2025 ASEE Annual Conference & Exposition

Abstract
As artificial intelligence (AI) continues to transform the Bioengineering Engineering industry, it is critical for biomedical engineering programs to equip students with relevant AI skills that align with industry demands. Towards understanding the needs of industry partners, the goal of this work is to survey leading biotech companies to identify the most essential AI tools and techniques that should be incorporated into the biomedical engineering curriculum. By gathering insights from industry professionals, we aim to bridge the gap between academic instruction and practical applications in areas such as machine learning, data analysis, and AI-driven automation. The survey focuses on AI tools commonly used in biotech research and development, exploring the skills that employers expect from future biomedical engineers. The results of this survey will inform curriculum design, ensuring that students graduate with the AI competencies necessary to succeed in a rapidly evolving biotech landscape. This study contributes to shaping the future of biomedical engineering education, ensuring alignment with industry advancements and preparing students for the next generation of AI-driven healthcare solutions.

Methods

In this study, we designed a survey to gather insights from leading biotechnology companies on the AI tools and techniques that are essential for biomedical engineering students. The survey aims to identify which AI-driven skills, including machine learning, data analysis, and automation, are most commonly used in the industry. We targeted professionals working in biotech research and development roles, focusing on their expectations for future biomedical engineers.

The survey consists of both qualitative and quantitative questions, designed to capture detailed information about the specific AI tools and the level of proficiency required for different industry applications. Participants are asked to rank the importance of various AI skills and tools in their day-to-day work, as well as to provide feedback on any gaps they perceive between the current educational curriculum and the demands of the biotechnology industry.

The survey will be distributed to companies in the biomedical industry, including biotechnology and medical device companies ranging from large multinational corporations to startups, ensuring a broad representation of industry perspectives. Data will be collected via an online survey instrument to facilitate broad participation, with responses analyzed using statistical methods to identify key trends and priorities for AI-related education in biomedical engineering.

Results and Discussion

At the time of writing, the survey is still in its planning phase, and data collection has not yet been completed. However, based on preliminary discussions with industry professionals, we expect to gain valuable insights into the AI tools that are most commonly used in biotechnology, such as machine learning algorithms for data analysis and AI-driven automation for laboratory processes. These findings will help inform curriculum design by ensuring that students are equipped with relevant, industry-aligned AI skills upon graduation.

One anticipated outcome is the identification of gaps between current academic instruction and practical applications in biotech. For instance, early conversations suggest that while many students may learn about basic AI techniques in their courses, they may not receive sufficient hands-on experience with specific industry tools such as TensorFlow, PyTorch, or automated data pipelines. By addressing these gaps, the survey results will help guide the development of a curriculum that is more closely aligned with the evolving demands of the biotechnology field.

Moreover, this study aims to bridge the gap between theory and practice by providing actionable recommendations for biomedical engineering programs to update their curricula. By incorporating industry feedback into curriculum design, we can better prepare students to meet the real-world challenges of AI-driven healthcare and biotechnology. We hope to share this work to help other programs also improve employability of their graduates and to contribute to the innovation and growth of the biotechnology industry.

Conclusion

This study represents an important step in aligning biomedical engineering education with the rapidly advancing field of AI in biotechnology. Through the survey of biotech professionals, we will identify the most critical AI skills that should be integrated into the curriculum, ensuring that students are prepared to succeed in an AI-driven industry. While the survey data is not yet available, we expect that the results will provide valuable insights into the specific tools and techniques that are most relevant to biotech research and development. Ultimately, this study will contribute to the development of a future-ready biomedical engineering curriculum that equips students with the AI competencies needed to thrive in a dynamic and evolving landscape.