Research Problem and Background

In cross-functional learning environments, students are often required to enter completely unfamiliar domains such as engineering, design, business, or psychology. However, when facing cross-domain design problems, most students encounter three recurring difficulties.

Inability to quickly understand background knowledge of a new domain

Without an understanding of the foundational context, students struggle to grasp the essence of the problem and fail to contribute meaningfully to problem framing.

Difficulty reading and interpreting academic literature

Even with AI assistance (such as ChatGPT), students may understand the words but not the underlying concepts, resulting in shallow comprehension and fragmented understanding.

Lack of understanding leads to decreased participation and reduced sense of achievement

Students cannot engage in domain-specific discussions, contribute ideas, or maintain motivation.
These issues lead to the central research problem:
Core Research Question
How can students in cross-functional teams quickly acquire and internalize domain knowledge when facing design problems?

Importance of the Problem

Direct impact on learning performance

Without sufficient domain understanding, students cannot effectively define problems, make informed decisions, or propose innovative design ideas.
Lack of understanding results in low sense of achievement and poor engagement.

Strong relationship with knowledge construction and reasoning skills

MIT’s study “Your Brain on ChatGPT” shows that students who rely solely on LLMs exhibit lower comprehension, lower brain engagement, and lower satisfaction.
Cross-domain design tasks require high levels of reasoning, making comprehension even more critical.

Essential for effective interdisciplinary collaboration

Cross-domain collaboration relies on shared language and shared conceptual structure.
If students cannot quickly establish a basic knowledge framework, they cannot communicate effectively with members from other fields.

Literature Context and Research Gap

Lai and Chang (2009)
Found that novice designers struggle to generate coherent concepts from knowledge networks.

Lo and Chang (2011)
Showed that novices lack linking patterns for metaphor displacement and conceptual transformation.

Lo, Lai, and Chang (2013) dJOE
Proposed a jigsaw-like interface for recomposing ideas but assumed participants already understood the domain content.

Chang (2004) Design Puzzles
Highlighted that design knowledge can be modularized, but novices often lack the logic to use these modules effectively.

Research Gap

Existing research focuses on concept generation and idea recomposition,
but they all assume that learners already possess sufficient domain knowledge.

In real cross-functional settings, students rarely have this prerequisite.
Therefore, a strategy is needed to help students quickly absorb and internalize new domain knowledge.

The Chameleon Learning Method (CLM)

The Chameleon Learning Method is a structured approach that helps students rapidly absorb unfamiliar domain knowledge by combining literature intake, modularization, link-node mapping, and cognitive restructuring (skin-shedding).

Process of the Chameleon Learning Method

Step 1 The Big-Bubble Framework

Before entering a new domain, students first create an outer framework by rapidly absorbing:

Foundational literature
Canonical examples
Theoretical models and domain frameworks

This forms a “big bubble” enclosing the learner’s original knowledge, similar to Chang (2004)’s design knowledge modularization.

Absorbed knowledge is broken down into nodes such as:

Concepts
Functions
Cases
Design rules
Theoretical models

Connections between nodes are formed using linking patterns such as causality, analogy, functionality, relations, or metaphors.
This corresponds to Lo and Chang’s (2011) displacement linking patterns.

Step 3 Cognitive Skin-Shedding

Once the big bubble becomes stable, small fragmented old bubbles (previous knowledge) collapse and reorganize.
Like a chameleon shedding its skin, this process restructures the learner’s internal understanding.

The new skin represents:

True comprehension of the target domain
An internalized conceptual framework
A personalized model that can be immediately applied

This is the key distinction between CLM and past studies.

Step 4 Applying the New Framework to Design Problems

After shedding the old cognitive skin, the learner can immediately apply new understanding to:

Problem framing
Design ideation
Theoretical justification
Model construction
Literature interpretation
Deep AI collaboration

Students can thus rapidly understand unfamiliar domains and generate meaningful design contributions.