PART 3: Design for Play: New Approaches for AI-Curious L&D Teams
A 4-part series exploring the use of play, playful techniques and AI for learning designers and Learning & Development teams
Links to the series: Part 1: The Science of Play and Playfulness in Adult Learning, Part 2: Modern Applications of AI in Adult Learning: The State of Play in L&D, Part 3: 12 Approaches for Designing Play with AI, and Part 4: P.L.A.Y. Build Framework Photos by Xavi Cabrera on Unsplash, Luke Jones on Unsplash, and Alex Guillaume on Unsplash
Part 3: 12 Approaches for Designing Play with AI
Here are 12 considerations or approaches helping L&D teams infuse AI-powered play into educational experiences. Each element represents a distinct category for creating engaging, effective learning with some straight-forward, and some advanced applications.
1. Play and Design Thinking
Description: Integrating design thinking methodologies into playful learning experiences with AI support.
Design thinking—with its emphasis on empathy, ideation, prototyping, and testing—naturally aligns with playful learning approaches. AI can enhance each phase of the design thinking process, serving as a collaborative partner that helps learners understand user needs, generate creative solutions, rapidly prototype ideas, and gather feedback for iteration. This approach transforms traditional problem-solving into a game-like experience of discovery and creation. As Tim Brown notes in his foundational work Change by Design, design thinking is inherently playful because it "utilizes elements of play, intuition and exploration as well as the more traditional tools of rational and analytical thought."
Applications for learning designers:
Empathy-building simulations: A learning designer creates AI-powered simulation games where learners must understand different stakeholder perspectives. For example, in a healthcare innovation course, students might interact with AI personas representing patients with diverse needs and constraints. The AI adapts responses based on the student's questions, creating a dynamic role-play that builds empathy through gameplay. Students earn points for uncovering key insights about user needs, turning empathy-building into an engaging challenge.
AI-enhanced ideation workshops: In a design thinking workshop, an AI facilitator guides learners through playful ideation techniques. The system might start with a "How Might We" challenge, then introduce game-like constraints ("What if you had unlimited budget?" or "What if this had to fit in your pocket?") to spark creative thinking. The AI can also introduce random connections or analogies from unexpected domains to break conventional thinking patterns. After each ideation round, the AI visualizes the group's ideas and identifies interesting combinations, creating a dynamic, evolving idea landscape that feels like a collaborative exploration game.
Rapid prototype playground: Learners engage in fast-paced prototyping cycles with AI assistance. The AI helps translate rough sketches or descriptions into more polished visual prototypes, allowing learners to focus on conceptual iteration rather than technical execution. For example, in an educational design course, students describe learning experiences they want to create, and the AI generates interactive mockups they can immediately test and refine. This creates a playful sandbox environment where ideas can be quickly built, tested, and improved—similar to building in a creative game like Minecraft, but with real-world applications.
User testing simulation game: After creating prototypes, learners test their solutions in an AI-simulated environment that mimics real user behavior. The AI generates diverse virtual users who interact with the prototype in different ways, surfacing usability issues or unexpected use cases. Learners earn points for identifying and addressing user pain points, turning the testing phase into a detective-like game of discovery. The AI can also introduce surprise scenarios or edge cases that challenge assumptions, adding an element of playful unpredictability that enhances learning.
2. Create & Invent
Description: Empowering learners to build new ideas, solutions, or artifacts through AI-assisted creative processes.
AI can serve as a creativity partner — providing inspiration, generating prototypes, or handling tedious tasks — so that learners can focus on imaginative work. By designing "maker space" opportunities where learners build or design something with AI support, we tap into the power of learning-by-creating. Daniel Christian notes in her work on Learning from the Living Class Room that AI-facilitated spaces could enable virtual brainstorming sessions, group creation, and collaborative projects, even suggesting resources and giving feedback on ideas.
Applications for instructional designers:
Virtual maker space: A learning designer creates a digital "studio" where a team of students can sketch inventions together. An AI tool like Luma AI or Kaedim can generate quick 3D models or images based on the students' ideas and ask questions to spur further creativity. [11]
AI brainstorm buddy: In a marketing course, each student uses an AI chatbot as a brainstorming buddy. The AI comes up with unconventional campaign ideas or slogans when the student hits a wall, and also critiques the student's ideas constructively. [12]
Co-creating content: Rather than a traditional essay, learners are tasked with creating a podcast or video. AI tools assist by generating music, images, or even rough drafts of scripts based on student input. The student remains the director, but the AI expands the creative possibilities — a process that mirrors play, where imagination leads and tools follow.
3. Discover & Explore
Description: Creating environments where learning happens through curiosity-driven investigation and AI-guided exploration.
… Yep, this encourages open-ended exploration and trial-and-error. AI can create “low-stakes environments” where learners feel safe to take risks and learn from mistakes. By reframing fear of failure as a chance to iterate, educators can shift from a mindset of caution to one of curiosity and innovation. As Dr. Philippa Hardman writes in her article A Post-AI Learning Taxonomy, we are entering "an era of experimentation and innovation" where AI enables risk-taking and iteration in learning.
Applications for Learning Designers:
Virtual science lab: Using an AI-driven lab simulation in Science-based companies like pharma, students freely experiment with different chemical reactions. The AI provides hints if experiments go wrong, letting learners safely learn from failed attempts and adjust their approach. Texas A&M's research on Why Video Games Should Be Used in Schools highlights how these environments allow students to "learn in a safe environment where failure is allowed and encouraged."
AI-powered knowledge quests: Design information scavenger hunts where learners interact with an LLM through increasingly complex prompts to uncover information, with the AI providing clues and guidance as learners refine their questioning strategies.
Idea brainstorming: Learners use a generative AI tool (like ChatGPT) to brainstorm multiple solutions to a design challenge. They can test wild ideas, get instant feedback on why some fail, and refine their solutions — turning failure into a learning-rich game of discovery.
4. Collaborate & Connect
Description: Using AI to facilitate teamwork, peer learning, and social connection in educational contexts.
Playful experiences are often social, and AI can enhance this by connecting learners, moderating group activities, or even acting as a participant. Research on AI in Learning Ecosystems shows that the social interaction in communal learning yields academic gains comparable to one-on-one instruction, with added socio-cultural benefits for more students.
What to consider:
AI-coordinated study groups: A learning designer sets up an AI tool that forms student groups based on complementary strengths and interests. The AI handles logistics (scheduling, discussion prompts) and even translates across languages so a global cohort can collaborate seamlessly. [13]
AI as discussion facilitator: In a virtual classroom, an AI teaching assistant "bot" joins breakout groups to stimulate discussion and ensure everyone is included. It asks guiding questions, answers simple queries, and keeps the team on track so that the group work stays lively and on-topic. [14]
Collaborative problem game: Learners from different locations meet in an online role-play game (designed by an instructional designer) to solve a mystery. An AI agent in the game plays the role of a teammate or helper, providing clues and feedback to the group. This shared playful experience builds communication skills and a sense of community, as encouraged by Hardman's vision of AI-powered communal learning. [15]
5. Game on!
Description: Implementing game mechanics and competitive elements enhanced by AI for motivation and engagement.
This element keeps learners in the "flow" zone with well-balanced challenges. Playful learning thrives when tasks are neither too easy nor impossibly hard. AI can be leveraged to tailor difficulty in real-time: for instance, by adjusting the complexity of questions or problems as a learner improves. According to AI Multiple's research on Conversational AI in Education, these systems "can personalize the educational experience based on how students respond to the chatbot," creating a dynamic, game-like learning environment.
Applications for learning teams:
Adaptive quiz game: An instructional designer creates a quiz app where an AI algorithm selects questions based on the learner's performance. If a student gets answers correct easily, the AI serves up a tougher question next; if the student struggles, it provides a simpler question or a hint. This ensures a personalized challenge curve for each learner, much like a video game adjusting difficulty to keep players engaged.
Scenario with levels: In a customer service training simulation, learners must handle virtual customer interactions. The AI system behind the scenes monitors how the learner handles easy cases before unlocking more complex customer scenarios. It might start with a friendly customer (easy mode) and gradually introduce tougher, upset customers (hard mode) as the learner's skills improve — maintaining a fun but stretching challenge.
Intelligent difficulty scaling: Create game-based learning where AI adjusts challenge levels in real-time based on learner performance, maintaining the optimal flow state between boredom and frustration. This may be a bit forward-looking, but worthy of exploring with your favorite LLM as a fellow game designer.
6. Simulate & Immerse
Description: Creating realistic, AI-powered practice environments where learners can safely apply skills.
Immersion means the learner feels "inside" the experience, which can make learning feel like play and enhance engagement. AI can generate rich media (images, sounds, even virtual environments) or NPC characters to populate a simulation. When done right, AI-enhanced immersion provides that all-encompassing focus seen in good games.
Applications for learning designers:
AI-enhanced virtual reality: Nursing students train in a VR hospital simulation. The scenario is powered by AI: patients (virtual characters) respond realistically to treatment, and emergencies unfold unscripted. The combination of VR's visual immersion and AI's dynamic interaction creates a high-fidelity "game" where students forget they are in a training and fully engage in problem-solving as if it were real.
Augmented reality field trip: An outdoor education program uses AR glasses with an AI guide. As learners walk through a botanical garden, the AI overlays information, quests, and challenges ("Find a plant that cures headaches") onto their view. The experience is like a Pokémon Go-style game for learning botany. The AI adjusts the hints or route based on how quickly the group completes challenges, keeping them immersed in the exploratory play.
Interactive story world: A literature teacher designs an interactive storytelling world where students converse with AI-driven characters from a novel. The setting includes AI-generated artwork and soundscapes to match the story's mood. Students can roam the environment, talk to characters, and even influence the story's direction. This deep immersion in the story's world not only makes learning fun but also helps students grasp narrative and themes more intuitively than a traditional lecture.
7. Challenge & Overcome
Description: Creating optimal difficulty experiences where AI helps learners push their boundaries.
One hallmark of games is instant feedback on actions, and similarly, AI tutors or agents can give real-time responses to learner inputs. This might include correcting errors, offering explanations, or giving praise and points for good attempts. Timely feedback keeps the experience interactive and helps learners course-correct in the moment, which is both motivating and critical for learning.
Applications for content developers:
AI writing coach: A student writes an essay draft in a learning platform. As they write, an AI writing assistant highlights unclear sentences or factual errors and provides immediate suggestions. The instant feedback turns writing into an iterative game: students try to get a "clear draft" with no AI flags, learning from each round of feedback. This one’s easy!
Interactive tutor bot: In a math learning app, an AI tutor watches as a learner works through algebra problems step by step. If the student makes a mistake, the bot immediately points it out and gives a tailored hint. By answering questions and giving real-time feedback on exercises, an AI tutor can significantly deepen learning impact in a scalable way.
Progressive challenge pathways: Design learning sequences where AI monitors mastery and unlocks increasingly difficult challenges, automatically identifying when learners are ready to advance to more complex material. For those teams in particular already implementing gamified learning approaches, this could be a great enhancement.
8. Construct & Build
Description: Using AI to support learners in assembling knowledge structures and mental models.
This includes leveraging narrative to make learning experiences playful and meaningful. Storytelling provides a context that draws learners in and gives purpose to activities. AI can assist by generating rich story scenarios, characters, or dialogues on the fly, tailoring the narrative to learners' interests or choices. Research from the National Institutes of Health on Leveraging Micro-Stories in Education demonstrates how narrative elements significantly enhance engagement and retention.
Applications for Learning Designers:
Dynamic scenario generation: An instructional designer uses an LLM to create interactive case studies. For instance, a history teacher prompts an AI to spin a story where the student is a journalist in ancient Rome. The student then navigates this narrative, interviewing AI-generated historical figures, effectively learning history through storytelling and conversation. [16]. This scenario could take on a more modern approach by examining the growth of Apple and Steve Jobs, or the compelling life of Walt Disney.
Choose-your-own-adventure quests: A training program on ethics is transformed into a branching story game. Learners make decisions at key plot points, with an AI adapting the storyline and consequences based on their choices. The narrative format helps learners see the real-world impact of decisions in a safe, story-driven simulation.
AI co-created stories: To practice language skills, students collaborate with an AI to write a short story. The AI might suggest plot twists or new characters, and students respond creatively. This playful co-creation yields a narrative product that is highly engaging — similar to how The Oregon Trail game taught children about 19th-century pioneer life through an immersive story context. As Texas A&M's research highlights, these narrative-driven games have been "teaching history to generations of young people."
9. Solve & Resolve
Description: Creating complex problem scenarios where AI enhances the problem-solving process.
Give learners agency and choice in the learning process. Autonomy is a core aspect of play — think of open-world games where players decide what to do next. AI can support autonomy by offering a menu of options or adapting to user decisions. Allowing learners to pursue their interests or preferred approach increases engagement and personal relevance.
Applications:
Branching learning paths: A designer creates an AI-driven module where at the start, learners choose a "mission" (e.g., solve a city's water crisis vs. design a water park). Based on the choice, the AI personalizes subsequent tasks to that context. This is similar to choosing a quest in a game. All missions teach the same core skills, but students exercise autonomy in how they learn them, which boosts motivation.
Personalized project selection: In a professional development program, an AI assistant helps learners customize their capstone project. It asks about their interests and career goals, then suggests project ideas accordingly. The learner can tweak or choose among these AI-suggested options. Because they have a say in defining the project, they engage with it more playfully and meaningfully.
Multi-path problem analysis: Design complex case studies where AI helps learners explore different solution approaches, showing the potential consequences of various strategies without giving away answers.
10. Experiment & Test
Description: Creating safe spaces for trial-and-error learning with AI-enhanced feedback.
Incorporate role-playing scenarios where learners can assume different identities or perspectives, with AI enriching the experience. Role-play is inherently playful and can develop empathy, communication, and critical thinking. Dr. Philippa Hardman highlights AI-mediated role-playing as a powerful idea: AI can generate realistic scenarios and guide learners through roles, coaching them as they make decisions and collaborate. [17]
How tos:
Virtual customer role-play: For a customer service course, the instructional designer uses an AI to simulate customers with various personalities. A learner must "serve" the AI-customer via chat or voice. The AI responds in character (sometimes friendly, sometimes upset, and make sure your LLM knows it’s a role-play!), and the learner practices handling each situation. After the interaction, the AI coach gives feedback on what the learner did well or could improve. This is essentially a playable scenario where the student is the hero solving customer problems.
Historical role-play game: In a history class, students are assigned different roles (e.g., delegates at a historical UN meeting). An AI system generates the setting and context — it might brief each student on their role's viewpoint and even generate speech prompts. During the simulation, the AI moderates and can even intervene as a non-player character ("NPC") diplomat to steer the debate if it stalls. Learners thus learn history by living it in a guided role-play, with the AI ensuring the experience stays on track and educational. [18]
11. Validate & Reflect
Description: Using AI to help learners evaluate their progress and extract meaningful insights from their experiences.
Reflection turns experience into insight — it's when learners think back on what happened, why things turned out a certain way, and what could be learned for next time. AI tools can facilitate this by asking probing questions, guiding debrief discussions, or generating summaries of a learner's performance for analysis. Stanford's d.school has created Riff, an AI-Powered Reflection Assistant that "engages students in deeper exploration of their experience" and helps "embed reflection into all kinds of learning experiences."
Applications:
AI reflection chatbot: After a simulation game or role-play, learners converse with an AI chatbot (like a virtual coach) that asks them reflective questions: "What strategy worked best for you and why?", "What was a mistake that ended up teaching you something?". The AI might draw attention to a pivotal moment (using data from the simulation) — e.g., "I noticed you changed approach after level 3; what prompted that?" — to spark insight. This mirrors a guided reflection session, accessible anytime.
Automated debrief report: In a business strategy game played by students, the AI generates a custom "post-game report" for each team. It highlights key decisions, outcomes, and turning points in the game. The report includes prompts like "Given these results, what might you do differently in a real business scenario?" The learning designer in charge provides these reports to teams, who then discuss them (peer reflection). The AI's summary saves time and focuses the reflection on substantive lessons.
Personalized learning journals: Design AI-guided reflection tools that prompt learners with targeted questions based on their specific experiences and challenges, helping them process and integrate new knowledge.
12. Orchestrate & Direct
Description: Empowering instructors and learners to coordinate complex learning experiences with AI assistance.
Personalize the experience to each learner through AI's adaptive capabilities. In a playful context, this ensures the game stays fun and instructive for everyone — novice or expert, fast or slow learner. Conversational AI systems already demonstrate this by tailoring content to individual interests and even shifting teaching strategy as the learner's questions grow more advanced, as highlighted in research on Use Cases of Conversational AI in Education.
Try this:
Learning path navigation: Load sample learner records or portfolio(s) into an LLM and prompt AI to suggest personalized next steps based on learner progress, interests, and goals, helping them navigate self-directed learning journeys. If in a classroom cohort, you could ask students to guess mock-up skills being gained, or the level of proficiencies gained.
Interest-based adaptation: A language learning app uses AI to detect what topics excite a particular learner. If the student tends to write a lot about football or music in their practice essays, the AI starts weaving those themes into grammar exercises or reading comprehension passages. By aligning with the learner's interests, the AI maintains engagement — it's as if the learning game knows the player's favorite themes and rewards them with relevant content.
Real-time skill coaching: During a public speaking practice session, an AI analyses a learner's performance (volume, pace, filler words). If the AI notes the learner is speaking too fast, it might prompt them mid-practice: "Take a breath, and try to slow down for the next part." If the learner is doing well, it might introduce a new challenge, like an unexpected question to respond to on the fly. This adaptive coaching ensures the practice stays in that productive zone of improvement, much like an adaptive AI opponent in a video game that gets stronger as the player gets better.
A review of how this all fits…. Scienvce, Strategy and the Elements toolset:
By thoughtfully combining these 12 elements, learning designers can create AI-enhanced experiences that are as playful and engaging as they are pedagogically effective. Each element contributes to a rich learning journey where AI is not just a content-delivery tool, but a creative partner in play.
Links to the series: Part 1: The Science of Play and Playfulness in Adult Learning, Part 2: Modern Applications of AI in Adult Learning: The State of Play in L&D, Part 3: 12 Approaches for Designing Play with AI, and Part 4: P.L.A.Y. Build Framework
Acknowledging leveraging Perplexity, Gemini, ChatGPT, and Claude for research, formatting, testing links, challenging assumptions and aiding the creative process.
The views and opinions expressed in this article are solely those of the author and do not necessarily reflect the views of the author’s employer or any affiliated organizations.
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NOTES
[11] Christian, D. S. (n.d.). Learning from the Living [Class] Room -- A powerful vision by Daniel S. Christian. Retrieved from http://danielschristian.com/thelivingclassroom/quotes.htm#:~:text=%2A%20AI,discussions%20%26%20collaborative%20project%20work
[12] Christian, D. S. (n.d.). Learning from the Living [Class] Room -- A powerful vision by Daniel S. Christian. Retrieved from http://danielschristian.com/thelivingclassroom/quotes.htm#:~:text=,foster%20collective%20learning%20and%20innovation
[13] Christian, D. S. (n.d.). Learning from the Living [Class] Room -- A powerful vision by Daniel S. Christian. Retrieved from http://danielschristian.com/thelivingclassroom/quotes.htm#:~:text=performance%20data%20to%20create%20optimised,learning%20cohorts
[14] Christian, D. S. (n.d.). Learning from the Living [Class] Room -- A powerful vision by Daniel S. Christian. Retrieved from http://danielschristian.com/thelivingclassroom/quotes.htm#:~:text=create%20and%20connect%20diverse%20groups
[15] Christian, D. S. (2024, January 14). Your classmate could be an AI student at this Michigan university [Frick] + other AI in our Learning Ecosystems. Retrieved from http://danielschristian.com/learning-ecosystems/2024/01/14/your-classmate-could-be-an-ai-student-at-this-michigan-university-frick-other-ai-in-our-learning-ecosystems/#:~:text=experiences%3F%20%E2%80%A6%20TL%3BDR%3A%20while%20personalised,Personalised%E2%80%9D%20Learning
[16] PMC. (n.d.). Leveraging Micro-Stories to Build Engagement, Inclusion, and Neural Networking in Immunology Education. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC6893969/#:~:text=Abstract
[17] Christian, D. S. (n.d.). Learning from the Living [Class] Room -- A powerful vision by Daniel S. Christian. Retrieved from http://danielschristian.com/thelivingclassroom/quotes.htm#:~:text=AI
[18] Christian, D. S. (n.d.). Learning from the Living [Class] Room -- A powerful vision by Daniel S. Christian. Retrieved from http://danielschristian.com/thelivingclassroom/quotes.htm#:~:text=,and%20guide%20learners%20through%20them
[19] Hardman, P. (n.d.). A Post-AI Learning Taxonomy - by Dr Philippa Hardman. Retrieved from https://drphilippahardman.substack.com/p/a-post-ai-learning-taxonomy#:~:text=Hardman%20drphilippahardman,of%20experimentation%20and%20innovation
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These are some really smart and well-informed use cases Marc, and deserve to be part of any AI learning strategy! Definitely influencing my thinking.