CFGdegree Career Pathway Blogs

The Matilda Effect in STEM

Amber De-Terville — Thu, 05 Mar 2026 15:47:57 +0000

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The History of Overlooked Women in STEM

March 5, 2026

If you haven’t heard of the Matilda Effect by name, you have almost certainly heard of it in practice. The term describes a persistent bias in which the contributions of women in STEM are overlooked, downplayed, or credited to their male colleagues, or in some cases erased entirely.

Coined in 1993 by science historian Margaret W. Rossiter, the phrase honours suffragist Matilda Joslyn Gage, who first documented the pattern back in the 19th century. Yet the roots of the phenomenon stretch far deeper into history, woven through centuries of scientific discovery and innovation by pioneering women in STEM.

Ada Lovelace and the birth of computing

Perhaps the most well-known of these pioneers today is Ada Lovelace. Although she created what is now recognised as the world’s first computer program, the achievement was long attributed to her collaborator Charles Babbage. Despite receiving little recognition in her lifetime, Lovelace’s groundbreaking contribution has since been celebrated as a significant milestone in the history of computing and in the story of women in STEM.

But Ada is just one of many.

Trota of Salerno: Early Medicine and Women’s Health

In the 12th century, Trota of Salerno was an innovative physician, one of the first medical practitioners to document women’s health and the incorporation of exercise, good diet, low stress and cleanliness into daily living. Initially her work was credited to her husband and son and later misattributed to a male scholar. Even today, many of her discoveries remain under-credited, reflecting the long-standing challenges faced by women in STEM fields.

Nettie Stevens and the Discovery of Chromosomes

Jump ahead to the 19th century and we come across Nettie Stevens, who discovered the XY sex-determination system through her research into mealworms. Through this work, Stevens provided critical evidence for chromosomal theories of inheritance.

Thomas Hunt Morgan, a distinguished geneticist of the time, was credited with her work, and Nettie only received wider recognition in the late 1990s when she was inducted into the National Women’s Hall of Fame. Her story is another example of how contributions from women in STEM have historically been overlooked.

Rosalind Franklin and the Structure of DNA

Moving into the 20th century, we find Rosalind Franklin, a brilliant British scientist whose X-ray images were crucial to revealing the structure of DNA. Despite her pivotal work, the credit instead went to her fellow researchers Francis Crick, James Watson, and Maurice Wilkins, who used much of her work without permission or acknowledgement.

Crick, Watson, and Wilkins were awarded the Nobel Prize in 1962, while Rosalind Franklin was never nominated, a stark reminder of how the achievements of women in STEM have often gone unrecognised.

Marie Curie and Recognition in Science

This was not the only time Nobel Prizes were awarded to men for work involving women’s contributions. Back in 1903, Marie Curie was almost excluded from sharing the award with her husband due to the fact she was a woman. Fortunately, her husband insisted she be added to the nomination. Many pioneering women in STEM were not so fortunate to have someone advocate for them.

Marie Tharp and the Mapping of the Ocean Floor

The Matilda Effect also appears in geology. In the 1950s, Marie Tharp, a geologist at Columbia University, transformed raw sonar data into detailed hand-drawn maps of the Atlantic seafloor. Her work revealed a deep rift running through the mid-Atlantic ridge, evidencing a spreading ocean floor and giving the first visual proof of plate tectonics.

Initially, this work was dismissed as “girl talk” by her research partner Bruce Heezen, who believed this was beyond the realms of a female mind. But her work was gradually proved by earthquake patterns and further data, and Heezen gladly added his name to her findings in 1977.

Although Maria produced the maps that reshaped modern geology, she was barred from travelling to sea due to discriminatory policies, and her full contributions were only honoured decades later with the Hubbard Medal in 2023, recognition that came far too late for one of the most influential women in STEM.

The Matilda Effect Today

Despite progress in gender equality, bias against women in STEM remains widespread. Science, technology, engineering, and mathematics are still male-dominated fields, which continues to fuel unconscious bias favouring men in research and leadership roles.

With fewer women in senior roles, there are fewer role models, mentors, and advocates for female scientists. Papers written by women are less likely to be cited than those written by men, further diminishing their visibility, and significant funding gaps continue to limit career progression for women in STEM.

Challenging the Bias

Organisations like Code First Girls are pushing back by giving women in STEM and aspiring technologists opportunities not previously available through free education, increasing visibility and recognition, and promoting women into leadership roles. Their work is crucial for changing the narrative and supporting the next generation of women in STEM.

Why Recognition Matters

The Matilda Effect serves as a stark reminder that crediting women for their work matters. By ignoring their breakthroughs and achievements, we discourage future generations of female scientists, mathematicians, technologists, and engineers.

Women have always been innovators. Supporting and recognising women in STEM ensures that future discoveries are not hidden, overlooked, or forgotten.

We need to be the game changers, the champions, the advocates.

We need to declare our brilliance and tell the world:

No more Matildas.

Inspired by the stories of women in STEM? If you’re curious about starting your own journey in tech, explore Code First Girls’ free coding courses and see where your first step could take you.

The post The Matilda Effect in STEM appeared first on Code First Girls.

From Teaching to Tech: My Career Change with the CFGdegree

Laura Mammatt — Tue, 03 Mar 2026 13:04:35 +0000

From Teaching to Tech: My Career Change with the CFGdegree

March 3, 2026

If you had told me a few years ago that I would be working as a Senior Data Analyst at United Utilities, I would not have believed you. Like many CFG students, my journey from teaching to tech was not a straight line. It was shaped by curiosity, career changes, and the confidence to try something new.

From Biomedical Science to Rethinking My Future

I graduated with a degree in Biomedical Science, excited to begin a career in science. However, once I started working in a laboratory, I realised the reality did not match my expectations. The work was repetitive and highly structured, and while it was important, it did not challenge or inspire me in the way I had hoped. I began to reflect on what I really wanted from my career. I wanted work that felt meaningful, dynamic, and people focused. That led me to my next chapter.

Seven Years in Teaching and the Turning Point

I moved into teaching, where I spent seven years working with young people in the classroom. Teaching gave me purpose and helped me develop strong communication skills, resilience, and adaptability. It was rewarding work, and for a long time, it felt like the right place for me. After Covid, however, teaching became increasingly challenging. Behavioural issues increased, workloads grew heavier, and the emotional demands of the role became harder to manage. I found myself questioning whether this was a sustainable long term career for me. I knew I needed a change, but I did not yet know what that change would be. My journey from teaching to tech was about to begin.

Discovering Data and Falling in Love with SQL

My introduction to tech came through curiosity rather than a clear plan. I came across SQL while exploring ways to understand data more effectively and decided to give it a try. I started learning through YouTube tutorials and simple practice exercises, and very quickly, I was hooked. For the first time in a long while, learning felt exciting again. I loved the problem solving aspect of SQL and the way data could be used to answer real world questions. It gave me a sense of control and creativity that I had been missing. What started as casual learning soon became something more serious.

Finding CFG and Taking the Leap

While scrolling on TikTok one day, I came across an advert for the CFGdegree starting in Spring 2024. It immediately stood out to me. A clear pathway into tech, a supportive learning environment, and a focus on building confidence alongside technical skills. I applied, unsure if I would be accepted, but knowing I would regret not trying. When I received my acceptance, it felt like a turning point. For the first time, moving from teaching to tech felt genuinely achievable.

Learning with Confidence and Community

The CFGdegree taught me far more than technical skills. It helped me rebuild confidence in my ability to learn and grow. Coming from a teaching background, I was used to structure, but tech pushed me to become comfortable with not knowing everything straight away.

Through the CFGdegree, I learned the importance of hands-on practice, asking questions, and learning alongside others who were on similar journeys. The sense of community and encouragement made a huge difference, especially as someone transitioning from a non traditional tech background. My previous experience turned out to be a strength. Teaching helped me communicate clearly, science strengthened my analytical thinking, and the CFGdegree helped me connect those skills to real world data problems.

One Year Later: Senior Data Analyst at United Utilities

Just one year after starting the CFGdegree, I secured a role as a Senior Data Analyst at United Utilities. It still feels surreal. I now work with data every day, solving problems, uncovering insights, and supporting decisions that have real impact. The role challenges me, keeps me learning, and gives me the balance and fulfilment I was looking for in my career.

What CFG Helped Me Realise

CFG helped me understand that changing careers does not mean starting from scratch. Every role I had before contributed to where I am now. Biomedical science gave me attention to detail. Teaching gave me resilience and communication skills. The CFGdegree gave me the technical foundation and confidence to step into tech. You do not need to come from a traditional tech background to succeed. What you need is curiosity, commitment, and a supportive environment that believes in your potential.

Conclusion

If you are considering a career change but feel unsure, know that you are not alone. Many CFG students start exactly where I did, questioning what comes next. It is never too late to learn something new. Your existing skills matter. And with the right support, a career in tech is possible. Sometimes all it takes is curiosity, one SQL query, and the confidence to say yes to yourself.

The post From Teaching to Tech: My Career Change with the CFGdegree appeared first on Code First Girls.

How to tackle bias in AI: practical tips for building fair and inclusive models

Laura Mammatt — Fri, 24 Oct 2025 10:16:12 +0000

practical tips for building fair and inclusive AI models

October 24, 2025

Hello! I’m Megan, a Code First Girls ambassador and recent MSc graduate in Human Centred AI. I’m passionate about responsible AI and tackling bias so we can build fair, inclusive technology for our future.

If you’re thinking of building an AI project, whether a small side project or as part of your career, there’s one thing you can’t afford to overlook: bias.

In fact, you’ve likely experienced it without realising, whether that’s through job applications filtered through an algorithm or recommendations that aren’t your cup of tea. This is something we must learn from, grow from, and acknowledge so we can actively design against it.

When completing my masters in Human-Centred AI, my final project focused on developing a decision support tool for maternity care, with a focus on ethnic disparities. Delving deep into the world of data and fairness, I found that AI bias isn’t just a tech problem, it affects real people. More on this later…

What are the different types of bias?

A simple definition of bias, is when an AI system produces unfair outcomes that disadvantage certain groups. It’s not always intentional, it often creeps in quietly through the data or design choices.

Common types of bias

📌 Data bias: When some groups are missing or underrepresented in the data. For example, medical datasets underrepresenting ethnic minority women, meaning tools may work less accurately for them.

📌 Sampling bias: When the training sample doesn’t reflect reality. A well-known example is CV screening tools trained mostly on male candidates, which ended up downgrading women’s applications.

📌 Measurement bias: This is when the accuracy of data varies across groups. For example, facial recognition systems being less likely to recognise people from ethnic minority backgrounds or women.

All of these biased systems reinforce inequalities, and over time, it chips away at our human trust in technology.

What is fairness in an AI context?

Fairness in AI means the development and design of AI systems that support equitable treatment for all individuals and groups. Fairness is something that shouldn’t be an afterthought when designing AI systems. It’s a crucial part of design that should be considered at the start of your project.

Here are a few principles you could apply to any AI project :

Define fairness goals early. Decide early what fairness means in your context. Is it equal opportunity for across groups? Minimising disparities in error rates?
Build with diverse personas in mind. In my own project, I completed research and a thematic analysis of literature to build four diverse personas. This helped keep inclusivity front and centre.
Involve end users in testing and feedback loops. Fairness is best evaluated with the people who will actually use or be affected by the system.

Now you know what it is – how do we actually tackle it?

Steps to reduce bias in AI

1. Audit your data. Explore it – who’s missing? Is anyone underrepresented? Overrepresented?

2. Fairness toolkits. I have personally used the Fairlearn library in python, which can flag where different models show performance differences across demographic groups.

3. Apply explainability methods. Libraries like SHAP or LIME can help spot if a model is overly reliant on sensitive features.

4. Diversify your design process. If you’re designing a product or tool, include user personas with different lived experiences to challenge any blind spots.

5. Iterate, and monitor! Fairness isn’t a one off fix, models need to be revisited, tested, and updated.

Looking at a real world example: Bias in healthcare

In my MSc project, I focused on designing a decision support tool for maternity care. It took me on a deep dive into the world of bias in healthcare.

When designing my user personas, I started by researching the systemic disparities in UK maternity outcomes. The reality is stark: according to the latest MBRRACE-UK report, Black women in the UK are more than twice as likely to die during pregnancy or shortly afterwards compared to white women.

When designing a tool to try to alleviate these disparities, it is obviously important to address bias.

Here’s how I approached bias in my project:

Diverse user personas. I built four synthetic personas from a thematic analysis of literature, representing different ethnicities, languages, and healthcare experiences. This helped me design a tool that didn’t assume a one-size-fits-all patient.
Fairlearn fairness library. As mentioned above, I included Fairlearn to design a ‘fairness audit’, to trigger an alert on the tool when one group was underrepresented.
Explainability with SHAP. I wanted clinicians to not only see a risk score, but also why the tool made that prediction. This added transparency and trust, and revealed patterns in risk factors across different groups.

This project was just a prototype, but it revealed a lot. Not only that so many people have been failed by technology, but also, with the right tools, we can try our best to prevent these failures. Fairness and bias simply cannot be treated as secondary features; they are the foundation of responsible AI.

Conclusion

Bias isn’t just a technical element, it’s also about values, culture and the choices we make at every stage of our design. Even if your model outputs seem fair or neutral – question it. Be that person on your team, or within your own project, to advocate for inclusivity.

As researcher Kate Crawford reminds us, ‘histories of discrimination can live on in digital systems, and if they go unquestioned, they become part of the logic of the AI we build’. That’s why we must design with everyone in mind, monitor continuously, and never treat fairness as a checkbox.

Every small step counts, whether you’re auditing your dataset, adding diverse personas, or simply asking the hard questions, you are helping create AI that’s fair to everyone – and that’s the tech that’s worth building!

The post How to tackle bias in AI: practical tips for building fair and inclusive models appeared first on Code First Girls.

How to Stay Ahead in A Fast-Moving Tech World

Laura Mammatt — Fri, 03 Oct 2025 10:17:54 +0000

How to Stay Ahead in Tech with Simple Weekly Habits

October 3, 2025

Do you ever feel like the world of tech is ever-changing, and there is always a new trend to keep up with? One minute, you’re learning a new coding language, and the next, your friends on GitHub and LinkedIn are mastering a new tool. There is always something new in your colleague’s slack channel and it can be so easy to feel like you have already been left behind.

Only recently, I have found myself drowning in online training for work, like applying for Co-pilot licenses and trying to stay up to date with each update. I had barely wrapped my head around the newest implantation of AI tools at work before already feeling like I was behind again. If that is the same for you – you are not alone. We have all been there, and truthfully, I am sure it won’t be long until I find myself back in that position.

As part of the Code First Girls community, we always hear people – both internally and externally – asking “How do I keep up?” or “What happens if I get left behind?”. The pace of change in tech can feel so overwhelming, particularly as many of the women in CFG are mothers, full-time workers/full time students and many are even navigating a career switch! Here is the good news: staying up to date doesn’t have to mean burning out or knowing everything.

Staying current is about keeping a curious mindset and staying consistent with your research. Here are some of my top tips on how you can stay ahead, without losing the fun of it!

Why staying current matters

Technology is one of the most dynamic and future focused industries and each day, there are updates that impact technology, such as new AI tools or updates on how technology affects the environment and society.

Whether your job role is tech related, you are interested in switching, or just curious, staying up to date with tech can help you to:

Stay adaptable and desirable in your career
Spot new opportunities (and win them!)
Build confidence in yourself to contribute

For me, staying up to date and more well informed about technology has made me feel empowered. It has built my confidence in everyday conversations, opened unexpected doors and opportunities, and has helped me to build a network with people who I otherwise may have been too shy to approach.

Simple weekly habits to help you stay ahead in tech

Carrying out small, regular habits can help you to keep yourself in the loop and grow your knowledge base at your own pace.

A few tips:

1) Curate 3–5 trusted sources

Follow a few trusted sources online that deliver infographics/bitesized updates on technology. My go-tos that I love:

I am subscribed to the Code First Girls newsletter and I keep up to date more casually on instagram, LinkedIn or Tiktok @CodeFirstGirls.
I love to listen to podcasts such as CodeNewbie and The Women in Tech Show (on Spotify/Apple Podcasts) on my commute to work, or whilst doing some housework
Watching Youtube videos in your spare time or tune in to a CFG Tech Taster (a short learning course) that can be picked up in your own time and at no cost! I have completed two Tech Taster courses and have been able to keep up by just putting aside half an hour per week and building on my knowledge each time.

Tip: aim for 10–15 minutes, 3 times a week.

2) Engage with voices that matter to you

Engage with voices that matter to you! I love to follow inspiring women who are making big steps in the tech world, and I try to actively engage in online communities to help curate my algorithm to find people with similar interests.

3) Block tiny learning slots

Actively set aside learning time. If learning is something that you want to do, then set time aside to invest in yourself and your interests. Even just reading a 15-minute article at the end of the day or on your commute can make a huge difference.

If I start treating my learning time like a chore, it grates on me and starts to become a stressful activity. Now, I tend to just set aside a bit of time on a Sunday morning - even just 15 minutes over a coffee – to have a read of an article or watch a video.

You are not behind, you’re in motion

Finally, here is a reminder that a journey into the world of tech is going to be a lifelong journey. It is expanding at a rate that we cannot possibly keep up with, but isn’t that the fun of it?

My biggest difficulty with learning anything, is often the need for structure, or ‘ticking it off’ a to-do list and so, I personally found it hard to realise that there is no certain finish point in tech, which is why I have found small milestones to be so important.

For me, the best way to stay ahead, is to keep moving, just one step at a time.

If you ever fell that you are struggling, just reach out. Myself and the CFG ambassador team will be happy to help or guide on your tech journey!

Conclusion

Stay curious, don’t make it a chore.

Build small habits to learn continuously.

Use free resources which are at your disposal.

You don’t need to know everything, just show up for yourself.

If you want to share any top tips on how you can stay ahead in tech, tag @CodeFirstGirls on social media and keep building our community!

The post How to Stay Ahead in A Fast-Moving Tech World appeared first on Code First Girls.

No STEM, No Stress: How I Found My Place in Tech Without a Tech Degree

Laura Mammatt — Mon, 08 Sep 2025 15:53:33 +0000

How to get into tech without a STEM degree

September 8, 2025

Hi, I’m Skye. I’m a Technology Platform Risk Partner at SWIFT, where I focus on helping the organisation manage risk across complex cloud and engineering platforms. Before this, I worked as a Tech Strategy & Advisory Consultant at Accenture, supporting some of the world’s leading financial institutions in managing technology risks and transforming securely through cloud solutions. I studied Economics and Politics at the University of Liverpool, so my journey into tech wasn’t exactly traditional. If you’re wondering whether you belong in tech without a STEM background, trust me, you do.

Getting Started in Tech

My journey into tech started unexpectedly during COVID. With extra time at home, I enrolled in a Python Coding Kickstarter course run by Code First Girls (CFG). It began simply as curiosity, but quickly became something much bigger.

I soon joined a CFG mentorship programme hosted in partnership with Bank of America. Mentorship from senior women in tech was instrumental in building my confidence and deepening my understanding of tech careers. With their encouragement, I took part in CFG’s Big Data Challenge, run in collaboration with IBM and Emergent Alliance. My team analysed sustainability trends during COVID using Python and predictive modelling, and to our excitement, we became one of the four finalist teams.

Empowered by this success, I applied for a scholarship on an intensive 8-month data science course. The programme covered machine learning techniques, including supervised learning and computer vision. Balancing this intensive programme alongside my final year at university was challenging. Social life and leisure took a back seat, but support from friends and family kept me motivated.

This curiosity and commitment to learning led to me receiving a graduate scheme offer at Accenture despite not having any previous internship experience.

My Career Growth at Accenture and Beyond

As a consultant I had the opportunity to explore a wide range of tech domains. From Business Analyst roles at a global investment bank, modelling complex financial data to Agile Delivery Lead roles and facilitating agile methodologies for global IT modernisation programmes.

I then moved into leading technology strategy and risk at a major investment bank, where I oversaw cloud and application security strategy. My focus was on integrating security into cloud infrastructure, embedding risk management into software development processes, and leveraging AI-driven analytics to proactively address enterprise threats. Recently, I’ve taken the next step in my journey by joining SWIFT as a Technology Platform Risk Partner, where I continue to work at the intersection of cloud, engineering, and risk.

This journey hasn’t always been straightforward, but exploring various roles helped me discover my passion at the intersection of cybersecurity, cloud engineering, and risk.

Why Tech Needs More than Coders and Computer Scientists

Tech is broader than most people realise. While coding skills matter, the tech industry needs diverse skills and perspectives. UX design, product management, cybersecurity strategy, content creation, and tech governance are all crucial areas within tech that thrive from different perspectives and experiences.

Statistically, diverse teams are better for productivity. According to research by McKinsey & Company, companies with diverse teams outperform their competitors by 35%. Your non-STEM background isn't a disadvantage, it can actually be your greatest strength. My own career is proof that curiosity and the right resources can open doors into tech careers traditionally reserved for STEM graduates.

The Skills That Helped Me Break into Tech Without a Tech Degree

When I began, I worried that my lack of a technical degree would hold me back. But I soon realised that my degree in Economics and Politics had already equipped me with crucial transferable skills that are valuable in tech:

Effective communication: Translating complex information into easily understandable formats
Critical thinking and problem-solving: Approaching complex issues logically and strategically
Adaptability: Quickly learning and adjusting to new concepts and environments
Project management: Efficiently managing deadlines and resources

Chances are, your non-STEM education has provided similar skills. Highlighting and developing these transferable skills along with demonstrating technical interest is key to successfully transitioning into tech.

My Advice for Non-STEM Graduates Starting a Tech Career

Starting a tech career without a STEM background can feel overwhelming. Imposter syndrome was a significant challenge for me, especially as a woman in cybersecurity and cloud, which remain heavily male-dominated fields. It’s easy to question whether you belong when you’re frequently underestimated or face situations where your abilities are doubted.

Here’s my advice, based on what helped me:

Acknowledge your feelings: Understand that imposter syndrome is common and doesn’t reflect your capabilities. Identify what’s triggering your doubts and address them directly
Find supportive mentors: Mentorship was transformative for me. Look for mentorship programmes, including those within your personal network
Ask questions constantly: It’s okay not to know everything. Asking questions accelerates learning and shows curiosity, an asset in tech
Network intentionally: Attend tech events, join online communities, and build connections with professionals who inspire you

Getting Started: Resources and Courses to Kickstart Your Tech Journey

Taking the first step can be intimidating, but there are many accessible resources designed specifically for non-STEM individuals. Here are some excellent starting points:

CFG Tech Tasters: Short courses providing foundational knowledge across key tech topics
CFG Kickstarter Courses: Deeper dives into specific skills such as Python, data, and web development
Professional networking: LinkedIn groups and online forums are excellent for networking and learning from industry professionals

Start today by choosing just one action. Whether that is signing up for a CFG course, sending a connection request to someone who inspires you or exploring a tech topic that intrigues you. Small actions add up quickly.

Conclusion

Reflecting on my journey, I’m incredibly grateful that my path into tech wasn’t conventional. My Economics and Politics background became my differentiator rather than my disadvantage. I have been able to thrive due to my diverse perspectives and skills.

If you’re doubting yourself because you don’t have a STEM degree, let my story reassure you: tech is absolutely for you. Your unique experiences and perspectives are precisely what the industry needs.

The post No STEM, No Stress: How I Found My Place in Tech Without a Tech Degree appeared first on Code First Girls.

How to use AI ethically in education

Laura Mammatt — Wed, 03 Sep 2025 13:55:04 +0000

How to use AI ethically in education

September 3, 2025

Hi there! I’m Yasmin and I completed the CFG Data Science Degree and a Masters in AI and Machine Learning in Spring 2025, all while working full time as a graduate engineer at Ford Motor Company. During that time, I discovered a real passion for data, wanted to grow my skillset and found Code First Girls to be the perfect pathway.

My time in tech has opened my eyes to just how quickly AI is evolving and how deeply it touches our lives. One of the most exciting, and challenging, areas is education. More students than ever are experimenting with using AI in education to support their learning, but it also raises important questions about ethics, responsibility and confidence in the classroom.

Why AI and education matter now

AI has developed rapidly in recent years, with Large Language Models (LLMs) like ChatGPT, DeepSeek and Gemini becoming increasingly sophisticated. In fact, a survey from the Digital Education Council found that 86% of university students are already using AI in their studies.

There are, however, mixed opinions about the ethical use of AI in education. Some argue it undermines learning development, while others see it as an invaluable tool that can make studying more efficient, personalised and inclusive.

For the Code First Girls community, this matters because women are 20% less likely than men to use generative AI tools. As students increasingly rely on AI, what really matters is how they use it and whether women feel equally confident in doing so. Education is where confidence is built, career paths begin and AI skills for women can be developed or missed altogether.

How AI is transforming learning

Students are increasingly experimenting with AI, and there’s a clear rise in LLMs being used for assignments and homework. Sometimes that means clever prompting to help structure an essay, other times it’s building a study guide, testing knowledge with quizzes or generating revision materials. And it’s not limited to STEM, students in subjects like English, Geography and History are also embracing these tools.

However, it’s not all doom and gloom about students “cheating.” AI has the potential to support ethical, effective learning in areas like:

Intelligent tutoring: LLMs can provide personalised feedback and support i.e. personalised quizzes, feedback and revision guides

Chatbots: chatbots can offer instant clarification of tricky concepts

Summarisation & translation: AI systems can be used (either via inbuilt in lecture recordings or copying transcripts) to summarise lecture materials into concise notes, or translate them into a student’s first language

Cheating or learning? Navigating the ethics of AI

One of the biggest debates in AI in education ethics is whether using AI amounts to “cheating.” The truth is, it’s a complex and evolving question and the answer often lies in the individual intent of students. Using AI in education can be considered cheating if it’s employed to generate original work that is then submitted as one’s own, without proper attribution or understanding. For example, having an AI write an entire essay or solve complex math problems on an exam without personal effort or comprehension would generally fall under academic dishonesty, as it bypasses the learning process and misrepresents a student’s abilities.

However, using AI as a tool for learning as we’ve discussed above is a different story. The line between using a tool to learn and using a tool to avoid learning is what truly defines cheating in this new era.

Will AI replace the need for teachers?

It’s a common concern, but highly unlikely. Whilst AI can automate the daily tasks of a teacher such as grading quizzes, providing feedback on a piece of writing or delivering learning content (like explanations, visual images etc) it is highly unlikely to replace our fundamental need for a teacher.

The role of teachers extends far beyond the scope of AI. Indeed, teachers help foster critical thinking through spontaneous discussions, real time social learning, emotional support and an understanding for each students’ educational capabilities and struggles.

AI however can be an incredible assistant to teachers should they choose to utilise it. Lesson planning, marking of multiple-choice questions and other content-based learning can be developed easier and faster for teachers, allowing them the time to focus on human centric aspects of teaching. Teachers should think of AI as a guide, not an opponent. Instead, they should work with it.

Making education more inclusive with AI

So, we have established how AI can be a beneficial tool for both students and teacher. However, AI can also empower disadvantaged students and make learning more inclusive.

Financially disadvantaged students – There are a lot of costs associated to being a student. Textbooks, tutoring and school fees (student loans for university students) can put a deep strain on finance. Whilst AI can’t pay for school fees, it can reduce costs by acting as a free or low cost alternative to textbooks and tutoring. It can generate explanations, create quizzes and provide tailored learning support without extra expense.
Students with additional learning needs – AI has a unique ability to personalise learning in ways that can transform access. Visually impaired students can use text-to-speech. Students with hearing loss can benefit from speech-to-text captioning. Those with ADHD or dyslexia can use AI to structure work, simplify instructions and receive real-time feedback.

AI gives a wide range of students who might otherwise fall behind, the tools to succeed in their learning journey and allows equity between students.

Conclusion

To conclude, whilst AI can be thought to have negative consequences on the learning development of students, particularly around critical thinking or problem-solving skills, this isn’t necessarily the case. AI is already a tool students use every day, and its value depends on how responsibly it’s applied. It is ultimately the individual’s responsibility to use AI in a way that honours educational guidelines and avoids academic dishonesty. Teachers may not be able to prevent students turning to AI, but there are safeguards such as AI trackers for written work that help protect integrity.

Regardless, AI as a whole is extremely valuable to the student learning experience. Its abilities should be promoted, especially for disadvantaged students, where it can support progress and provide access to resources that might otherwise be out of reach. Likewise, teachers can use AI to free up time for deeper discussions, wellbeing, and the human aspects of learning that no machine can replicate.

I deeply encourage the use of AI in education, not as a shortcut, but as a skill worth learning in itself. Even understanding how AI works can open up new perspectives on problem-solving and critical thinking.

For the Code First Girls community, this matters now more than ever. Women are still underrepresented in AI roles and less likely to use AI tools, yet the skills gained today will shape the careers of tomorrow. Building AI skills for women at the education stage means giving the next generation the confidence to use these tools responsibly and ethically.

This isn’t just about keeping pace with technology. It’s about ensuring women lead the way in shaping the ethical use of AI in learning and in building an inclusive, AI-enabled future.

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How I Pivoted from Agile Delivery to Product Management

Laura Mammatt — Fri, 22 Aug 2025 15:36:43 +0000

How I Pivoted from Agile Delivery to Product Management

August 22, 2025

Hi, I’m Aileen Gana, a Product Manager in fintech who loves channelling my entrepreneurial spirit into building innovative solutions both at work and in my personal projects. When I’m not crafting product strategies, you’ll usually find me on the golf course, tennis court or padel court. I also share my latest adventures and product insights on Instagram @aileen.builds.

Career pivots can feel daunting, especially when you’re venturing into uncharted territory. My journey from chemical engineer to agile delivery manager to product manager has taught me that the most rewarding paths are often the ones you never planned to take. If you’re considering a move into product management, or wondering whether your current skills could translate into this exciting field, I hope my story shows that with curiosity, determination and the right support, any career transition is possible.

My Winding Path to Product Management

My career journey started in a completely different world. After studying chemical engineering at university, I spent three years working at a downstream refinery in South England. It was stable, well-paying work, but when COVID hit in 2020, it gave me the space to reflect on what I really wanted from my career and life.

I missed London and craved being part of a more dynamic industry. That’s when I remembered Code First Girls. I’d first encountered them in 2016 at university, where I completed a basic HTML course. During lockdown, I decided to dive back into coding, completing Python and SQL Coding Kickstarters. The experience was transformative, I had so much fun meeting other women in tech and building games and search tools that I knew I wanted to make tech my new home.

Interestingly, this wasn’t my first entrepreneurial experience. In high school, I ran an online shoe store for three years, before retiring it to start my chemical engineering degree in 2013. That early taste of business ownership and customer focus would later prove surprisingly relevant to product management.

Landing in Agile Delivery

After many interviews and a steep learning curve, I pivoted into tech as an Agile Delivery Manager at a challenger bank. The role was challenging but rewarding, and I even completed a Scrum Master certification to strengthen my foundation.

It was during this time that my interest in product management began to grow. My boss recommended “Inspired” by Marty Cagan, which is often called the bible of product management, and as I read about product thinking and customer-centric approaches, I had a lightbulb moment. Running my online shoe store had actually been a form of product management: both were about solving customer problems and delivering the best possible experience.

For four years, I worked as an Agile Delivery Manager, collaborating with product and engineering teams to deliver effectively. I focused on building sustainable team culture, keeping everyone informed about potential blockers, and managing changes in business objectives. I loved working in the background to enable my teams’ success, but I couldn’t shake the growing interest in product management.

Making the Leap into Product Management

My first real step into product management came when a senior product manager at ClearBank needed extra capacity. I jumped at the chance to work alongside them, and I immediately knew I wanted to transition fully. When a product manager role opened up, I didn’t hesitate.

Now I’m the product manager for billing – it’s internal product management, which is often overlooked, but it’s actually the perfect playground to learn and help the company grow from the foundations up. Internal products might not have the glamour of customer-facing features, but they’re crucial for business operations and offer incredible learning opportunities.

What Agile Delivery Taught Me About Product Management

My background in agile delivery has been invaluable in my product management role. Here are the key lessons that transferred seamlessly:

Culture is everything. Agile delivery taught me that with the right team culture, you can achieve anything. If you don’t know the answer to something, someone else will – you just need to find out who that is and how their knowledge can solve the problem.
Continuous learning is essential. You can do anything if you really want to – you just have to be willing to pick up new skills along the way. This growth mindset has been essential in product management.
Stakeholder management is crucial. The most important thing I learned is to keep all involved parties updated. No one likes surprises, and managing expectations proactively prevents so many problems down the line.

The Biggest Differences Between Agile Delivery and Product Management

The transition from agile delivery to product management brought some significant changes:

Backlog ownership – the biggest shift is that I’m now accountable for the product backlog, and the team looks to me for strategic direction. This felt strange at first, but having a supportive team and manager who trust and empower me has made all the difference.
Strategic thinking – while agile delivery is about ensuring smooth execution, product management requires more strategic thinking about what to build and why. I’m now responsible for understanding customer needs and translating them into a product roadmap.
Direct customer impact – in agile delivery, I was enabling others to solve customer problems. Now, I’m directly responsible for identifying and solving those problems myself.

When I moved into billing, I initially felt embarrassed that I didn't have all the answers immediately. I had to learn that it’s okay not to know everything right away. My biggest advice for new product managers is to get to know your domain deeply and lean on your colleagues. Curiosity and willingness to learn are strengths, not weaknesses.

Tips for Aspiring Product Managers

If you’re considering a move into product management, here’s what I wish I’d known:

Be genuinely curious. You need to want to solve customer problems and be creative about how you approach them. Product management is fundamentally about understanding needs and finding innovative solutions.
Remember there’s no set path. My journey from chemical engineering through agile delivery to product management proves there’s no single route into this field. Focus on asking for the right experiences and being open to opportunities.
Use your transferable skills. Whatever your background, you likely have skills that translate to product management. My entrepreneurial experience, analytical thinking from engineering, and stakeholder management from agile delivery all contribute to my effectiveness as a product manager.
Network and learn. Connect with other product managers, read industry publications, and don’t be afraid to reach out for informational interviews. The product management community is generally very supportive of newcomers.

Why I’m Excited About the Future of Product Management

I’m thrilled to be diving deep into the billing domain. Whilst I’m still learning the intricacies, I’m excited to develop the roadmap and strategy that will help our business continue to grow.

Internal products like billing might not seem glamorous, but they’re the backbone of any business. Getting to optimise and improve these foundational systems feels like having a direct impact on the company’s success from the ground up.

Beyond my day job, I’ve discovered that I’m naturally restless when it comes to building and creating. This drive has led me to continuously work on side projects – from exploring new product ideas to experimenting with different business models. I document some of these building adventures on my Instagram @Aileen.builds, where I share insights about product thinking and the entrepreneurial journey. There’s something incredibly fulfilling about applying product management principles to personal projects and seeing ideas come to life outside the corporate environment.

Conclusion

My journey from chemical engineering to agile delivery to product management has shown me that career pivots can be challenging but deeply rewarding. The key is to stay curious, keep learning, and recognise how transferable your existing skills are.

If you’re thinking about making the change, remember there’s no perfect time to make the leap, but there’s never been a better time to start preparing. Take a course, read industry blogs, volunteer for product-related projects in your current role, or simply start playing closer attention to the products you use every day. Every product manager’s story is unique, and yours could be exactly what the industry needs.

Ready to explore a career in this field? The CFGdegree offers a product management career stream designed to give you the skills and confidence to make your pivot.

The post How I Pivoted from Agile Delivery to Product Management appeared first on Code First Girls.

From Fashion to Front-End: A Creative’s Guide to Switching to a Career in Tech

Laura Mammatt — Thu, 14 Aug 2025 13:11:15 +0000

From Fashion to Front-End: A Creative’s Guide to Switching to a Career in Tech

August 14, 2025

Hi, I’m Amie, a former fashion designer turned front-end software engineer. When I tell people I used to be a menswear designer but now work in tech, it usually sparks one of two reactions: surprise or intrigue.

On the surface, moving away from designing garment collections to writing code might seem like a huge leap and some days, it really does feel that way! But there are more similarities than you might think. Both careers rely on skills such as creativity and problem-solving to make great products. It’s just that the tools I use day-to-day are now very different.

After seven years of designing menswear for high street retailers, I found myself more drawn to the websites I was browsing than the clothes I was researching. Curious to know how these sites were built, I took part in the Code First Girls Coding Kickstarter course in Web Development, which sparked a pivotal change in my professional life. It gave me the confidence to join a coding bootcamp and I landed my first role in tech at Sage as part of their graduate programme.

My Career Switch Story

Switching careers is challenging. Landing your first role in a new industry can feel overwhelming, especially when the learning curve is steep. It’s completely normal to experience feelings of self-doubt or imposter syndrome when starting something new.

I remember many negative thoughts creeping in at the time. I felt worried about starting over in my 30s, questioning whether I was technical enough to land a role. I often wondered if not having a traditional STEM background would hold me back.

But what I’ve learned is that those fears are more common than you think and they don’t have to define your potential. The more you speak to other friends and colleagues, you’ll see that many people have come from equally unconventional backgrounds too.

If you want to take on new challenges, learn more technical skills and allow your career to evolve and change as you do – go for it! It’s never too late to change direction. In fact your past experience and background might be your greatest asset.

As a career switcher, I’m sharing some of the transferable skills that have helped me thrive in tech, along with practical tips for navigating your first role in the industry.

Transferable Skills That Helped Me Build a Career in Tech

It’s true that mastering technical skills like coding is essential for an engineering role, however it’s easy to overlook how valuable your existing soft skills are. Creativity, communication, empathy and problem-solving are equally as important. Here are some of the transferable skills from my previous career that I still use everyday:

📌 User-Centered Design

My experience in fashion gave me a strong understanding of how to balance design and usability, with the end user in mind. Now, I apply that same approach to front-end development, instead swapping fabric for code and garments for digital products.

📌 Detail-Oriented

As a designer, getting the details right made all the difference. Creating accurate technical packs with precise measurements was key to successful production. Today, that same attention to detail shapes my work, through detailed code reviews and a focus on code quality.

📌 Creative Problem Solving

Fashion design taught me to think laterally and approach problems from a new angle. I now apply that same mindset to coding, tackling challenges with a fresh perspective.

📌 Adaptability & Iterative Thinking

Great products don’t happen overnight, they are shaped through prototyping, feedback and continuous improvement. This approach is key in both fashion and software development, where agile practices help teams to stay flexible, learn quickly and build solutions that truly meet the users needs.

📌 Collaboration & Communication

Building a successful product requires a strong team. In my previous role, I worked closely with buyers, merchandisers and designers to bring ideas to life. This experience taught me the value of clear communication and teamwork. Today, those same skills help me collaborate effectively with technical architects, UX designers and solution designers to create digital products.

Remember, your soft skills matter. Creativity, collaboration, and empathy are just as important as technical skills when building products that solve real problems. The industry benefits from diverse perspectives and experiences.

How to navigate your first job in Tech

Starting fresh in an industry that is constantly evolving can feel daunting. Here are some insights and tips that helped me build confidence and develop my skills as I made the leap:

📌 Don’t be afraid to ask questions

Thoughtful questions show you’re curious, engaged and eager to learn. As someone new to a role and an industry, I constantly felt out of my depth but I had to remind myself that I wasn’t expected to know everything. Asking questions was how I would ultimately grow.

📌 Track your wins

Imposter syndrome can feel unsettling at times. One habit that’s helped me is tracking my wins, both big and small, along with the challenges I’ve overcome. On tough days, when nothing seems to work (cue: console.log(“send help!”)), looking back can be a real confidence boost. Remember, growth takes time and feeling out of your depth is often a sign that you’re moving in the right direction.

📌 Get comfortable with uncertainty

One piece of advice I’ve been given is to let go of the pressure of trying to know and remember everything. While constant learning is part of the job, the real goal is to develop problem-solving strategies to enable you to seek out those answers when you need them.

📌 Make time to focus on your weaker areas

Set aside regular, dedicated time each week to work on the topics you find most challenging or feel least confident in. Progress and confidence are the results of consistent action.

📌 Build your network

Starting out in a new industry can feel tough at times but having the right people around you makes a huge difference. Whether it’s connecting with teammates, finding a mentor or joining communities like Code First Girls, surrounding yourself with people you can learn from (and lean on) can give you the support and encouragement you need as you grow in your career.

Conclusion

If you’re curious about the world of tech, start exploring! There are so many beginner-friendly classes and communities out there to get involved with. Code First Girls offers some great options to help you get started:

Explore potential career paths in tech
Take part in a Tech Taster
Complete a Coding Kickstarter Course

The post From Fashion to Front-End: A Creative’s Guide to Switching to a Career in Tech appeared first on Code First Girls.

Mastering AI Prompts for Smarter, Faster Results

Laura Mammatt — Thu, 31 Jul 2025 16:08:28 +0000

mastering AI PromptS for Smarter, Faster Results

July 31, 2025

Hi, I’m Ashruti! My journey into tech began as a career-switcher with a humanities background, drawn to the idea that technology can open doors rather than close them. Through Data Analysis training with Code First Girls, I built the foundation that led to my current role as a Data Analyst at Tasman Analytics, a data consultancy that excels in turning disorganised data into actionable business insights.

Through my time with both Code First Girls and Tasman Analytics, I’ve seen how vital it is to adapt to emerging tools, and how easy it is to feel behind. Recent research from Harvard Business School found that women are adopting AI tools like ChatGPT at significantly lower rates than men. This gap matters. AI isn’t just a buzzword, it’s rapidly becoming a core skill that shapes how we work, learn and solve problems.

The good news is that with a bit of confidence and curiosity, anyone can learn to use it well.

Why Learning AI Prompts Matters

Knowing how to write a good AI prompt isn’t just helpful, it’s empowering. It boosts productivity, unlocks creativity and gives you a sharper edge in everything from analysis to content creation. Whether you’re a total beginner or looking to improve your results, this guide will help you get more out of tools like ChatGPT, Claude and Gemini.

The digital revolution of recent years has redefined almost every aspect of modern life. At the heart of this transformation is artificial intelligence (AI), which has become a dominant force in today’s tech landscape. With AI clearly here to stay, the big question is: ‘how can we harness its power to work for us?’

Large language models (LLMs) developed by OpenAI, Anthropic, Google and others rely on human input. We’re already seeing this at scale with 71% of organisations now use generative AI in at least one area of their business. That brings us to a more refined question: “are we just writing prompts, or are we engineering them?”

As someone who’s experienced the transformative power of AI on day-to-day tasks, I believe the skill that truly unlocks its potential is effective communication. Being able to “speak AI” is quickly becoming as valuable as any technical skill.

What is ‘AI Prompt Engineering’?

Put simply, AI prompt engineering is the art of crafting clear, specific instructions that get meaningful results from AI models. Think of it like learning to speak a language. You might start with “I’m hungry”, but with fluency you might say “I’m hungry for grilled salmon with roasted vegetables and a dash of lemon.” Both get a response, but one gets you exactly what you’re looking for.

Just like learning a language, prompt engineering is about tone, context and clarity. A beginner might write, “Write SQL to help with database analysis”, while someone more fluent would say, “Write a SQL query that finds all customers who haven’t ordered in 6 months, includes their contact info, and is optimised for performance on a 2M-row table.”

Both are valid, but one delivers a better solution.

At its core, prompt engineering blends creativity with systematic thinking. You don’t need a coding background, just the ability to:

📌 Understand how AI processes information

📌 Structure your requests clearly and specifically

📌 Iterate and refine based on results

📌 Apply context and constraints to guide the AI effectively

How to get started with the C.R.E.A.T.E. Framework

To write smarter prompts, try using the C.R.E.A.T.E. framework by AI consultant Dave Birss. This approach gives your prompt structure, tone and clarity, by helping you speak directly to the AI as if you were briefing a human.

C – CHARACTER

Define the role the AI should play.

“You are a data analyst who specialises in customer retention and reporting”

R – REQUEST

Clearly state what you want the AI to do.

“I want you to create a SQL query that shows monthly active users grouped by acquisition channel”

E – EXAMPLES

Provide sample inputs, outputs or context.

“The table is called user_events and contains columns for user_id, event_type, channel and event_date”

A – ADDITIONS

Add specific preferences, e.g. style, voice, audience, constraints, etc.

“Please ensure it’s compatible with BigQuery syntax”

T – TYPE OF OUTPUT

Clarify what format or structure you want.

“Output the SQL in a code block with inline comments, no additional explanation needed”

E – EXTRAS

Include any additional info that might help.

“Assume the analysis is for a weekly team dashboard used by marketing stakeholders”

This structure works whether you’re writing SQL, drafting content, or troubleshooting a business problem.

Tips for Stronger AI Prompts

Once you’re comfortable with the basics, try these techniques to level up:

Few-Shot Learning. Provide 2-3 examples of desired outputs to establish patterns and quality standards. Instead of asking ‘write a professional email’, show the AI an example of the tone and structure you prefer, then ask it to create something similar for your specific situation.

Chain-of-Thought Prompting. Guide the AI to complete tasks step-by-step, displaying each stage and result in order. This improves accuracy and transparency, especially for data analysis or problem-solving scenarios.

However, don’t overlook the essentials. While advanced techniques are powerful, they work best when built on solid foundations:

Start with a clear structure. Begin every prompt with at least three key elements: context (what background information does the AI need?), task (what specifically do you want accomplished?) and format (how should the output be structured?).

Be specific about constraints. Include important limitations in your prompts and specify word counts, target audiences, required elements or things to avoid. The more specific you are, the more useful your results will be.

Iterate and refine! Your first prompt rarely produces perfect results. Treat prompt engineering as a conversation, if the output isn’t quite right, explain what needs adjustment and ask the AI to revise accordingly.

AI Prompting with Confidence

Beginning your prompt engineering journey may feel overwhelming, but confidence comes from practice and experimentation.

Start small, then build up. Practice writing prompts for simple and low-stake tasks, such as email drafting or spreadsheet functions. As you become comfortable with the basics, gradually tackle more complex professional challenges.

Join communities & learn from others. Connect with people and invite conversation on prompt-writing approaches. Online communities, workshops and courses provide opportunities to see how others tackle different challenges, as well as leaning on colleagues to learn from their experiences.

Keep a prompt library. Document prompts that work well for you. Build a personal collection of effective templates that you can adapt for different situations. This saves time and helps you recognise patterns in what makes prompts successful.

Embrace the learning process. Remember that prompt engineering is still an emerging field. Even experts are continuously learning and refining their approaches. Mistakes made along the way are learning opportunities, not failures.

Conclusion

AI prompt engineering isn’t just a buzzword, it’s a proven way to work smarter with AI. Studies show that when used systematically, generative AI delivers median productivity increases of 25% across professional tasks. Users report faster content creation, sharper analysis and better overall outcomes. The difference is simple: successful users approach AI with clear, structured prompts rather than leaving it to chance.

Whether you want to enhance your current role, explore new career paths or simply reduce repetitive work, learning how to prompt effectively gives you an advantage that compounds over time. Every confident AI user began as a beginner willing to experiment, iterate and learn from each result.

AI is here to amplify human creativity and problem-solving, not replace them. By starting small, refining your approach and building confidence, you can harness AI to save time, think faster and create better outcomes than you imagined.

Ready to Begin?

Expand your prompt power by reading articles by experts and bookmark this post to use as reference material.
Explore Code First Girls’ Tech Taster classes to build foundational skills in a supportive environment designed for individuals without traditional technical backgrounds.
Discover Tasman Analytics, an expert-led data analytics agency who help fast-growing companies turn emerging technologies into competitive advantages by clarifying data strategy, filling critical skill gaps and building internal capabilities for long-term independence.

The post Mastering AI Prompts for Smarter, Faster Results appeared first on Code First Girls.

LGBTQ+ ICONS WHO CHANGED THE WORLD OF TECH

Laura Mammatt — Wed, 16 Jul 2025 11:42:43 +0000

The future is powered by LGBTQ+ icons in tech

July 16, 2025

I’m Miranda, a full stack software engineer and Code First Girls Ambassador. Over the past two years, I’ve had the privilege of encouraging women and non-binary people to see themselves as future tech leaders. As someone who is part of the LGBTQ+ community, I know how rare and powerful it can be to see yourself represented in this space.

The tech industry, for all its innovation, hasn’t always been the most inclusive when it comes to sexual orientation and gender identity. That’s why highlighting LGBTQ+ icons in tech matters. Their contributions are immense, their stories are inspiring, and their presence reminds us that tech thrives on diversity.

This post is both a celebration and a call to action. Honouring those who paved the way, while helping us imagine a more inclusive and innovative tech future.

Historical LGBTQ+ Icons in Tech

Alan Turing (1912 - 1954)

Widely recognised as the father of computer science (New Scientist, 2025), Alan Turing played a pivotal role in cracking the Nazi Enigma code during World War II, which helped shorten the war and saved millions of lives (Hodges, 2014). He also laid the theoretical foundation for modern computing and artificial intelligence (AP News, 2023).

Despite his brilliance, Turing was persecuted for being gay, which was a crime in 1950s Britain. He was chemically castrated by the government and died by suicide two years later (Leavitt, 2006). In 2013, he received a posthumous royal pardon (Ministry of Justice, 2013), and in 2019, his face was placed on the UK’s £50 note (Center for Networked Systems, 2019). Today, he stands as both a tech hero and a reminder of how far we’ve come, as well as how far we still have to go.

Lynn Conway (1938 - 2024)

A computer scientist, electrical engineer, and transgender pioneer, Lynn Conway made groundbreaking contributions to microprocessor architecture while at IBM. When she transitioned in the late 1960s, IBM fired her; a move she only publicly revealed decades later (AP News, 2024).

Conway didn’t just bounce back, she helped revolutionise chip design with her work on VLSI (Very Large Scale Integration), a process foundational to modern computers. Later in life, she became a vocal advocate for transgender rights in academia and industry, opening doors for others to live authentically while pursuing STEM careers.

Present LGBTQ+ Icons in Tech

Tim Cook

As CEO of Apple, Tim Cook is one of the most powerful people in tech and in 2014, he became the first Fortune 500 CEO to come out as gay. In his words:

“I’m proud to be gay, and I consider being gay among the greatest gifts God has given me.” (Cook, 2014).

Under Cook’s leadership, Apple has doubled down on privacy, accessibility, and inclusion. His public identity has inspired countless LGBTQ+ individuals in business and tech, showing that you don’t have to hide who you are to lead at the highest level.

Angelica Ross

A trans woman, tech founder, and activist, Angelica Ross is the creator of TransTech Social Enterprises, an organisation that helps transgender people learn coding and find employment in tech (TransTech Social Enterprises, 2025). As a self-taught programmer, Ross turned her own barriers into pathways for others through using tech as a tool of empowerment.

She also became one of the first trans women to star on major television shows (Pose, American Horror Story), proving that tech skills and creativity can intersect in powerful ways.

Arlan Hamilton

Founder of Backstage Capital, Arlan Hamilton is a Black lesbian venture capitalist investing in founders who are often overlooked: women, people of colour, and LGBTQ+ entrepreneurs. Her mission is to “back the underestimated”, and she’s raised millions to do just that (Forbes, 2024; Delaney, 2022).

In an industry where less than 1% of VC funding goes to Black women (digitalundivided, 2022), Hamilton’s presence is nothing short of revolutionary. She’s not just changing who gets funded, she’s changing who gets to dream.

ONLY 43% OF LGBTQ+ STUDENTS FEEL SAFE IN STEM CLASSES (GLSEN, 2019).

Why Representation in Tech Matters

Representation goes beyond a feel-good metric: it actively shapes the quality of innovation, the wellbeing of teams, and the usability of products.

Diverse teams are smarter teams
According to a study by Harvard Business Review (2013), companies with diverse leadership are 45% more likely to report growth in market share and 70% more likely to enter new markets. Diverse teams outperform because they bring different life experiences, perspectives, and approaches to problem-solving (Harvard Business Review, 2013).

Inclusion drives better design
When everyone is at the table, the technology we build is more accessible, ethical, and human. LGBTQ+ people often experience the world differently, especially in relation to privacy, identity, and safety. That lived experience can inform better, fairer systems.

Visibility changes culture
When LGBTQ+ individuals are visible and celebrated in tech, it normalises their presence, as well as making it easier for others to come out and contribute fully without fear. And when LGBTQ+ students or early-career coders see role models at the top, it fuels their drive to get there.

Actions We Can Take To Break Down Barriers

There’s no one-size-fits-all solution to making tech more inclusive. But small, intentional actions can add up to massive change.

✅ Advocate for inclusive policies in your company or organisation: gender-neutral toilets, pronoun sharing, comprehensive health coverage, and LGBTQ+ employee resource groups all make a real difference.

✅ Normalize pronouns in meetings, Slack bios, and email signatures. It signals respect and reduces assumptions.

✅ Support LGBTQ+ events and networks like Out in Tech, Lesbians Who Tech, and TransTech. These spaces provide visibility, mentorship, and resources for the community.

✅ Speak up when you see bias or exclusion. Whether it’s challenging a microaggression or calling out discriminatory hiring practices, allyship means action.

✅ Mentor or uplift someone from a marginalised identity in tech. You don’t have to be an expert; just being available, encouraging, and present can make a huge impact.

Inspiring the Next Generation of LGBTQ+ in Tech

Representation starts early. Only 43% of LGBTQ+ students feel safe in their STEM classes (GLSEN, 2019). Many leave tech not because they lack ability, but because they don’t feel they belong.

We can shift that by telling stories, sharing platforms, and being the role models we wished we had. When LGBTQ+ youth see people like themselves building apps, leading teams, or founding companies, they gain permission, as well as ambition.

Whether it’s a coding bootcamp, a hackathon, or a school visit, let’s make sure the next generation knows: your identity is an asset, not a limitation.

The Future of LGBTQ+ Leadership in Tech

What could the tech industry look like if LGBTQ+ people were represented everywhere, not just as coders, but as CTOs, startup founders, investors, policymakers?

The answer is: more innovative, more just, and more powerful.

As we design tomorrow’s technologies, from AI ethics to climate tech, we need LGBTQ+ perspectives at the helm. This means they’re not just included and tolerated, but centred and celebrated.

If we want to build a world where everyone thrives, we need to build a tech ecosystem that reflects everyone. That means continuing to “change the code”; not just in what we program, but in who we program for, hire, promote, and amplify.

Conclusion

The tech industry has always been shaped by those willing to challenge the status quo through pushing boundaries, rewriting systems, and imagining new futures. LGBTQ+ pioneers like Alan Turing, Lynn Conway, Tim Cook, Angelica Ross, and Arian Hamilton have not only advanced technology, but redefined what leadership, innovation, and resilience look like.

But representation is not just about visibility, it’s about equity, inclusion, and access. It’s about ensuring that the next generation of LGBTQ+ coders doesn’t have to choose between authenticity and opportunity. Progress happens when we bring diverse perspectives in from the periphery of the tech field, instead acknowledging their foundational role in its advancement.

Whether we’re founders, students, engineers, or allies, we each have a role in building a tech culture where LGBTQ+ voices are valued and empowered, as well as heard. Because changing the code isn’t just about what we build, it’s about who gets to build it.

The post LGBTQ+ ICONS WHO CHANGED THE WORLD OF TECH appeared first on Code First Girls.

CFGdegree Career Pathway Blogs

The Matilda Effect in STEM

Content Menu

The History of Overlooked Women in STEM

Ada Lovelace and the birth of computing

Trota of Salerno: Early Medicine and Women’s Health

Nettie Stevens and the Discovery of Chromosomes

Rosalind Franklin and the Structure of DNA

Marie Curie and Recognition in Science

Marie Tharp and the Mapping of the Ocean Floor

The Matilda Effect Today

Challenging the Bias

Why Recognition Matters

From Teaching to Tech: My Career Change with the CFGdegree

Content Menu

From Teaching to Tech: My Career Change with the CFGdegree

From Biomedical Science to Rethinking My Future

Seven Years in Teaching and the Turning Point

Discovering Data and Falling in Love with SQL

Finding CFG and Taking the Leap

Learning with Confidence and Community

One Year Later: Senior Data Analyst at United Utilities

What CFG Helped Me Realise

Conclusion

How to tackle bias in AI: practical tips for building fair and inclusive models

Content Menu

practical tips for building fair and inclusive AI models

What are the different types of bias?

Common types of bias

What is fairness in an AI context?

Steps to reduce bias in AI

Looking at a real world example: Bias in healthcare

Conclusion

How to Stay Ahead in A Fast-Moving Tech World

Content Menu

How to Stay Ahead in Tech with Simple Weekly Habits

Why staying current matters

Simple weekly habits to help you stay ahead in tech

1) Curate 3–5 trusted sources

2) Engage with voices that matter to you

3) Block tiny learning slots

You are not behind, you’re in motion

Conclusion

No STEM, No Stress: How I Found My Place in Tech Without a Tech Degree

Content Menu

How to get into tech without a STEM degree

Getting Started in Tech

My Career Growth at Accenture and Beyond

Why Tech Needs More than Coders and Computer Scientists

The Skills That Helped Me Break into Tech Without a Tech Degree

My Advice for Non-STEM Graduates Starting a Tech Career

Getting Started: Resources and Courses to Kickstart Your Tech Journey

Conclusion

How to use AI ethically in education

Content Menu

How to use AI ethically in education

Why AI and education matter now

How AI is transforming learning

Cheating or learning? Navigating the ethics of AI

Will AI replace the need for teachers?

Making education more inclusive with AI

Conclusion

How I Pivoted from Agile Delivery to Product Management

Content Menu

How I Pivoted from Agile Delivery to Product Management

My Winding Path to Product Management

Landing in Agile Delivery

Making the Leap into Product Management

What Agile Delivery Taught Me About Product Management

The Biggest Differences Between Agile Delivery and Product Management

Tips for Aspiring Product Managers

Why I’m Excited About the Future of Product Management

Conclusion

From Fashion to Front-End: A Creative’s Guide to Switching to a Career in Tech

Content Menu

From Fashion to Front-End: A Creative’s Guide to Switching to a Career in Tech

My Career Switch Story

Transferable Skills That Helped Me Build a Career in Tech

How to navigate your first job in Tech

Conclusion