Have you ever thought you had a well-designed course or class activity only to discover certain aspects that did not go as planned? When I first taught a graduate course on human anatomy, students told me, “I didn’t know where to start with all the resources—so I just didn’t.” The next year, I streamlined everything into a single textbook, only to hear, “There’s too much information in here, and some of it doesn’t apply to what I’m wanting to know.” Now, with a customized artificial intelligence (AI) agent that delivers concise, targeted answers and page references, I’m finally seeing students engage with confidence. Every iteration of how I provide resources has taught me something new about managing my students’ cognitive load.
Cognitive load refers to the amount of mental effort required to process information and make decisions (Sweller, 1988). John Sweller developed the Cognitive Load Theory (CLT), which has since been expanded to distinguish between three distinct types of cognitive load that play a crucial role in how we learn: intrinsic, extraneous, and germane.
Intrinsic cognitive load is the mental effort associated with the inherent complexity of the material itself. For example, teaching graduate students about the intricate structures of human anatomy naturally demands a high level of mental processing—there’s no way to make the content less complex. Similarly, learning a new language or solving advanced math problems comes with a built-in level of difficulty that can’t be avoided.
Extraneous cognitive load is the mental effort required from the way the information is presented, rather than the content itself. Poorly organized slides, distracting visuals, or extraneous text can increase extraneous load, making it more difficult for students to focus on the important content you’re trying to convey. For example, when learning the muscles of the body I show images that isolate the specific muscle students are trying to learn, rather than an image containing all muscles of the body. The use of clear and relevant visuals allows students to focus their attention on the critical learning objectives, while reducing unnecessary distractions to the learning process.
Germane cognitive load is the mental effort needed for integrating new information into existing knowledge—what we call schema building. This integration and construction of new knowledge promotes a deeper understanding of the content. Active learning strategies facilitating reflection, problem-solving, discussion, and kinesthetic learning all serve to increase germane cognitive load, allowing connections to be made and content to be transferred to long term memory storage. In the classroom this may look like drawing diagrams or models, explaining concepts to peers, or problem-solving through a case study.
Our goal as educators is to foster meaningful learning for our students. This means we must be intentional in designing learning experiences that prevent students from becoming overwhelmed by the quantity, complexity, or presentation of material. When students are inundated with information, they risk experiencing cognitive overload—a state where the brain is unable to effectively process information, hindering understanding and retention. Signs of cognitive overload may include frustration, blank stares, or students falling behind on tasks. To gauge whether cognitive overload is impacting learning, try a quick in-class check-in by asking questions like, “What was the most confusing part of today’s lesson?” or “Is there anything you feel overwhelmed by right now?” By actively monitoring for these signs and gathering immediate feedback, we can better tailor our teaching methods to student needs.
By identifying and responding to signs of cognitive overload, we can make informed decisions about how to adjust our instruction. One effective approach is to incorporate cognitive load effects. These effects can be thought of as active learning strategies designed to optimize working memory. For example, when introducing a new concept with a set process, such as solving math problems or analyzing research articles, one type of cognitive load effect is the worked example which allows students to “see” all the steps before attempting problems independently. Cognitive load effects can also be scaffolded as student learning advances, moving from fully worked examples to partial examples, and finally to productive failure. For advanced students, productive failure can be used, where students attempt to solve problems with minimal guidance. This approach activates prior knowledge, reveals knowledge gaps, and, with feedback, leads to deeper understanding. For more examples of cognitive load effects see Sweller et al. (2019).
When thinking about cognitive load theory and cognitive load effects it is essential to plan strategies that match student understanding. Asking students to engage in productive failure when they do not have the foundational knowledge can exceed their working memory and produce confusion and frustration. Conversely, having advanced students review fully worked examples may feel redundant and disengaging. The goal of implementing cognitive load theory is not to reduce cognitive load indiscriminately but to optimize it, focusing on the most essential aspects of student learning.
With these principles in mind, we can now explore how AI can be harnessed to further optimize student cognitive load in your classes. AI tools can generate a progression of worked examples, partially worked examples, and complex case studies, supporting the use of productive failure. Additionally, students can interact directly with AI, adapting content in real time based on their performance and feedback. For example, students can request more challenging problems or ask the AI to break down complex tasks into simpler, scaffolded steps, ensuring that instructional strategies remain aligned with their current level of understanding.
However, integrating AI with cognitive load theory requires careful consideration. While AI can increase engagement through its novelty and interaction, it may also introduce distractions if the technology is difficult to use or if its novelty overshadows the learning objectives. To avoid cognitive overload, it is essential that AI tools are purposefully integrated. When designing cognitive load effects strategies with AI, educators should ensure that the technology is essential to the learning process rather than simply an add on.
Suggestions to Integrate Cognitive Load Theory Into Your Teaching
- Use Partial Worked Examples: Utilize AI to scaffold examples so that students begin with only one missing step and gradually progress to students completing the task without a worked example.
- Progress from Group to Independent Work: AI can assist in structuring activities so students move from group work, to working in pairs, to solo problem-solving, reducing support as competence grows.
- Incorporate Formative Assessments: AI tools can quickly generate formative assessment prompts tailored to specific learning objectives or student needs. For example, AI can create an exit ticket or 1-minute paper prompt to assist the instructor in checking for understanding.
- Leverage AI for Scaffolding: AI can present questions in a sequence from objective to open-ended, allowing students to request hints and get instant feedback.
- Encourage Reflection and Collaboration: After students discuss answers in pairs, an AI chatbot can generate tailored reflection prompts based on their discussion topic or responses, nudging them to think more deeply.
- Monitor Cognitive Load with Technology: Be mindful of students’ mental workload; introduce AI tools gradually and ensure they aid, not hinder, learning.
- Personalize and Target Feedback: Use AI-generated data and student reflections to offer individualized feedback and address specific learning needs.
Experimenting with AI tools in your teaching can provide innovative avenues for engagement and assessment. However, it is important to be mindful of how these tools impact students’ cognitive load. When integrating AI into your course design, consider not only the potential benefits for personalization, but also the risk of overwhelming students with new technologies.
Next time you are developing a new course or learning performance, and it does not go as planned, perhaps an exploration of cognitive load can transform student learning in ways that your mind’s eye intended. With practice, designing with cognitive load in mind will become second nature and you will get better at maximizing student germane load while also reducing extraneous load.
Michael Kiener, PhD, CRC, is a professor at Maryville University of St. Louis in their Clinical Mental Health Counseling program. For the past 10 years he has coordinated their Scholarship of Teaching and Learning Program, where faculty participate in a yearlong program with a goal of improved student learning. In 2012 and 2024 he received the Outstanding Faculty Award for faculty who best demonstrate excellence in the integration of teaching, scholarship and/or service. He has over thirty publications including a co-authored book on strength-based counseling and journal articles on career decision making, action research, counseling pedagogy, and active and dynamic learning strategies.
Laura Landon, OTD, OTR/L, is an Assistant Professor of Occupational Therapy at Maryville University in St. Louis, Missouri. Laura earned her doctorate in occupational therapy from Washington University School of Medicine. Currently, within her educator role, she teaches graduate-level OT students within the courses of Anatomy, Biomechanics, Neuroscience, and Cognitive Evaluation and Intervention. Her scholarly interests include innovation in teaching practices, addressing challenges in higher education, and integrating technology in educational spaces.
References
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive science, 12(2), 257-285.
Sweller, J., Van Merriënboer, J., & Paas, F. (2019). Cognitive Architecture and Instructional Design: 20 Years Later. Educational Psychology Review, 31, 261 – 292. https://doi.org/10.1007/s10648-019-09465-5
