Driving Questions That Motivate Learning
Overview
A driving question is a specific type of question that is carefully crafted to propel students toward defined learning goals. Unlike simple recall questions, driving questions challenge students to think deeply, synthesize information from multiple sources, and generate original responses grounded in course content.
Driving questions share three key characteristics. They’re complex, requiring students to synthesize information from multiple sources rather than recalling a single fact. They’re not easily Google-able—students can’t simply look up the answer. And they’re relevant to students’ experiences or future professional contexts. A driving question might ask students to debate an issue, solve a problem, or compare phenomena. In each case, the question serves as a catalyst for inquiry that unfolds over time rather than something students can answer immediately.
For example, in a nutrition course, you might ask: “What diet would you recommend for a patient who wants to lose 50 pounds while managing Type 2 diabetes?” This question drives students to explore metabolism, caloric intake, nutritional science, and the interplay of disease and behavior—all while building toward an evidence-based recommendation.
Research shows that this approach works. When students encounter questions that create genuine curiosity—what researchers call a “need to know”—they engage more deeply with course material. Recent studies confirm that inquiry-based approaches that leverage driving questions lead to meaningful improvements in critical thinking and problem-solving abilities (Antonio & Prudente, 2024; Arifin et al., 2025).
From a practical standpoint, driving questions also help organize course content. The question provides a through-line that connects individual class sessions, readings, and activities into a coherent learning experience.
In This Guide
- How do I create effective driving questions?
- What might student responses look like?
- How do I assess responses to driving questions?
- Driving questions in the age of AI
- Tips for success
- Practice in action
How do I create effective driving questions?
Start With the Learning Outcomes – Identify the major concepts or skills you want students to develop, then work backward to craft a question that requires students to use those concepts or skills to respond.
This process resembles the game show Jeopardy: you begin with the answer (the content to be learned) and derive the question from it. For instance, if you want students to understand the relationship between supply and demand, you might ask: “How would you adjust pricing strategy for this product if a competing company suddenly went out of business?”
Consider the Scope – Driving questions can frame a single class session, a unit, or an entire course. Match the complexity and scope of the question to the available time and the depth of learning you’re targeting.
Make It Meaningful – Connect the question to issues students care about or situations they’re likely to encounter in their professional lives. A question about abstract principles matters more when students can see its relevance.
Ensure It Requires Synthesis – The question should demand that students pull together information from multiple sources—readings, lectures, discussions, their own experiences—rather than relying on a single text or lecture.
What might student responses look like?
Students can respond to driving questions through various products or performances, depending on your learning goals and disciplinary context.
Products – Might include a research paper, policy recommendation, educational materials, media production, or blog post series. For example, students responding to a question about climate policy might produce a white paper for a specific governmental body.
Performances – Might include an expert interview, role play, debate, or presentation. Students exploring ethical dilemmas in healthcare might engage in a structured debate where they must defend positions different from their own views.
The format matters less than whether it allows students to demonstrate their thinking and application of course concepts. Choose formats that align with the type of reasoning the question requires.
How do I assess responses to driving questions?
Because driving questions ask students to synthesize and apply knowledge rather than reproduce it, assessment should focus on the quality of their thinking and use of evidence.
Common assessment criteria include the accurate application of course concepts, thoroughness of the response, strength of arguments and reasoning, appropriate use of credible sources, and proper citations.
A rubric can help clarify expectations.* For instance, you might assess whether students correctly apply course concepts to the situation, whether they consider multiple perspectives or counterarguments, and whether they support their position with appropriate evidence.
The key is making your criteria visible to students before they begin working. When students understand how their thinking will be evaluated, they can direct their efforts more effectively.
*For more information on rubrics, see Designing Effective Rubrics.
Driving questions in the age of AI
AI tools change the landscape for assignment design, but driving questions are actually well-suited to this new reality. Here’s why: the characteristics that make a question “driving”—complex, course-specific, requiring synthesis of multiple sources—are the same characteristics that make it difficult for AI to generate a complete, satisfactory response.
Design Questions That Require Course-Specific Application – Generic driving questions (“How should cities address homelessness?”) can yield generic AI responses. Course-specific questions (“Based on the three theoretical frameworks we’ve studied and the case studies we’ve analyzed, how would you address homelessness in Boston differently than in Seattle?”) require students to apply knowledge from your specific course content. An AI tool doesn’t have access to your class discussions or the examples you’ve worked through together.
Embed Process Into Your Expectations – Instead of asking only for a final answer, incorporate an iterative process with multiple opportunities for feedback and reflection. In addition, include process documentation into the assignment where students track how their thinking evolved, what surprised them during their research, or where they encountered conflicting evidence. This shift in emphasis from product to thinking process supports learning skills development and requires outputs that AI can’t authentically replicate.
Use AI as a Thinking Partner for Students – Some instructors explicitly encourage students to use AI tools during early stages of inquiry—generating initial ideas, identifying potential sources, or exploring different angles on the question. The key is making clear that AI-generated content represents a starting point for thinking, not a substitute for it. Students then need to test AI suggestions against course concepts, evaluate the quality of AI reasoning, and develop their own informed position. Other instructors take the opposite view, arguing that these early, generative stages of work are precisely what AI use should be withheld from, since they’re where the foundational thinking happens.
Make Course-Specific Knowledge Visible in Assessment – Your rubric can include criteria that specifically assess students’ use of course materials, class discussions, or disciplinary frameworks you’ve taught. For example: “Applies at least two theoretical frameworks from the course to analyze the case” or “References specific examples from our case study analysis sessions.” This signals that successful responses require engagement with course-specific learning.
Consider Alternatives to Traditional Papers – Driving questions can be answered through formats that are harder to outsource to AI—recorded presentations where students explain their reasoning, debates that require real-time application of knowledge, or annotated portfolios where students document their inquiry process over time.
The bottom line is that driving questions that are deeply connected to your course content and that prioritize students’ thinking processes maintain their ability to motivate student learning in an AI-enabled world. Rather than trying to AI-proof your questions, encourage students to use AI as a tool to enrich their exploration rather than a learning shortcut.
Tips for success
Share the Question Early – Give students time to live with the question and gather their thoughts. Some instructors introduce driving questions on the first day of a unit or course.
Build Scaffolding – Include a first step for students to identify what they already know and what they need to know in order to answer the question. Use iterative processes with multiple opportunities for feedback.
Create Space for Exploration – Driving questions work best when students have opportunities to research, discuss, revise their thinking, and receive feedback before producing a final response. One-shot assignments rarely allow for the depth of inquiry these questions invite.
Be Open to Unexpected Responses – Because driving questions ask students to construct original responses, you may encounter answers you didn’t anticipate. This is often a sign of genuine learning and points to a teachable moment rather than a problem to fix.
Revise Based on Experience – Pay attention to how students engage with your questions. If most students struggle to find entry points, the question may be too broad, and if responses are shallow or simple, it may be too narrow. Your observations can provide clues about how you might adjust the question or the scaffolding around it.
Check the Practice in Action page to see examples of driving questions across disciplines.
References:
Antonio, R. P., & Prudente, M. S. (2024). Effects of inquiry-based approaches on students’ higher-order thinking skills in science: A meta-analysis. International Journal of Education in Mathematics, Science and Technology, 12(1), 251–281.
Arifin, M. Z., Saputro, S., & Kamari, R. (2025). The effect of inquiry-based learning on students’ critical thinking skills in science education: A systematic review and meta-analysis. Eurasia Journal of Mathematics, Science and Technology Education, 21(2).
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