherence by sending reminders and offering rewards for compliance. In cases where there are drug supply constraints, AI can recommend alternatives to prescribers for discussion with patients.
How Do We Create AI That Targets UX?
Developing this type of AI starts with defining an objective to enhance engagement, personalize experiences or provide dynamic content — content that changes based on data, user behavior and preferences. Each would use different models.
Generative adversarial networks, or GANs, generate images (dynamic content), while a long short-term memory, or LSTM, creates a custom sequence (personalizing experiences). GANs and LSTM then work together to enhance engagement.
Integrating AI into a platform starts with data. We need to collect data about users, their behaviors, their preferences and how they interact with the platform. Once we have trained and fine-tuned an initial model(s), we must integrate it into the platform to receive input and send output continuously.
Generative AI-powered gamification enables revolutionary personalization, realistic simulations and optimized wellness journeys for everyone. As leaders, we must recognize gamification and generative AI as indispensable tools to forge the next generation of transformative solutions. They represent a new frontier of innovation and immense possibilities available to those bold enough to explore it.