2026 ASEE Annual Conference & Exposition

Contingent Instructional Scaffolding for Teaching Tissue Scaffolds Using PrePostClass Assessments

Presented at Generative AI in BME Courses

The extracellular matrix (ECM) comprises hundreds of interacting proteins and connections that together form tissue scaffolds. As the structural and signaling foundation of engineered tissues, ECM understanding is central to tissue engineering practice and design. Teaching tissue engineering is conceptually challenging, yet it has a large impact on biomedical engineering applications and clinical translation. This challenge arises in large part because tissue engineering requires students to reason about ECM composition, organization, and function as a complex, context-dependent system that directly shapes cellular behavior and tissue outcomes. A concrete and coherent instructional approach is therefore essential. Scaffolding in education provides structured support calibrated to the learner and gradually faded as competence increases, grounded in the Zone of Proximal Development (ZPD) framework. Prior scaffolded learning in tissue engineering has improved capstone design skills, scientific writing, laboratory communication, and material characterization outcomes. However, a robust scaffold that systematically teaches ECM concepts across the tissue engineering curriculum, remains missing, and this gap is particularly acute in GenAI-aware settings where out-of-class assignments may not yield reliable measures of learner progress. To address this limitation, we implemented an ECM-by-ECM instructional scaffold that organizes tissue engineering content around individual extracellular matrix proteins, with ZPD-aligned, low-stakes pre/post class formative assessments administered before and after each lecture, with instructional supports progressively removed, in an upper-division course with data collected across three offerings from 2023 to 2025. The results support a scalable scaffolding approach that aligns instructional fading with learners’ developmental trajectories and improves both recognition and application under authentic, GenAI-aware constraints. This approach is broadly applicable to concept-dense topics in interdisciplinary bioengineering education, and we release a complete adoption kit to enable rapid replication.

Authors

Prof. Reem Khojah University of California, San Diego [biography]

Reem Khojah serves as an assistant teaching professor in the Shu Chien-Gene Lay Department of Bioengineering at the University of California, San Diego. With experience in instructing bioengineering at introductory and graduate levels, she actively contributes to enhancing accessibility to research tools for undergraduate research experiences. Her primary focus is on optimizing engineering education through data-driven pre-and post-lecture formative assessments and designing AI-proof assignments. Her educational background includes a B.S. in Medical Technology, a Master's degree in Chemical and Biological Engineering from KAUST, and a Ph.D. in Bioengineering from the University of California, Los Angeles. Reem has also engaged in post-doctoral research at the University of California, Santa Cruz, and the University of California, Irvine.
Dr. Adam J. Engler University of California San Diego [biography]

Adam J. Engler serves as the Kenneth Bowles Professor and Chair of the Shu Chien-Gene Lay Department of Bioengineering at the University of California, San Diego. His research background focuses on the mechanobiology of cancer and heart disease, especially understanding the role of the extracellular matrix in disease. His educational background includes a B.S.E. in Bioengineering and a Ph.D. in Mechanical Engineering and Applied Mechanics from the University of Pennsylvania.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

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