Evidence map›Paper›PMID 42505478›Full record

ArticleBiomimetics (Basel, Switzerland)2026

A Multi-Level Approach to Biomimetic Design Education: Developing a Biomimetic Transfer Framework and Matrix for Design Analysis.

Ayşenur Kandemir, Turgut Kalay

Abstract read
In one paragraph

Article in Biomimetics (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Ayşenur KandemirDepartment of Interior Architecture, İstanbul Nişantaşı University, 34398 İstanbul, Türkiye.ORCID 0000-0002-9558-2984
Turgut KalayDepartment of Interior Architecture, Kütahya Dumlupınar University, 43100 Kütahya, Türkiye.ORCID 0000-0002-8532-1203

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study presents and pilot-tests the Biomimetic Design Education Framework, a structured pedagogical model developed to systematize the translation of biological knowledge into furniture design within studio-based educational contexts. Positioned as a pilot implementation, the study introduces the Biomimetic Transfer Matrix as an accompanying analytical tool for assessing the depth of biological knowledge integration in student design work. It is based on 18 student projects developed during a furniture design course, assessed through qualitative content analysis. The projects were evaluated according to four types of biomimetic transfer: formal, structural, mechanical, and functional/behavioral. Results reveal that structural transfer was the most prevalent category (38.9%), followed by functional/behavioral transfer (33.3%), formal transfer (16.7%), and mechanical transfer (11.1%). This distribution indicates that structured pedagogical guidance can successfully direct students beyond surface-level morphological imitation toward deeper principle-based biological abstraction, while also identifying mechanical and system-based transfer as areas requiring targeted curricular development. On this basis, the study presents the Biomimetic Design Education Framework and introduces the Biomimetic Transfer Matrix as an analytical tool for examining different levels of biomimetic knowledge transfer in design. Results underline the importance of structured approaches to support deeper levels of biological abstraction in design education. The findings contribute to SDG 4 (Quality Education) by advancing evidence-based approaches to biomimetic design instruction.

Indexed as

biological knowledge abstractionbiomimetic transfer matrixdesign methodologyinterior design educationtransfer depth assessment

Identifiers

PMID42505478
PMCPMC13406902

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.