Stop Asking 'A, B, or C': Generating Complex Scenario-Based Assessments with AI

For decades, exams have relied on the familiar format of multiple-choice questions—easy to grade, easy to standardize, and easy to game. Yet, as artificial intelligence reshapes how we learn and work, this model feels increasingly outdated. The world demands problem solvers, not test-takers. It’s time for assessments that mirror reality—complex, contextual, and creative. Generative AI is emerging as the key to this transformation. By leveraging large language models (LLMs) and prompt engineering, educators and organizations can now build dynamic, scenario-based assessments that adapt to learners in real time. These tools move beyond “A, B, or C” to measure critical thinking, ethical reasoning, and situational judgment—skills that no bubble sheet can capture.

The Problem with Traditional Assessments

Traditional tests were designed for efficiency, not authenticity. They assess recall, not reasoning.

• Limited cognitive scope: Multiple-choice tests primarily measure memory and recognition rather than synthesis or application.
• Static design: Once written, questions rarely evolve. They ignore context and individual learner pathways.
• Encouragement of surface learning: Students often study to guess patterns or keywords rather than to understand concepts deeply.

As one Reddit discussion among professors noted, students are increasingly using AI tools to “cheat” not because they want shortcuts, but because the tasks themselves lack relevance. When assessments fail to connect with real-world application, learners disengage or find ways around them. The solution isn’t to ban AI—it’s to design assessments that require human judgment, creativity, and adaptability.

From Recall to Realism: The Rise of Scenario-Based Assessment

Scenario-based assessments (SBAs) immerse learners in realistic contexts, asking them to apply knowledge to solve complex problems. In medical education, simulation-based training (SBT) has already proven its value. According to research published in PMC (2024), SBT enhances decision-making, teamwork, and clinical reasoning far better than traditional exams. These same principles can be applied across disciplines: business ethics, engineering, law, or even K-12 education. Instead of asking, “Which formula applies here?”, AI-generated scenarios can ask, “You’re designing a bridge under budget constraints—how would you prioritize safety, cost, and materials?” In this environment, there’s no single correct answer—only a range of defensible decisions supported by reasoning.

How Generative AI Enables Complex Assessments

Generative AI platforms like ChatGPT, Claude, or NotebookLM can generate nuanced, context-rich scenarios that evolve dynamically. They can simulate clients, patients, or environmental conditions, adjusting the difficulty and direction based on learner responses.

1. Contextual Story Generation

AI can create multi-layered narratives that mirror real-world conditions. For instance, a business ethics assessment might begin with a supply chain dilemma and evolve as the student makes choices, introducing new stakeholders or constraints. This dynamic storytelling transforms static questions into interactive learning experiences.

2. Adaptive Complexity

AI systems can adjust difficulty in real time. If a learner demonstrates strong reasoning, the scenario can escalate—adding variables like time pressure or conflicting data. If the learner struggles, the AI can offer scaffolding or hints. This adaptive approach ensures that every assessment is both challenging and personalized.

3. Multimodal Integration

Modern AI tools can blend text, audio, and video to simulate environments. NotebookLM, for example, allows integration of podcasts, videos, and custom datasets. Imagine a student analyzing a recorded interview, identifying ethical issues, and drafting a response—all within one adaptive assessment.

4. Automated but Insightful Evaluation

LLMs can generate rubrics and evaluate open-ended responses based on reasoning patterns, not just keywords. While human oversight remains essential, AI can pre-score submissions, flag inconsistencies, and provide formative feedback. This shifts grading from mechanical correctness to qualitative insight.

The Role of Prompt Engineering

Prompt engineering—the art of designing inputs that guide AI output—is central to building effective scenario-based assessments. As discussed in r/PromptEngineering, crafting precise, structured prompts determines the depth and realism of generated content. For instance: > “You are an HR manager handling a workplace conflict between two employees. Generate a three-stage scenario where the learner must decide how to mediate, considering emotional intelligence, company policy, and legal implications.” This single prompt can yield multiple, branching scenarios, each unique yet aligned with learning objectives.

Best Practices in Prompt Design

• Define clear roles and contexts: Specify who the learner is and what goals or constraints exist.
• Incorporate uncertainty: Real-world problems rarely have perfect information—let the AI introduce ambiguity.
• Balance structure and freedom: Too rigid, and the scenario feels artificial; too loose, and it loses focus.
• Iterate and refine: Test prompts with small groups to ensure realism and fairness.

Prompt engineering turns AI into a co-designer of assessments, not just a content generator.

Real-World Applications Across Sectors

Education

Universities are beginning to integrate AI-driven simulations into coursework. Instead of final exams, students might complete a week-long case study where AI agents simulate clients or collaborators. A law student could negotiate a contract with an AI-generated counterpart that argues back. A medical student could diagnose a virtual patient whose symptoms evolve based on their questions. These experiences assess not only knowledge but also communication, ethics, and adaptability.

Corporate Training

In corporate settings, scenario-based assessments can evaluate leadership, compliance, or crisis management. An AI system could simulate a cybersecurity breach, requiring employees to coordinate responses under pressure. Because these simulations are data-driven, organizations can identify skill gaps and tailor training accordingly.

Healthcare

Simulation-based training has long been a cornerstone of medical education. AI now amplifies its reach and realism. Instead of limited, pre-programmed cases, AI can generate infinite patient variations—different ages, histories, and comorbidities—allowing continuous practice and assessment. This aligns with the PMC (2024) findings that simulation-based methods improve not only technical proficiency but also empathy and teamwork.

Public Sector and Policy

Governments and NGOs can use AI-generated scenarios to train decision-makers in crisis response, diplomacy, or ethics. For example, an AI-driven disaster management simulation could test how officials allocate resources amid conflicting priorities and incomplete data.

Ethical and Practical Considerations

While the potential is vast, AI-driven assessments raise important questions.

1. Fairness and Bias

AI models can reflect biases present in their training data. If not carefully audited, scenario outcomes could unfairly advantage or disadvantage certain groups. Continuous monitoring and human oversight are essential.

2. Transparency

Learners should know when and how AI is used in their assessment. Transparency builds trust and ensures accountability.

3. Data Privacy

Scenario-based systems often collect behavioral data. Institutions must ensure compliance with privacy regulations and ethical data use.

4. Human Oversight

AI can assist in grading and feedback, but final evaluations should involve human judgment. The goal is augmentation, not automation.

5. Risk of Over-Reliance

As Yoshua Bengio noted in his FAQ on AI risks, over-dependence on AI systems can lead to complacency and reduced critical oversight. Educators must remain active designers and evaluators, not passive users.

The Future: AI as a Partner in Assessment Design

Generative AI should not replace educators or trainers—it should empower them. As discussed in the opinion paper “So what if ChatGPT wrote it?”, AI’s greatest value lies in partnership. It can co-create, iterate, and expand possibilities, but human intention and ethics must guide the process. In the coming years, we can expect:

• AI-driven assessment platforms that integrate with learning management systems to generate personalized scenarios on demand.
• Cross-disciplinary simulations where students from different fields collaborate in shared virtual environments.
• Continuous assessment models that replace high-stakes exams with ongoing, AI-facilitated performance tracking.

These innovations will redefine what it means to “test” knowledge. Instead of measuring what learners remember, we’ll measure how they think.

Building Trustworthy AI Assessment Systems

To ensure credibility and adoption, institutions should follow a structured framework:

1. Define Learning Objectives Clearly: Start with what you want to measure—critical thinking, collaboration, ethical reasoning—and design backward.
2. Collaborate with AI Ethicists and Psychometricians: Combine technological innovation with educational validity.
3. Pilot, Evaluate, and Iterate: Use small-scale testing to refine scenarios and identify unintended biases.
4. Maintain Human Review: Keep educators in the loop for quality assurance and contextual interpretation.
5. Communicate with Learners: Explain the purpose, process, and safeguards of AI-assisted assessments.

Trust is built through transparency and consistent human oversight.

Conclusion

The age of “A, B, or C” is ending. In its place, AI offers a new paradigm—assessments that mirror the complexity of real life. By generating dynamic, scenario-based evaluations, we can measure not just what learners know, but how they reason, decide, and adapt. Generative AI, guided by thoughtful prompt engineering and ethical design, transforms assessment from a static checkpoint into a living, learning process. It’s not about replacing teachers or simplifying grading—it’s about elevating what we measure and why. If education and training are to remain relevant in an AI-driven world, our assessments must evolve too. The next generation of learners deserves more than multiple choice—they deserve meaningful choice.