Key points:
- Students must have AI know-how for success in the future economy
- An AI primer for higher-ed leaders and educators
- Innovative education: AI and AGI shaping tomorrow’s universities
- For more on AI in education, visit eCN’s Teaching & Learning hub
Artificial intelligence (AI) usage is exploding across industries. In fact, 70 percent of companies are expected to adopt at least one form of AI technology by 2030, according to a report from McKinsey Global Institute.
To leverage AI’s exciting potential, we all need to start with understanding the fundamentals. A successful AI-driven future starts with training innovative individuals and ensuring alignment on our collective understanding of this technology. Our colleges and universities will serve as a catalyst in developing AI-savvy individuals who will create our AI-supported future.
The curriculum should start with the basics of what AI is and what it is not. From there, our engineering and physics fundamentals will help us develop better computers and more sophisticated algorithms to distribute better, faster, and more accurate AI. Beyond educating students on necessary technical skills, we must also integrate AI into all other degrees of study to encourage how the technology can be applied across all industries. We must also be reminded that some of those lessons are best learned outside of the classroom in real-world settings.
Here are five strategies to help higher education institutions prepare students for a future with AI.
1. Integrate AI skills into existing programs
I believe AI should be integrated into existing academic programs rather than offered as a standalone major. Just as there are no specific degrees for smartphone or electric vehicle engineering, AI should be embedded within disciplines like computer engineering, coding, mathematics, and data science. By highlighting AI within these contexts, colleges can attract students who are excited about exploring its innovative applications across industries.
Universities can enhance existing programs with AI-centric coursework and projects. This approach ensures that students receive a comprehensive education that includes AI, without isolating it from other crucial technological and scientific studies.
2. Align on AI’s limitations
AI is helpful for making decisions, but it’s not always reliable, so students need to be made keenly aware of its limitations to make the most of its benefits. At its core, AI operates on pattern detection: It can detect patterns in large data sets with amazing speed and accuracy, but the outcome is limited by the breadth of the data used for training. Students need to understand these limitations to foster realistic expectations and use AI effectively and responsibly. A key skill will be those who know how to assess the validity of AI results.
3. Address bias in AI
AI systems are influenced by the data they are trained on, which can carry inherent biases. Bias is all around us and can influence the outcomes of AI. The key is that bias is not necessarily negative–data can be influenced by bias intentionally or unintentionally. For example, an analyst at an international company may want to look at sales data to help predict future needs. Data biases could be set to only look at one region, or only one season of sales. Teaching that bias is not inherently negative, but a factor to be managed, will help students create fairer, more accurate AI models.
4. Explore legal and ethical implications
A well-rounded AI education must include legal and ethical considerations. For instance, if a public AI database is used to develop a new vaccine, who owns the patent on it? If I develop an AI solution for detecting cancer, how should I structure my agreements to avoid liability in the event of a misdiagnosis? What about data brokers who sell data used for AI training–who owns the data, outcomes, and liability?
Students need to be aware of issues related to intellectual property, liability, and data ownership. Integrating these topics into existing courses will help students navigate the legal landscape of AI and its applications.
5. Provide real-world experience
Practical experience is important to help students apply their knowledge. Gaining hands-on experience is the only way students can translate the concepts they’re learning in the classroom into practical, applicable use cases in the business world. Universities can collaborate with businesses and government agencies to offer internships and mentoring programs that help students develop practical skills and understand real-world applications of AI.
Educating the next generation of AI expertise is essential for meeting the demands of our tech-forward society and future business economy. Universities will play a pivotal role in educating students not only in the technical aspects of AI, but also in its ethical and societal implications. Taking a holistic approach is the key to developing well-rounded, conscientious AI professionals ready to tackle future challenges and create new opportunities.
Paul Jonas is the Technical Director of the FirePoint Innovations Center at Wichita State University and brings 45 years of aviation-related experience. With over 40 years in industry as an Aircraft Chief Engineer on multiple new commercial and military aircraft, Paul focuses now on developing new technology and capabilities for our U.S. Military and works with regulators and international standards organizations on how AI could be used in flight critical applications.
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