Instructional Technology and Design Services

Experiential Learning

Experiential learning is a teaching approach in which students learn through direct experience, followed by reflection and application. Rather than only absorbing ideas through lectures or readings, learners engage in meaningful tasks, think critically about what happened, and apply what they’ve learned in new contexts.

The Experiential Learning Cycle

The Experiential Learning Cycle (Kolb, 1984) describes learning as a continuous, iterative process of four interconnected stages. Rather than following a strictly linear path, learners cycle through these stages repeatedly, deepening understanding with each pass. Importantly, the cycle can begin at any stage depending on the learning context.

Concrete Experience

In this stage, learners engage directly and personally with real-world scenarios—they are doing rather than watching or reading about something passively. The emphasis in this stage is on hands-on involvement that creates a shared reference point for subsequent reflection and analysis.

Examples: Simulations, role play, lab experimentation, case study analysis, field observations, internships, service learning, or hands-on workshops.

Reflective Observation

In this stage, learners step back from their experience(s) to examine it from multiple perspectives and reflect on their observations, what patterns they noticed, how it felt, and what worked or didn’t work. This deliberate pause allows learners to identify patterns and inconsistencies before drawing broader conclusions.

Examples: Guided journal or reflection prompts, structured discussions or debriefs, peer review activities, video self-review, or think-pair-share exercises.

Abstract Conceptualization

In this stage, learners move beyond describing what happened to understanding why it happened and what it means more broadly. They connect their experiences and reflections to theories, models, and frameworks, and in the process are better able to form generalizations, identify underlying principles, and integrate new knowledge with existing understanding.

Examples: Analytical essays, concept or mind-mapping activities, Socratic seminars, comparing multiple case studies, or connecting course readings to lived experience and personal contexts.

Active Experimentation

In this stage, learners plan how to apply their new understanding in fresh contexts, essentially asking, “What would I do differently next time?” They test ideas, refine approaches, and translate abstract concepts into concrete action. This often generates new experiences and restarts the cycle.

Examples: Incorporating draft feedback into a final paper or project, redesigning a prototype after a soft launch, developing action plans, simulating a new approach before implementation, or A/B testing strategies.

Through experiential learning, students engage directly with authentic problems, pause to examine their experiences, connect those insights to theory, and apply their understanding in new contexts; knowledge isn’t only retained, but becomes durable, transferable, and rooted in practice. When this cycle becomes a regular part of instruction, students ultimately move from passive recipients of information to active participants in knowledge construction, making learning feel alive and relevant. 

Experiential learning also supports authentic assessment. Instead of demonstrating knowledge through traditional exams, students demonstrate learning by solving genuine problems, creating professional deliverables, or engaging with community or industry stakeholders. When assessment mirrors the work professionals really do in their fields, students gain not just knowledge but tangible, real-world experience.

Approaches to Experiential Learning

Experiential learning can take many forms depending on discipline, course level, and specific learning goals. Below are common approaches used to structure experience and reflection.

Case-Based Learning

Case-based teaching uses real-life examples that provide students with a common problem to work through. Students analyze the case study, justify decisions, weigh any tradeoffs, and apply disciplinary reasoning. A case study can be treated as a single assignment, or it could unfold over the course as new information about that case emerges, just as it would in “real time.”

You can find peer-reviewed case studies that are ready-made in repositories like the National Center for Case Study Teaching in Science. Choose a case that aligns with a key course concept and foster authentic discussion around it.

Examples:

  • Humanities/Social Sciences: Students examine a case where a city proposes using AI surveillance cameras to reduce crime, raising concerns about privacy and equity. They must decide whether the policy should move forward and justify their reasoning using ethical frameworks from the course. New community testimony is introduced later, prompting students to reassess tradeoffs.
  • STEM: Students analyze a case involving a river contaminated by agricultural runoff, using real water-quality data to identify the cause. They propose evidence-based solutions while weighing environmental impact against local farming needs. Additional test results appear mid-unit, requiring them to refine their recommendations.
  • Business: Students explore a case where a company faces public backlash over unethical supply chain practices. They recommend a strategy that balances profitability, brand reputation, and corporate responsibility. New competitor actions emerge later, pushing students to revisit their decision.
  • Education: Students examine a classroom case in which a middle school teacher is facing disengagement and behavioral challenges from several students. They propose instructional and management strategies grounded in learning theory and culturally responsive practice. Midway through the case, new information about a student’s background shifts how they interpret the situation and adjust their response.

Examples generated in collaboration with ChatGPT 5.2

Problem-Based Learning

Problem-based learning flips the traditional sequence by starting with a complex, messy problem before students have learned the content needed to solve it. Working in small groups, they identify what they need to know, divide up research tasks, and teach each other—with the instructor serving as a guide rather than a lecturer. Research shows that ill-structured problems can drive deeper engagement than a tidy textbook chapter.

Examples:

  • Humanities/Social Sciences: Students are presented with a community conflict over removing a controversial historical monument, with no clear “right” answer. In groups, they determine what historical, cultural, and ethical knowledge they need to research before proposing a course of action. Each group teaches their findings to peers and defends a recommendation.
  • STEM: Student teams are tasked with designing a temporary emergency bridge for a community after flooding has washed out the main crossing. They must determine what engineering principles, material properties, and safety constraints they need to learn before proposing a feasible design. Groups research, test assumptions, and defend their solution while the instructor serves as a design consultant rather than delivering step-by-step instruction.
  • Business: Student teams begin with a struggling startup facing declining sales and negative customer feedback, without access to a predefined strategy model. They decide what marketing, finance, and consumer behavior knowledge they must learn to diagnose the problem. Groups research independently, teach one another, and propose a turnaround plan.
  • Education: Students are introduced to a case where a school is seeing widening achievement gaps across student groups, with limited resources and competing priorities. In small teams, they identify what they need to learn about equity, instruction, and school policy before designing interventions. They collaboratively build and justify an improvement plan, guided by instructor coaching.

Examples generated in collaboration with ChatGPT 5.2

Project-Based Learning

Project-based learning immerses students in substantial, open-ended work anchored in real needs, such as designing a prototype, building a tool, or creating something that will actually be used. This category of experiential learning also includes maker spaces, in which students build something physical, whether a device, a model, a community garden, or public artwork, or anything that engages different modes of the experiential cycle.

Examples:

  • Humanities/Social Sciences: Students partner with a local community organization to create a digital oral history archive documenting residents’ experiences around a shared social issue. They conduct interviews, curate narratives, and produce a public-facing resource that will be used beyond the course.
  • STEM: Student teams partner with a campus office or local nonprofit to design and build a functional software tool that addresses a real need—such as a volunteer coordination app or a data dashboard for tracking community impact. They gather user requirements, develop and test the application, and iterate based on stakeholder feedback. The final product is delivered for actual use, along with documentation and a deployment plan.
  • Business: Students collaborate with a small business or nonprofit to develop a full marketing and growth plan, including customer research, branding recommendations, and actionable deliverables. The final product is designed to be implemented by the organization after the course ends.
  • Education: Teacher candidates design a complete instructional unit and create classroom-ready materials (lesson plans, assessments, multimedia supports) for a partner school. Their project culminates in a resource package that practicing educators can directly use with students.

Examples generated in collaboration with ChatGPT 5.2

Community-Engaged Learning

Students partner with community organizations (i.e. schools, nonprofits, municipal agencies, cultural institutions) to co-create services or research that generates real impact. In community-engaged learning experiences, students could have the opportunities to design surveys, facilitate focus groups, and analyze real-time data. Getting students out of the classroom and into real environments creates visceral learning that readings alone can’t replicate.

Examples:

  • Humanities/Social Sciences: Students partner with a local cultural organization to study community perceptions of a public history exhibit. They design surveys and facilitate focus groups, then analyze findings to offer recommendations that inform future programming.
  • STEM: Students collaborate with a municipal agency to analyze real transportation or housing data to address a community planning challenge. They apply statistical modeling, create data visualizations, and present evidence-based recommendations to support decision-making.
  • Business: Students work with a small nonprofit to assess community awareness and donor engagement. They design and administer surveys, analyze the data, and co-develop outreach strategies based on their findings.
  • Education: Teacher candidates partner with a local school to examine standardized testing scores in a specific grade level or subject area. They gather and analyze classroom data, facilitate student feedback sessions, and co-create instructional strategies with practicing educators.

Examples generated in collaboration with ChatGPT 5.2

Simulations and Role-Play

Simulations place students in scenarios that mirror professional situations, allowing them to practice skills and make decisions in a controlled environment before facing real stakes. Emerging tools, including AI-based simulations, can support low-risk practice of professional interactions, like client conversations, diagnostic discussions, or negotiations.

Examples:

  • Humanities/Social Sciences: Students participate in a role-play simulation of a community mediation session addressing a conflict over a local policy change. They assume stakeholder roles, negotiate perspectives, and practice disciplinary reasoning in a low-stakes environment.
  • STEM: Students engage in a simulated engineering design review where a prototype has failed safety testing. In teams, they interpret technical data, propose revisions, and defend decisions as if presenting to a professional board.
  • Business: Students take part in a negotiation simulation between a company and a dissatisfied client facing contract renewal. They practice communication, decision-making, and tradeoff management while responding to evolving scenario details.
  • Education: Teacher candidates role-play a parent–teacher conference scenario involving a student struggling academically and socially. Using an AI-based or peer simulation, they practice professional communication, empathy, and problem-solving before entering real classrooms.

Examples generated in collaboration with ChatGPT 5.2

Authentic Research

Involving students in genuine research in which they are not just learning about methods but also contributing to knowledge creation. Authentic research opportunities, at the graduate or undergraduate level, build skills and confidence while also producing meaningful work.

Examples:

  • Humanities/Social Sciences: Students conduct original archival or interview-based research on a local historical issue, producing findings that contribute to a public exhibit or digital repository. Their work moves beyond practice exercises to creating new interpretations or documentation for broader audiences.
  • STEM: Undergraduates participate in a faculty-led research project analyzing real environmental or biomedical data, helping generate results that may inform a conference poster or publication. Students contribute to authentic discovery rather than working with pre-scripted lab outcomes.
  • Business: Students collaborate with an industry or nonprofit partner to investigate a real organizational challenge, collecting and analyzing primary data on consumer behavior or operational efficiency. Their research produces actionable insights the organization can use and may contribute to broader applied scholarship.
  • Education: Teacher candidates engage in classroom-based action research, studying the impact of an instructional strategy on student learning in partnership with a local school. They analyze authentic classroom data and share findings with educators to improve practice.

Examples generated in collaboration with ChatGPT 5.2

Getting Started in Your Course

You don’t need to overhaul your course to bring experiential learning into your teaching. Start small: choose one activity that fits naturally into a topic you already teach. A single class session built around a brief case study, a short simulation, or a structured reflection exercise can give you and your students a feel for the approach without requiring major redesign.

Try these entry-level activities: 

  • Turn a lecture into a problem. Instead of explaining a concept first, present students with a scenario that requires them to figure out what they need to know. Let them struggle productively before you fill in the gaps.
  • Add structured reflection. After any hands-on activity, assign a brief written reflection or facilitate a ten-minute debrief. Prompts like “What surprised you?” or “What would you do differently?” help students extract meaning from experience.
  • Simulate a conversation. Use an AI tool to let students practice a professional interaction (a client meeting, a difficult conversation, a negotiation) and then discuss what worked and what didn’t.
  • Bring in a real problem. Reach out to a local organization, a colleague in another department, or even a former student working in the field. Ask if they have a small, bounded challenge your students could tackle.

Once you’re comfortable with smaller activities, you can build toward more nuanced projects: semester-long partnerships, multi-week simulations, or student-driven research. The key is to start where you are and iterate from there.

References & Resources