Bridging the AI Literacy Gap Between Higher Education and Industry

Sinead Burnett is an intern at RDW and a rising senior at Northeastern University studying Digital Communication and Media. 

When I started my internship at RDW this summer, I quickly discovered that my carefully crafted college essays and researched term papers had left me unprepared for one critical aspect of work in the real world: using artificial intelligence (AI) responsibly and effectively. While the boundaries around its use in the academic setting are somewhat fuzzy, my new colleagues at RDW are seamlessly integrating AI-powered tools into their daily workflows. They’re researching content, analyzing data, brainstorming solutions, even creating AI customer service assistants with the same casual efficiency they bring to checking their calendars.

I’m not alone. Conversations with fellow interns revealed a common theme: we had all spent years learning to work without AI, only to enter workplaces where AI literacy was not just welcomed but expected. Now, as I head into my final semesters of university, that gap feels impossible to ignore. We’re graduating into jobs where AI fluency is expected, but many students still don’t know where to start, let alone how to use it responsibly or effectively.

How do we better prepare students for a workforce that has already embraced AI while preserving the intellectual rigor of higher education?

Hesitation in higher ed

The hesitation surrounding artificial intelligence in education settings stems from legitimate concerns that reflect education’s mission to develop independent thinking and authentic learning. For many, AI still feels like a “black box,” powerful but mysterious and unpredictable.

Concerns abound: Will students use AI to cheat? Could generative AI introduce bias or misinformation into academic work? What if students rely on AI models so much that they lose the ability to think critically? Caution has resulted in a paralyzing uncertainty that leaves both educators and students navigating an increasingly blurry landscape without clear guidance.

• Many schools (97%) lack clear policies on AI use, leaving both students and instructors uncertain about what constitutes legitimate use versus academic dishonesty, resulting in a “don’t-ask-don’t-tell” culture around AI in education.
• Institutional bureaucracy and outdated assessment models slow the integration of AI technologies, causing many schools to restrict or ban its use rather than adapt curricula to embrace AI as a learning tool.
• Widespread skepticism about AI reliability is fueled by frequent “hallucinations” (fabricated information) and unreliable AI detection tools.
• Educators face a significant training and resources gap, making it difficult to encourage AI literacy and critical thinking skills in students while balancing preparing for an AI-driven world with traditional educational goals.

Acceptance in the workforce

In contrast to the higher education environment, AI tools in the workplace are enhancing human capabilities, prompting organizations to invest in upskilling and digital fluency to remain competitive.

• Up to 80% of tasks are being impacted, allowing employees to shift focus from routine work to higher-value, creative, and strategic activities.
• Forward-thinking companies are investing heavily in AI training and digital literacy programs, integrating AI tools like Microsoft Copilot and advanced CRM systems into daily workflows to boost efficiency and decision making.
• The most successful organizations recognize that competitive advantage lies in workforce augmentation, not replacement, and are transforming their operations to ensure employees can effectively collaborate with AI systems.

The impact of inaction

Research shows that three in four higher education students want AI training, but only one in four universities and colleges currently provide it. This gap between student demand and institutional supply represents a critical failure to meet student needs.

Leaving students to fend for themselves when it comes to AI use can undermine the critical thinking skills education aims to develop. When students turn to AI without proper guidance and instruction, they can use it as a shortcut rather than a partner.

The result is surface-level learning that looks impressive but lacks depth and transferability. Students who rely on AI without understanding the underlying processes are essentially training themselves to be passive consumers of information rather than active learners of knowledge. They’re developing a dependency that weakens rather than strengthens their intellectual capabilities.

In addition, the uneven adoption of AI education across schools is creating a new form of educational inequality. Students in well-resourced schools with forward-thinking administrators may receive comprehensive AI literacy training; meanwhile, students in schools that ban or ignore AI are left to navigate these technologies without support or guidance. This divide perpetuates systemic inequalities. Students who lack proper AI education will be at a significant disadvantage in college applications, job interviews, and career advancement.  

How to bridge the gap

The path forward requires a fundamental shift in how we approach AI in education. We must move from trepidation to integration, from fear to informed adoption. Educational institutions must move quickly and deliberately to close this gap before it widens further.

Here are four steps that would go a long way:

1.    Think curriculum, not contraband

Rather than treating AI tools as contraband, schools need to normalize AI as they once did with other, now everyday technology. This means integrating AI tools directly into lesson plans with structured guidance and clear expectations. Students should learn not just how to prompt AI systems, but when to use them to perform tasks, how to verify their outputs, and when traditional methods are irreplaceable. Just as we teach students to evaluate sources and use calculators appropriately, we need explicit instruction in AI literacy.

Can education and learning experts at higher ed institutions help to evolve pedagogy to integrate AI in positive and productive ways? Presumably they have the knowledge and methods to think about learning in new ways.

Leading institutions are already showing the way. Duke University, among others, now offers ChatGPT Edu to students and faculty, providing a controlled environment for learning AI integration. According to recent reporting in The New York Times, these institutional endorsements signal a broader recognition that AI education requires institutional commitment and thoughtful implementation.

2.   Lead with professional development

The success of AI integration depends heavily on educator preparation. Teachers and administrators need comprehensive professional development that goes beyond basic tool familiarity to include prompt writing, AI ethics, and misinformation detection.

Professional development should also focus on policy creation, with institutions learning from early adopters who have developed thoughtful AI guidelines. These policies need to balance academic integrity concerns with the reality of AI’s growing presence in professional contexts, to give all community members a clearer understanding of an institution’s acceptable use.

As one K–12 digital learning coach commented: “I’m riding this wave as best I can. It’s truly overwhelming for both teachers and students… Teachers are asking for tools, like AI detectors (which are ironically powered by AI), and a clear path forward… We are stuck between preparing our students for success (in a world where having AI skills will be a requirement), and teaching students how to think critically. How can we do both?”

3.   Collaborate with industry

Education institutions shouldn’t develop AI policies and curricula in isolation. Instead, they need to co-design programs with employers in relevant sectors that can provide insight into the AI skills expected in the workforce. This collaboration ensures that students build job-ready competencies rather than merely complete academic exercises.

These types of partnerships have been proven valuable in other areas of study. For example, Google, the University of Michigan, and Coursera collaborated to develop a data analytics specialization for public sector work, where the U-M online course series complements Google’s Career Certificate in data analytics. Google and the University of Michigan joined forces to design a training program that provides students with the skills and expertise they need to secure jobs in high-growth industries.

What might this look like in practice?

• Schools could form advisory committees that include representatives from tech, healthcare, finance, consulting, and other AI-driven industries to help align curricula with real-world demands.
• They might introduce courses co-taught by industry professionals, offering students hands-on exposure to current challenges and case studies.
• We could even see the development of specialized AI degree programs co-designed with leading companies, much like we’ve seen with IBM’s cloud computing collaboration with the University of the Highlands and Islands. 

4.   Reinforce responsible use

Perhaps most critically, AI education must emphasize responsible use. Students need to learn how to recognize AI bias, detect misinformation, and avoid overreliance on these tools. This isn’t just about academic integrity; it’s about developing the critical thinking skills necessary for meaningful participation in an AI-influenced world.

Responsible use education should address both the capabilities and limitations of AI systems. Everyone should understand when AI is helpful, when it’s harmful, and when human judgment is essential. This includes teaching students to maintain and enhance their critical thinking skills even as they use AI assistance.

The framework for responsible AI use should extend beyond students to include faculty and staff. Everyone in the educational ecosystem needs to understand how to use AI ethically and effectively, creating a culture of informed adoption rather than avoidance or blind acceptance.

Each of these steps requires not just policy changes, but also consistent, clear communication across the entire education ecosystem. Successful integration depends on alignment among school administrators, faculty, staff, and students.

This is where strategic collaboration becomes essential. I have seen the team at RDW combine their deep understanding of the education space with their communications expertise as they work to support institutions navigating the challenges of AI integration. 

Conclusion

The gap between AI use in education and the workforce is no longer a future concern; it’s a present challenge with real consequences. At its core, this issue is about readiness, equity, and long-term relevance. As artificial intelligence becomes an integral part of nearly every profession, students risk entering the workforce unprepared to use these tools thoughtfully and ethically, and in ways that best benefit their work. 

To build an AI-literate future workforce, we must equip teachers with the training they need, empower students to use AI responsibly, and embed these tools into learning experiences in ways that preserve critical thinking. Industry and education must act now, before the AI fluency gap grows too wide to close.