Designing Biomimetic Learning Environments for Animal Welfare Education: A Gamified Approach

TL;DR

GamifyWELL is a gamified learning tool that uses nature-inspired principles to improve animal welfare education for students and professionals.

Contribution

The paper introduces a novel biomimetic gamification framework for animal welfare education grounded in ecological principles.

Findings

GamifyWELL shows strong usability and engagement with high uptake of optional enrichment tasks.

Learners provided positive feedback on role-specific rewards and the instructional design structure.

The framework demonstrates pedagogical promise for enhancing knowledge and practice in animal welfare.

Abstract

Animal welfare education requires pedagogical models that bridge conceptual knowledge with practice. This study presents GamifyWELL, a biomimetic, gamified learning environment for students, farmers, and veterinary technicians. Grounded in ecological principles of adaptation, diversification, and niche specialization, the design emulates how living systems evolve through feedback and cooperation. These principles were translated into an instructional model that integrates a core pathway (Pre-Test, Levels 1–4, Post-Test) with optional enrichment tasks and a role-specific Reward Marketplace. Question formats are constant across levels (MCQ, image-based, video-based) while cognitive difficulty increases, culminating in Positive Welfare scenarios. We describe the learning design structure and report preliminary implementation observations using a mixed-methods evaluation plan (pre/post knowledge assessments and engagement indicators). Results from early deployment indicate strong usability and engagement, with high voluntary uptake of enrichment tasks and positive learner feedback on role-tailored rewards; full empirical testing is in progress. Findings support the feasibility and pedagogical promise of biomimetic gamification to enhance knowledge, motivation, and intended practice in animal welfare education. GamifyWELL offers a replicable framework for nature-inspired instructional design that can be extended to allied sustainability domains.

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