Artificial intelligence is changing how people learn, work and prepare for employment, creating new opportunities while also raising expectations for students and early-career professionals.
In a recent LinkedIn post, Okunola Orogun, a data scientist, AI/ML researcher and Head of Team (Computing), shared insights from a session he had with students at Ulster University’s Birmingham Campus, focusing on how students can use artificial intelligence to build a stronger competitive position in the job market.
One of the central ideas from Orogun’s discussion was that obtaining a qualification is important, but increasingly, students also need to demonstrate what they can actually do with the knowledge they have acquired.
That distinction is becoming particularly relevant as AI tools become more accessible across education and professional environments.
From using AI for answers to using AI for learning
For many students, AI has become a convenient way to explain difficult topics, generate ideas, summarise information or assist with assignments.
But Orogun’s message goes beyond simply using AI to make academic work easier.
Instead, students can use AI as a tool for learning, experimentation and practical development.
Rather than asking AI to do all the thinking, students can use it to explore unfamiliar technologies, understand complex concepts, identify gaps in their knowledge, test ideas and receive feedback while developing their own solutions.
This changes the role of AI from an answer-generating tool into something closer to a learning and productivity assistant.
The distinction matters because employers are not only interested in whether someone has access to AI. They increasingly need people who can understand a problem, make sound decisions, use appropriate tools and turn ideas into useful outcomes.
Build evidence, not just credentials
A degree, diploma or professional qualification can demonstrate that someone has completed a structured course of study.
However, a qualification does not always show the full extent of what a person can build, solve or contribute in a practical working environment.
This is where projects and portfolios can become valuable.
A student studying computer science, for example, could use AI tools to help develop a website, mobile application, data-analysis project or automation system.
Someone studying business could use AI to analyse a market, develop a business model or create a data-backed strategy.
A student interested in cybersecurity could build controlled projects that demonstrate their understanding of security concepts, while someone pursuing data science could create models, dashboards or analytical projects that showcase their technical abilities.
The important point is not simply that AI was used.
It is what the person was able to accomplish with it.
Why a portfolio can strengthen a CV
A traditional CV often tells employers where someone studied, what qualifications they obtained and where they have worked.
A portfolio can provide another layer of evidence.
Instead of simply stating that someone has experience with programming, data analysis, design, research or another technical discipline, a portfolio can demonstrate examples of that work.
For students who have limited professional experience, this can be particularly useful.
A well-documented project can show how someone approached a problem, what tools they used, what decisions they made, what challenges they encountered and what the final result looked like.
That gives recruiters and hiring managers something more concrete to evaluate.
AI can make this process more accessible by helping students research unfamiliar subjects, troubleshoot problems, generate test cases, improve documentation and explore alternative approaches.
But the student still needs to understand the work and be able to explain it.
AI should strengthen human capability, not replace it
One of the biggest mistakes students can make is treating AI as a substitute for developing their own skills.
If someone allows an AI system to complete every task without understanding the underlying process, they may produce an impressive-looking result without developing the competence required to reproduce or defend that result independently.
That can become a problem during interviews.
An employer may ask why a particular approach was chosen, how a system works, what went wrong during development or how the solution could be improved.
Being able to answer those questions requires more than knowing how to generate a prompt.
It requires understanding.
The more productive approach is therefore to use AI to accelerate learning while retaining human judgment and responsibility for the final result.
AI can become part of career preparation
AI can also be useful before a student submits their first application.
Students can use AI tools to practise interview questions, review CVs, identify gaps in their skills, simulate technical interviews, research industries and improve how they communicate their experience.
They can also use AI to identify potential project ideas based on their field of study and then develop those ideas into tangible work.
For example, rather than telling an interviewer, “I am interested in artificial intelligence,” a candidate could discuss a project they built, explain the problem it addressed, describe the tools they used and demonstrate what they learned from the process.
That creates a much stronger conversation around capability.
The competitive advantage is the combination
The changing job market does not mean qualifications have suddenly become irrelevant.
Degrees, professional certifications and other forms of formal education remain important, particularly for careers that require specific academic or professional credentials.
The shift is that qualifications increasingly exist alongside other indicators of capability.
A competitive candidate may therefore bring together several elements:
- Formal education — evidence of structured learning and subject knowledge.
- Technical skills — the ability to work with relevant tools and technologies.
- Practical experience — evidence of applying knowledge to real or realistic problems.
- Projects and portfolios — visible examples of what the candidate can produce.
- AI literacy — the ability to use AI effectively, responsibly and critically.
- Human judgment — the ability to evaluate information, make decisions and take responsibility for outcomes.
- Communication skills — the ability to explain ideas and work effectively with others.
The combination can be more powerful than relying on any single element.
Students do not have to wait until graduation
Perhaps one of the most important implications is that students do not necessarily have to wait until they graduate before building evidence of their abilities.
A student can begin developing projects during their studies.
They can document what they learn, publish selected work, contribute to open-source projects, participate in competitions, build prototypes or create solutions to everyday problems.
AI can help reduce some of the barriers involved in starting those projects, particularly when students encounter unfamiliar technical concepts.
But the goal should remain skill development rather than simply producing something that looks impressive.
A small project that a student genuinely understands may ultimately be more valuable than a sophisticated project they cannot explain.
What this means for the next generation of workers
The broader lesson from Orogun’s discussion is not that students should abandon traditional education in favour of AI.
It is almost the opposite.
Education provides the foundation. Practical work provides evidence. AI can help students move faster between the two.
As AI becomes increasingly embedded in workplaces, employers are likely to place greater value on people who can combine knowledge with practical execution.
That means students should think beyond the question of “What qualification will I get?”
They should also ask:
“What can I build with what I am learning?”
“What problems can I solve?”
“Can I demonstrate my skills?”
“Can I explain the decisions behind my work?”
“How can I use AI without allowing it to replace my own thinking?”
Those questions can help turn education from something that simply leads to a certificate into a foundation for demonstrable professional capability.
The qualification is the beginning, not the entire story
The rise of AI does not eliminate the value of degrees or other qualifications. Instead, it is changing the wider context in which those qualifications are evaluated.
For students entering an increasingly AI-enabled workforce, the strongest approach may be to combine formal education with practical skills, meaningful projects, continuous learning and responsible use of AI.
A qualification can demonstrate what you have studied.
Your projects, experience and demonstrated results can help show what you can actually do.
And AI can become one of the tools that helps you make that capability visible.
Learn the fundamentals. Build practical skills. Use AI intelligently. Create real projects. Document your work. Keep improving.
That combination may matter increasingly as employers look beyond credentials to understand how candidates can contribute in a rapidly changing workplace.
Source: Insights shared by Okunola Orogun in his LinkedIn post about his session with students at Ulster University’s Birmingham Campus.