Beyond Quizzes: 10 Practice-Based Learning Activities Powered By LLMs

Human-Centered Activity Design For Adult Learning

Large Language Models (LLMs) make designing learning activities more efficient than ever. From early ideation through iteration and refinement, AI can support learning experience designers (LXDs) in creating engaging, human-centered instructional content that supports effective interactions and learning.

LXDs already utilize AI to generate and refine learning objectives, summarize resources, draft rubrics and feedback criteria, develop and refine instructional activities, and provide exemplars of completed work. Instructors are also discovering the benefits of AI use in live instruction. Learning can be enhanced through real-time creation of personalized vocabulary and reading tasks, as well as by engaging learners directly with AI for activities such as debate.

LLMs are supportive in learning design (LD), yet the activity types they produce run the risk of redundancy. Multiple-choice, gap-fill, short-answer, and open-response items are tried-and-true formats and can be useful for learner engagement. However, LLMs are capable of much more when it comes to developing learning activities that truly set Instructional Design (ID) projects apart. Below are ten practical, human-centered, practice-based adult learning activities that LLMs can support you in building today.

In this article...

• 1. Rapid Fire
• 2. Post-Mortem
• 3. Case Study
• 4. Chain Reaction
• 5. Building Writing
• 6. Counterfactual Thinking
• 7. Devil's Advocate
• 8. SCQA
• 9. Choose Your Own Adventure With Decision Replay
• 10. Assumption Testing And Reframing
• Keeping Human Judgment At The Center

1. Rapid Fire

Rapid Fire challenges adult learners in retrieval, prioritization, and synthesis of information. Pairing an LLM with a timer is key to developing an effective practice-based learning activity in which learners respond to AI acting as a prompt generator and time-boxed questioner. This can be especially effective with customized AI tools, though customization is not necessary when prompts are well crafted and front-ended with adequate data.

Rapid Fire works best when learners are comfortable responding in open-text formats. The LLM should first receive input consisting of the information, main ideas, topics, or themes the learner must master. The more specific this input, the more targeted the questions will be. Designers may set boundaries such as adaptive difficulty (increasing challenge as responses improve and decreasing it when learners struggle), a fixed number of questions, or progression through Bloom's taxonomy from recall and understanding toward analysis and evaluation. In live sessions, instructors may also manage timekeeping and learner accountability.

AI prompt to get started:

• You are acting as a time-boxed question generator for a professional learning activity.
• The topic areas I am learning are: [insert key concepts, themes, or objectives].
• Ask me one question at a time.
• Increase the level of difficulty as my responses demonstrate understanding.
• If I struggle, adjust the difficulty downward.
• Do not explain answers unless I ask.
• Wait for my response before moving on to the next question.
• We will complete [number] questions total.

2. Post-Mortem

Learning from failure is an important skill to cultivate. The post-mortem, practice-based learning activity encourages reflection, systems thinking, and goal-setting by examining both successes and shortcomings. LLMs can support AI-facilitated after-action reviews by generating reflective prompts aligned to learning objectives and guiding learners through the process in real time as a pattern spotter and neutral facilitator.

For example, following the rollout of a new onboarding process or internal tool, an LLM might prompt a team to reflect on what worked as intended, where breakdowns occurred, and which assumptions did not hold. By identifying patterns across successes and missteps, teams can develop clearer action plans for future implementations.

AI prompt to get started:

• You are acting as a neutral facilitator for a post-mortem learning activity.
• The context is: [describe the project, implementation, or experience].
• Guide me through reflection by asking structured questions about what went well, what didn't, and why.
• Help identify patterns, contributing factors, and missed opportunities.
• Do not assign blame or judgment.
• End by helping me articulate lessons learned and next steps.

3. Case Study

Case studies challenge learners to apply what they have been learning to real-world contexts. LLMs can generate scenarios and shift perspectives to personalize case studies for individual learners, their fields, and their professional environments. Case studies may be prepared ahead of time for teams or individual learners.

LLMs can also offer adaptive case studies with AI-generated variations when prompted to ask the user targeted questions prior to providing output. An LLM might be customized to ask for specific details, such as the user's department, role, years of experience, and professional goals, before offering a case study aligned to a shared learning objective, such as enhanced workplace communication or the development of social-emotional learning (SEL) knowledge and skills.

AI prompt to get started:

• You are acting as a case-study designer for adult learners.
• Before generating the case, ask me for relevant details such as my role, industry, experience level, and goals.
• Then present a realistic scenario aligned to the learning objective: [insert objective].
• Ask me to analyze the situation and make recommendations.
• There is no single correct answer.
• Prompt me to explain my reasoning and consider trade-offs.

4. Chain Reaction

"Chain Reaction" is another name for cause–effect mapping. The focus of this practice-based learning activity is impact awareness. Similar to a post-mortem, learners think about final outcomes or results; however, Chain Reaction provides the opportunity to examine both failures and successes at the micro level as a series of actions, events, and impacts.

In this activity, AI encourages learners to break situations into smaller parts, zoom in on individual behaviors and choices, critique what transpired, and then reassemble those parts to make meaningful connections. This activity is particularly powerful in leadership, ethics, and change-management contexts.

AI prompt to get started:

• You are acting as a systems-thinking facilitator.
• The situation or decision to analyze is: [describe event or action].
• Help me break this into a sequence of actions, reactions, and impacts.
• Ask me to identify both intended and unintended consequences.
• Encourage me to zoom in on individual choices and zoom out to broader effects.
• Pause regularly so I can explain my thinking.

5. Building Writing

Dialogue is easily facilitated by LLMs, which excel at simulating personality, intent, and language patterns. As language pattern experts, LLMs can serve as conversational partners and counterpoint generators.

In Building Writing, LLMs engage learners in a back-and-forth, "you say / I say" cumulative creation process. The learner may begin by telling the LLM what they intend to create, or the LLM may already be pre-programmed with a topic. Exchanges need not provide resolution until this practice-based learning activity concludes.

The ending can be defined in advance, such as after a set number of turns, or triggered by the learner using a specific phrase (e.g., "The End.") This activity sustains momentum, encourages respectful engagement with ideas that are not one's own, and reinforces collaboration skills.

AI prompt to get started:

• You are acting as a collaborative writing partner.
• The topic or purpose of our writing is: [describe].
• We will take turns adding to the text.
• Each turn should build on what came before without resolving the piece too early.
• Do not dominate the writing or close the discussion unless I instruct you to do so.
• The activity will end when I type: "The End."

6. Counterfactual Thinking

"What if" scenarios encourage systems thinking and build foresight and strategic reasoning. When learners share a real-life situation, past or present, within their experience or organization, LLMs can present alternative conditions for consideration.

Learners then engage with the AI to explore plausible downstream effects centered on the question, "What if X had been different?" As learners reflect on these alternative realities, LLMs can prompt them to explain and revise their reasoning. This activity is particularly effective in leadership, ethics, and policy contexts, as learners demonstrate not only knowledge, but integrity in action.

AI prompt to get started:

• You are acting as a facilitator for counterfactual thinking.
• The real situation or decision to examine is: [describe].
• Present an alternative condition by asking, "What if [key variable] had been different?"
• Walk through plausible downstream effects.
• Ask me to explain how and why outcomes might change.
• Encourage me to revise or extend my reasoning.

7. Devil's Advocate

Devil's Advocate is helpful for professional learning, leadership practice, ethics, and decision-making. In this activity, LLMs function as a structured counter-voice, challenging reasoning without ego or hierarchy, something that is not always feasible with human challengers.

By positioning AI in the challenger role rather than a colleague, Devil's Advocate supports psychological safety. The practice-based learning activity encourages critical thinking and allows learners to surface assumptions, blind spots, and risks while practicing how to defend decisions professionally.

AI prompt to get started:

• You are acting as a structured devil's advocate in a professional learning activity.
• The decision, position, or proposal I am presenting is: [describe].
• Your role is to respectfully challenge assumptions, surface risks, and ask difficult questions.
• Do not argue for the sake of winning.
• After each challenge, ask me to clarify or defend my reasoning.
• Maintain a neutral, professional tone.

8. SCQA

Situation, Complication, Question, Answer (SCQA) is widely used in consulting, executive communication, strategy, and leadership storytelling. SCQA supports structured reasoning and professional communication.

Developing an SCQA helps learners strengthen storytelling, argumentation, and negotiation skills by identifying problems, promoting inquiry, and proposing solutions. When learners apply SCQA to challenges in their own work environments, LLMs can assess drafts, test clarity and logic, and support message refinement. This approach encourages synthesis rather than information dumping and translates directly to workplace tasks such as briefings, proposals, and progress updates.

AI prompt to get started:

• You are acting as a communication coach using the SCQA framework.
• The context I need to communicate about is: [describe].
• Help me draft a Situation, Complication, Question, and Answer.
• Review each section for clarity, logic, and relevance.
• Ask clarifying questions where the structure is weak.
• Suggest refinements without rewriting the message for me.

9. Choose Your Own Adventure With Decision Replay

AI-supported decision-path simulations with reflective replay activate multiple adult-learning principles. Learners maintain agency by becoming decision-makers rather than passive consumers of content. This practice-based learning activity works especially well in contexts where there is no single right answer, mirroring real workplace decision-making.

AI presents step-by-step scenarios and offers plausible choices at each stage. It is critical that the AI does not judge learner decisions, instead allowing learners to explain their reasoning and explore outcomes without scoring. The Decision Replay element enables learners to revisit earlier decision points and try alternative paths, encouraging metacognition through reflection on what they would do differently and why.

AI prompt to get started:

• You are acting as a scenario guide for a decision-based learning activity.
• Present a realistic professional scenario related to: [topic].
• At each step, present 2–4 plausible choices.
• After presenting the choices, pause and wait for my response before continuing.
• Do not judge my decisions or score them.
• After each choice, describe likely consequences and ask me to explain my reasoning.
• Allow me to return to an earlier decision point and try a different path if I choose when I say "Decision Replay."
• Do not advance the scenario unless I select a choice or request a replay.
• When the scenario reaches a natural conclusion, ask whether I want to replay an earlier decision or end the activity with a reflective summary.
• The activity ends only when I say "End simulation."

10. Assumption Testing And Reframing

Critical thinking is developed as learners address unexamined beliefs, habits of thinking, and "the way things have always been done." Assumption Testing and Reframing helps learners surface assumptions and underlying decisions, policies, or practices.

In this activity, after the learner responds to a scenario, the LLM mirrors and surfaces assumptions that may not be immediately visible. For example, if a learner's response reflects gendered assumptions, the AI may highlight this aspect, prompting reconsideration. In this way, LLMs act as reframing partners and low-stakes challengers, offering alternative perspectives without declaring any single view correct.

AI prompt to get started:

• You are acting as a reflective partner for examining assumptions.
• The scenario, policy, or decision to analyze is: [describe].
• Ask me to explain my initial response or position.
• Then surface underlying assumptions that may be shaping my thinking.
• Offer alternative ways to frame the situation without declaring one "correct."
• Invite me to reconsider and reflect on what changes.

Keeping Human Judgment At The Center

LLMs are changing the way L&D professionals engage learners. Not only do LLMs support Instructional Designers and educators in developing standard question types more efficiently, but they also create opportunities to engage learners in varied, meaningful, and innovative ways. As explored in prior work on AI-supported professional development design, live tutoring, and ethics and integrity in AI use, the most effective applications of LLMs are those that extend human judgment rather than replace it. These activities are most effective when learners are also taught how to question AI outputs, surface assumptions, and verify reasoning, skills that are foundational to responsible AI use across learning and work.

When used thoughtfully, LLMs can function as facilitators, challengers, and reflective partners, supporting practice-based learning experiences that emphasize reasoning, decision-making, and reflection. Moving beyond quizzes and toward human-centered, practice-based learning activity design allows L&D professionals to harness AI's capabilities while keeping learning firmly grounded in human expertise and intent.