By Adam Uzonyi, Programming Tutor on Superprof
Over the past 3 years there definitely has been a significant increase in the number of students specifically focused on AI-related topics This shift began on November 30, 2022, with the release of ChatGPT, which introduced an unprecedented level of quality in performing assistant tasks. Since then, governments have also started recognizing the potential of this technology, leading to new policies and initiatives. Likewise, both job market players and students have quickly realized the vast opportunities AI presents. As a result, an increasing proportion of inquiries I receive are related to AI-based software development, understanding AI technologies, or simply learning how to use them effectively.
Students are especially interested in learning how we can integrate the latest AI technologies into our lives to work more efficiently, and how we build our own AI applications. They are also passionate about training models and learn what distinguishes AI from traditional algorithmic programming.
For beginners just getting into programming, we often start with Python-based projects, where we incorporate engaging visual experiences. These projects frequently evolve into areas like image processing, face recognition, and object detection.
With my MSc and PhD students, we often work on research-focused projects, exploring cutting-edge AI applications and pushing the boundaries of innovation.
In my opinion, when someone just starts exploring AI technology, the focus shouldn’t be on a single specific area of the recent developments. Instead, the priority should be on truly understanding the fundamental logic behind this technology.
We can’t skip steps when trying to grasp a complex system. We need to approach it analytically—understanding how it works, what it can do, and how we can apply it. Only then we can move on to questions like “How can I fine-tune this to my needs?” and “How can I build my own software?” These are essential steps, and jumping straight into specialisation without this foundation can be limiting.
However, once this knowledge is in place, I believe it’s worth focusing on AI-driven visual applications—such as image and video generation, object detection, and recognition—especially with advancements like Sora AI and other emerging tools.
These graphical AI technologies are set to revolutionise industries including filmmaking, media, advertising, and cybersecurity.
However, beyond AI’s impact on the market, there is a pressing demand for legal professionals with a deep understanding of AI. Currently, there are no comprehensive regulations governing this powerful—and potentially risky—technology, introducing AI laws and policies more crucial than ever.
In the UK, London and other major cities serve as key hubs for research, universities, and large technology companies where AI technology is in daily use. As a result, most of my students tend to come from these bigger cities.
When I developed an AI education curriculum for a U.S.-based company, they told me their goal was to teach children how to use AI—not just to familiarize them with the latest technology, but also to deepen their understanding of human cognition and the process of learning. I completely share this perspective. While writing my dissertation on neural networks—a branch of AI—I gained not only technical knowledge but also valuable insights into how the human brain works, including my own, and the learning process itself.
When it comes to learning about AI, E-learning on platforms such as Superprof, offers great flexibility, allowing students to focus more on the current topic. Additionally, we can collaborate on the same platform using screen sharing and remote control, making it far more convenient than physically passing a device back and forth to work on code. The flexible schedule also gives busy students more time to engage in sports and pursue their hobbies—an essential aspect of a well-balanced life.
I believe traditional education has several fundamental issues. It’s often not project-based and lacks personalization, despite the fact that we know each person is unique with different needs and aspirations.
When I work with a new student, I take the time to understand their personality, motivation, goals, and thinking style. This allows me to create tailored learning materials that resonate with them and keep them motivated. Patience, identifying areas for improvement, and offering positive reinforcement at the right moments are essential for accelerating their learning process.
