Mohamed (Med) Kharbach is an educator, researcher, and founder of Educators Technology, a popular platform helping teachers integrate technology into their practice. He holds a Ph.D. in educational studies from Mount Saint Vincent University and has over 15 years of experience reviewing EdTech tools. His research focuses on the role of AI in education and academic research, exploring how it can enhance learning and streamline teaching. After authoring ChatGPT for Teachers: Mastering the Skill of Crafting Effective Prompts, Med is currently working on a new book about the use of AI in academic research, which will be published soon.
It’s almost three years now since the release of ChatGPT and the transformations we have witnessed so far are nothing short of radical. Historically, the impact of new technologies, especially those with a transformative potential, takes decades to fully materialize but generative AI seems to be rewriting that script. Within a very short period of time, it has infiltrated almost every aspect of our life and has reshaped how we work, teach, and learn. And this is only the beginning. As the saying goes, today’s AI might be the worst version compared to what’s coming.
One sector where the impact of generative AI is being felt most acutely is higher education. As someone deeply interested in the intersection of AI and education, I can’t help but notice a familiar pattern: each time a transformative technology emerges, it’s met with skepticism and resistance until, almost inevitably, it becomes woven into the fabric of everyday practice. From the technology of writing which was once seen as a threat to memory according to Plato to calculators which were feared for potentially harming students math skills to the Internet, smartphones, and now AI.
The common thread among these technologies is that they all sparked the same initial resistance. Higher education is following the same trajectory: initial resistance followed by a gradual shift toward acceptance. However, unlike previous technologies, generative AI (especially AI chatbots like ChatGPT, Claude, Copilot, and Gemini) caught universities off guard. It did not arrive slowly or quietly, it appeared seemingly overnight with huge capabilities that posed direct challenge to long-standing academic norms.
Just as these institutions were recovering from the disruption of the COVID-19 pandemic, they were met with a powerful AI system capable of doing some amazing cognitive feats that include generating human-like text, writing and debugging code, solving complex equations, writing songs and poetry, among others. All of these tasks were to the recent past thought to be uniquely human.
As Professor Ethan Mollick puts it, for the first time in history, we have a tool that emulates our cognitive abilities. It can generate coherent essays, research summaries, and detailed literature reviews in minutes. And just three years in, we’re already talking about the possible arrival of artificial general intelligence (AGI), not in decades, but potentially in a few years. AGI is going to have similar level of intelligence as humans or even higher! The pace of innovation has definitely broken through Moore’s law.
After the initial wave of panic, including some short-lived bans, universities are beginning to come to terms with a new reality: AI is not going anywhere and it will only get smarter and better. However, considering it’s only been a couple of years since generative AI went mainstream, the fact that universities are already engaging with it is notable. In a system where change typically moves at a glacial pace, this kind of responsiveness, despite the usual layers of bureaucracy, is unusually fast.
With the initial uncertainty behind us, the real question for higher education is no longer if AI belongs, but how to integrate it meaningfully. What we need is a mindset shift, one that recognizes AI as a valuable educational tool with the potential to enhance both teaching and learning. And for this to happen, higher education institutions should engage in designing forward-looking and data-informed programs that reflect the spirit of this new era of technology and learning. This could involve actions such as:
- Investing in infrastructure: Upgrade networks, expand access, and modernize campus tech environments.
- Training faculty and staff: Run workshops and ongoing training on AI use and integration.
- Fostering innovation: Organize AI-focused symposia, support interdisciplinary research, and create a welcoming space for experimentation with AI.
- Establishing clear AI policies: Define acceptable use, academic honesty guidelines, and expectations around citation and disclosure. Invite students into these conversations.
- Building AI literacy programs: Equip students with the skills to understand both the strengths and limits of AI tools.
- Ensuring equitable access: Guarantee that all faculty and students, regardless of background, can benefit from available tools and support.
Still, building systems and policies is just one side of the equation. On the ground, the day-to-day reality of working with AI in education calls for something just as vital: adaptability. No amount of training will make your teaching immune to the unexpected. In fact, part of what makes teaching so dynamic is precisely that unpredictability. So yes, a good part of the responsibility for a successful integration of AI in higher education rests on teachers. They need to be resourceful within the boundaries set by their institutions, adapting their strategies to accommodate new realities and rethinking how learning can unfold in this evolving landscape.
Assessment is a good example of where adaptation is essential. AI can support academic work in powerful ways, but not all students will use it ethically. Misuse will happen, just as academic dishonesty always has. No policy can fully prevent it. What educators can do, however, is respond with creativity. With a flexible mindset, it’s entirely possible to design assignments that are less vulnerable to AI shortcuts and more focused on genuine thinking. These may include oral components, iterative submissions, or collaborative work grounded in classroom interactions. The same adaptable and flexible thinking should guide all aspects of instruction. This way, we harness the value of AI without undermining the integrity of learning.
As we continue to witness the unfolding of this AI revolution, I hold a deep hope that this “coming wave” of technology, as author and CEO Mustafa Suleyman puts it, will not only accelerate scientific research but also reinvigorate how we think about education itself. AI might well be the catalyst we need to reimagine learning and begin rebuilding systems that are more flexible, relevant, and responsive to the realities students face today. But no matter how we choose to approach it, one thing should never drive our decisions: fear. It has no place in shaping our vision for the future of education, nor in guiding our response to a technology with this much potential.