Grow with Google founder Lisa Gevelber on building AI fluency in higher education

Lisa Gevelber, founder of Grow with Google, spoke to ETIH about academic credit for AI training, changing employer expectations and how universities can build practical AI fluency without compromising academic judgment.

Lisa Gevelber, founder of Grow with Google, spoke to ETIH about AI fluency, higher education and the future of practical AI credentials

Much of higher education's initial response to generative AI has centered on plagiarism detection and the protection of academic integrity.

These concerns have not disappeared. However, universities are also confronting a different question: how can they prepare graduates for workplaces in which the ability to use AI responsibly is increasingly expected?

In the view of Lisa Gevelber, founder of Grow with Google, these issues need to be addressed together. Universities must preserve original thinking, strong writing and human judgment while also giving students practical experience of working with rapidly evolving AI tools.

ETIH spoke with Gevelber about the Google AI Professional Certificate, its formal credit recommendation from the American Council on Education, and the growth of the Google AI for Education Accelerator to more than 400 higher education institutions across all 50 US states.

That shift raises wider questions for universities: what AI fluency should look like in practice, whether faculty are equipped to teach it and how much influence technology companies should have over the skills students are expected to develop.

From experimentation to implementation

The Google AI Professional Certificate is one of the first AI credentials to receive a formal credit recommendation from the American Council on Education, known as ACE.

That recommendation gives colleges and universities a basis for considering how the certificate can fit within degree pathways, rather than treating practical AI training solely as an extracurricular activity or optional employability program.

Gevelber said the recognition matters because AI skills are becoming relevant across a much wider range of disciplines, not only courses traditionally associated with computing or technology: "Since AI fluency is becoming relevant across every field of study, not just technical roles, institutions need ways that help students build those skills as part of their education.

"The American Council on Education's (ACE) credit recommendation for the Google AI Professional Certificate offers schools a concrete way to do just that, allowing students to build cross-industry AI skills while earning degree credit."

For Gevelber, credit recognition can also widen access by making practical AI training part of a student's formal education, rather than leaving individuals to locate and complete additional courses independently.

"This credit incentive democratizes access, ensuring practical AI training isn't restricted to tech majors or left for students to find on their own," she said.

"Ultimately, it bridges the AI fluency gap so every graduate enters the workforce with both a formal degree and the practical AI skills employers now expect."

The recommendation comes as the Google AI for Education Accelerator continues to expand. Participating colleges and universities receive access to the Google AI Professional Certificate and become part of a community intended to support best practice sharing and provide updates on new developments.

With more than 400 institutions across all 50 US states joining the accelerator in less than a year, Gevelber saw the scale of adoption as evidence of a wider change: "The rapid adoption of the Google AI for Education Accelerator signals a major shift: universities are moving from AI experimentation to implementation."

She argued that institutions are increasingly focused on practical adoption: "Rather than debating AI's impact, colleges are actively equipping their communities to use it effectively. This momentum highlights a demand for practical, hands-on integration that translates classroom learning into workforce readiness across all fields."

Gevelber added: "We're focused on ensuring the next generation of graduates isn't just watching the AI shift happen, they're making AI work for them."

The accelerator is available at no cost to accredited nonprofit colleges and universities.

Different institutions, different models

The approaches taken by the three institutions reflect different priorities, from community engagement to lifelong learning and educator preparation.

For Gevelber, that flexibility is central to making AI training relevant across higher education: "These examples show that there is no one-size-fits-all model for bringing AI training into higher education. Institutions are thinking about what AI fluency should look like for their specific communities and their needs."

At the University of Virginia, the emphasis is on taking that learning beyond the classroom and into community-facing work: "Through dedicated 100-hour projects, dozens of students are using their summer breaks to build AI capacity within these small and medium-sized businesses, helping them think through how AI can support everyday needs like streamlining operations, building internal capacity, and identifying practical use cases."

The model turns the certificate into a starting point for applied experience, with students using their training to help local organizations identify where AI could support their day-to-day work.

At the University of Michigan, the certificate has been opened to a wider university community.

Gevelber explained: "Recognizing that many alumni graduated before AI became mainstream, the university offers the certificate to students, faculty, staff, and alumni through the Center for Academic Innovation, ensuring they can support their community at any point in their learning journey."

By opening the program beyond its enrolled student population, the university is treating AI fluency as part of an ongoing relationship with learners, rather than a skill reserved for those currently completing a degree.

The Texas A&M University System has applied the model differently, focusing partly on the people who will bring AI into future classrooms.

Gevelber pointed to the way training has been built into teacher preparation: "Faculty in teaching and learning have embedded AI training directly into courses that prepare future teachers, knowing how pivotal it is that they are AI-ready for their future students."

That curriculum work sits alongside the system's AI Learnathon, which gives faculty and staff practical experience with AI tools. Together, the initiatives recognize that student readiness depends partly on whether educators have the confidence to guide responsible use.

Drawing the examples together, Gevelber argued that their significance lies in how closely the training is tied to each institution's wider role:

"What these models have in common is that AI training is not being treated as a one-off course or a narrow technical skill. It is being embedded into how institutions teach, support their communities, and prepare people for work. The strongest models are practical, flexible, and connected to how people will actually use AI beyond the classroom."

Defining practical AI fluency

The term AI fluency is now common across education and workforce discussions, but universities still need to decide what sits behind it in practice. Gevelber defined it in terms of what students should be able to do: "AI fluency in a higher education context means students across all disciplines can use AI to solve problems, save time, and enhance their learning. It means knowing how to use AI to help with everyday school and professional tasks. That means understanding AI's strengths and benefits, but also knowing how to use it responsibly."

That moves the debate beyond whether students can access or operate an AI tool. It places equal weight on what they do with the response and the decisions they make next.

Gevelber emphasized the continuing role of human judgment: "A student who is AI fluent should know how to ask better questions, evaluate the response and apply human judgment."

The Google AI Professional Certificate seeks to put that principle into practice through tasks students may encounter during their studies and at work: "The certificate gives students hands-on practice using AI in the ways they will be expected to use it at work. Students practice prompting effectively, using AI to brainstorm and plan projects, summarize research and pressure test ideas, create clear messages and presentations for different audiences, analyze data and build a simple app using vibe coding. That kind of fluency has real value in the labor market."

The certificate was shaped by job-posting data and input from major employers. However, Gevelber stressed that demand is not limited to technical proficiency: "Employers are increasingly looking for people who are curious and able to work with the tools. Employers need people to have curiosity, critical thinking, creativity and judgement, attributes that colleges and universities are already great at helping foster."

Gevelber then turned to what she viewed as the remaining gap:

"The gap between what many universities are offering in AI education and what employers increasingly expect is the fluency using the tools, which students can get through practical, hands-on training with real world use cases."

Any training also needs to remain useful as individual products and capabilities change.

Rather than tying fluency to one platform, Gevelber argued for a foundation that can evolve with the technology: "This baseline level of AI fluency can travel with students as the technology changes. Because tools evolve rapidly, students need a foundational understanding of prompting, evaluating outputs, and responsible use so they can adapt."

As AI becomes more embedded in both study and work, universities are having to make their expectations more explicit.

Gevelber addressed that balance directly: "Higher education institutions should absolutely continue to emphasize original thinking, academic integrity and strong writing. Those skills are still essential, even as AI becomes more integrated into the way people learn and work. Universities need clear guidelines defining when AI use is appropriate, how students should disclose it, and where the line stands between leveraging AI as a supportive tool and outsourcing critical thinking entirely."

Those questions are beginning to influence how some assignments are designed and assessed. Instead of looking only at the finished work, educators can place greater emphasis on how students reached an answer, evaluated AI-generated material and explained their choices.

Gevelber pointed to a growing focus on the learning process: "That's why educators are redesigning assignments to focus on the learning process, prompting students to critique AI outputs, reflect on their tool usage, or use AI as a strategic starting point. Ultimately, the goal is not to eliminate AI from education, but to guide students to use it responsibly in ways that enhance, rather than replace, the critical thinking and creativity employers expect."

Faculty confidence and who defines AI fluency

Universities can make AI training available to students, but implementation also reaches the people designing courses, supporting learners and setting expectations around its use.

Gevelber placed faculty and staff development at the center of that work: "Faculty and staff training has to be a big part of this shift. It's hard to expect students to build real AI fluency if the people teaching and supporting them aren't yet feeling confident using the tools themselves.

"That doesn't mean every educator needs to become a technical expert, but they do need enough familiarity to understand how AI is changing the way students learn, work, and solve problems, and how to guide students in using it responsibly."

The Texas A&M University System Learnathon also illustrates how faculty development can be delivered at scale.

Gevelber pointed to its reach across the system: "By upskilling hundreds of faculty and staff across all 12 of its campuses, the system directly increased AI fluency among its workforce, leaving educators feeling more empowered to embed AI into their courses and confidently teach it to their students."

For Gevelber, this cannot be treated as a one-off intervention: "That kind of training is especially important because AI is moving so quickly. Faculty and staff need a solid foundation that helps them to keep adapting as the technology evolves."

She framed the question as one of guidance rather than prevention: "Students are already using AI, with or without formal guidance, so the question is really whether institutions are helping them use it thoughtfully and effectively. If universities want students to graduate with practical AI skills, educators need the ongoing support and confidence to build those skills too."

Google's response has included the Google AI Educator Series, with a curriculum developed specifically for higher education.

The involvement of a major technology company brings a different question into view. The Google AI Professional Certificate is built by Google, delivered at scale and supported by a formal ACE credit recommendation. ETIH asked whether relying on programs of this kind could give technology companies too much influence over how universities define AI fluency.

Gevelber acknowledged the concern: "It's a fair question, and AI education should not be defined by any one company or tool. The goal of the certificate is not to replace what universities do; rather it's to give them a practical, employer-informed resource they can use as they build their own approach to AI fluency."

She drew a distinction between the practical training offered through the certificate and the wider academic role retained by institutions: "Universities still bring the academic context, critical thinking, ethics and field-specific instruction, which the certificate complements by giving students hands-on practice with the kinds of AI skills employers are expecting."

That question sits alongside another: whether AI credentials will remain optional additions or become more closely integrated into degree provision.

On how far that integration should extend, Gevelber said: "The goal is not to use AI everywhere; it's to use it where it helps students learn, build confidence and prepare for work."

At the same time, she expected AI fluency to become a more established part of the higher education experience: "I do think AI fluency will become a more core part of the higher education experience, especially as these tools become more embedded in the kinds of work students are preparing to do.

"I think we'll see AI credentials become less of a separate add-on and more integrated into the broader student experience, whether through general education, career readiness programs or field-specific coursework."