Integrating AI literacy education into high school biology classes significantly improves students' AI knowledge while leveraging their biology background to create meaningful connections between the two domains.

Objective: The main goal of this study was to investigate the integration of AI learning in an advanced biology course, examining the interplay between students' AI learning and biology knowledge through both quantitative assessment of conceptual understanding and qualitative analysis of interdisciplinary reasoning.

Methods: The researchers conducted a concurrent triangulation mixed-method study with 37 high school students in an honors biology course. Students participated in four lessons that embedded AI learning (machine learning, artificial neural networks, and convolutional neural networks) within biology contexts over multiple class sessions. Data was collected through pre/post-tests measuring both biology and AI knowledge, fill-in-blank conceptual questions in worksheets, and open-ended questions assessing interdisciplinary reasoning. The study used both quantitative analysis (paired t-tests and multiple regression) and qualitative analysis (adapting an interdisciplinary reasoning and communication framework) to examine how students' biology knowledge affected their AI learning.

Key Findings:

• Students showed significant improvement in their overall AI knowledge after the integrated lessons
• Students' biology knowledge had a slight increase but was not statistically significant
• The relationship between biology knowledge and AI learning was positive - students' biology knowledge significantly predicted their AI knowledge
• Biology knowledge significantly impacted AI learning in Lessons 1 and 2, but not in Lessons 3 and 4
• Qualitative analysis revealed that students who transferred their prior knowledge from one domain (like biology) were more likely to offer accurate explanations in the new domain (AI)
• The learning tasks that incorporated concrete, hands-on experiences and aligned with students' existing knowledge (e.g., comparing human neurons to artificial neural networks) showed stronger connections between biology and AI learning
• Abstract and theoretical AI concepts presented in academic research articles posed comprehension challenges for high school students

Implications: The study offers valuable insights for AI education by demonstrating that:

• Embedding AI literacy within established subject areas like biology is an effective approach for introducing AI concepts
• Students' contextual knowledge plays a critical role in their ability to understand and apply AI concepts
• Pedagogical strategies such as activating prior knowledge, providing hands-on practice, and creating analogies between AI and biology concepts enhance learning
• A modular curriculum approach allows for flexibility and scalability when integrating AI topics into standard curricula
• Providing opportunities for students to explain and articulate their understanding of AI and biology concepts strengthens connections between domains
• Age-appropriate materials are crucial - complex academic papers should be converted to more accessible formats for high school students

Limitations:

• The study was conducted in a real classroom setting, making it difficult to control external variables and isolate different effects
• The pre/post-tests had a limited number of questions, with only a single question measuring biology knowledge for some lessons
• The small sample size (37 students) from an advanced-level biology course may not represent the diversity of students and performance in other courses
• Due to time constraints, some planned activities were cut short (e.g., reflections in Lesson 2)
• The small sample size may have increased the risk of Type II errors, potentially explaining why the observed increase in biology knowledge was not statistically significant

Future Directions:

• Conduct studies with larger sample sizes and more rigorous research designs to test the relationship between semantic gravity/density and AI learning outcomes
• Explore how the design of learning tasks affects students' use of interdisciplinary reasoning across written, verbal, and non-verbal behaviors
• Test the causal hypothesis between transfer and explanation in cross-disciplinary learning
• Investigate effective strategies for balancing semantic density/gravity between subject content in integrated curricula
• Develop and test age-appropriate materials for introducing complex AI applications to high school students
• Explore how different types of transfer (forward, backward, and analogical) affect student learning in integrated AI education

Title and Authors: "A Case Study of Integrating AI Literacy Education in a Biology Class" by Shenghua Zha, Marcy Maulucci Bragdon, Na Gong, Jinhui Wang, Silas Leavesley, Rachel Eaton, and Erin Bosarge.

Published On: April 7, 2025

Published By: International Journal of Artificial Intelligence in Education, Springer

This research makes a significant contribution to understanding how AI literacy can be effectively integrated into traditional subject areas. By examining both quantitative and qualitative aspects of students' learning experiences, the study reveals the importance of creating meaningful connections between AI concepts and students' existing knowledge domains. The findings suggest that when AI is presented in contexts that are familiar and concrete to students, they can better comprehend and apply AI concepts. The research provides practical guidance for educators seeking to incorporate AI literacy into their curricula, emphasizing the need for hands-on activities, analogies between domains, and opportunities for students to articulate their understanding. As AI continues to play an increasingly important role in society, this work offers valuable insights into how education can evolve to prepare students for an AI-driven future.