Dual-Platform Mushroom Cultivation for STEM Education: AI-Assisted Environmental Monitoring and Student Perceptions

Area of Science:

• Fungal Biology and Education
• Agricultural Technology
• STEM Education

Background:

• Hands-on fungal cultivation in education is limited by microbial models and fieldwork constraints.
• Experiential learning with multicellular fungi requires accessible, controlled environments.
• Bridging the gap between theoretical knowledge and practical application in mycology is crucial.

Purpose of the Study:

• To develop and evaluate a dual-platform mushroom cultivation system for STEM education.
• To integrate artificial intelligence (AI)-assisted environmental monitoring with controlled-environment agriculture (CEA).
• To support K-12 and undergraduate experiential learning in fungal biology and cultivation.

Main Methods:

• Implemented a system combining an AI-assisted automated cultivation unit and a tent-based chamber.
• Cultivated oyster mushrooms (Pleurotus spp.) and lion's mane (Hericium erinaceus).
• Utilized AI for sensor/camera-based monitoring and automated regulation of humidity, light, and airflow, alongside direct student manipulation.

Main Results:

• Students observed fungal development, managed environmental parameters, and collected data.
• Post-harvest activities connected fungal biology with food and sustainability.
• Preliminary survey data indicated increased student understanding, confidence, and engagement in mushroom cultivation.

Conclusions:

• The dual-platform system offers a practical model for integrating fungal biology, AI, and CEA into STEM curricula.
• This approach enhances experiential learning opportunities in mycology.
• The system facilitates interdisciplinary connections between biology, technology, and food science.