As a die-hard Tron fan, I recently went to watch Tron Ares in the cinema, and it stuck with me. In the Tron world, ENCOM and Dillinger Systems are rival companies competing for control of the digital future. Without spoiling the whole story, I can say that the movie shows two paths for AI: the ENCOM path, where tech helps people and opens new possibilities to improve our lives, and the Dillinger path, where power and control come first. Seeing that contrast on a giant screen made me think about how I use generative AI in my own life and why my experience has been mostly positive.
Day-to-day, GenAI, mostly large language models (AI chatbots), feels like an ENCOM tool for me. I use it to give me medical advice, clean up emails and messages, translate conversations. It summarizes long articles when I’m short on time, helps me outline essays, drafts slides and talking points. When I’m coding with RStudio, it explains errors in simple terms. And when I’m planning a trip, it helps me think through checklists. It’s like an assistant that only wants the best for you.
Part of why this stays positive is the way these GenAI tools are set up. It tries to be friendly and helpful, and it puts safety first. Harmful or abusive requests get blocked or redirected. It won’t imitate a living person’s exact voice, won’t help with dangerous instructions, and pushes me toward responsible use. That doesn’t make misuse impossible, but it does make it harder.
Of course, the Dillinger path exists in the real world, too. We see AI being built into defense, border security, and large-scale surveillance systems. Companies like Palantir and Anduril are known for powerful analytics and autonomous sensing platforms. Facial recognition firms have scraped massive image datasets. These tools can centralize power in ways that can be worrying. It’s very much like the movie’s warning: when a few actors control the Grid, ordinary Users lose agency. I’m not saying these companies are “villains,” but the direction of travel still matters.
So I set myself a simple goal: keep my use of AI on the ENCOM path. Tron Ares should remind us that AI Programs can turn dangerously powerful in the future. If we give them good goals, they can light up the city. If we don’t, the same power can bite back, with the risk of even turning against us. Kind of like Skynet, but that’s a whole other franchise.
From Print(“Hello”) to Data Analysis: My Thesis with AI-Assisted Coding
9
October
2025
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When I started my thesis, I barely remembered how Python worked. I knew what a dataset was and how to print a line or write a simple loop, but that was about it. The idea of building an entire data-science workflow seemed far beyond what I could do on my own. Yet, a few months later, I had written a full pipeline to analyze hybrid work patterns using behavioral logs, location data, and daily surveys. What made that possible was Generative AI.
ChatGPT quickly became my silent collaborator. Whenever I got stuck, I simply described what I needed: filtering AWT data by time, merging JSON files by date, or running a Mann-Whitney U-test. Within seconds, it generated structured and readable code that actually worked. It helped me clean and merge datasets, calculate metrics like active work time and task switches, and even combine GPS data with behavioral data to label each day as home or office. Suddenly, something that felt completely out of reach became manageable.
Of course, the process was not perfect. I often had to debug the AI’s mistakes, rewrite lines of code, and verify that the logic fit my data. Sometimes ChatGPT used outdated Pandas functions or made assumptions that didn’t make sense. But those moments taught me more than any tutorial could. I started to understand not just what the code was doing but why it worked that way.
Looking back, Generative AI didn’t write my thesis for me; it expanded what I was capable of. It turned Python from something intimidating into a tool I could actually use. For me, that is the real power of AI. It doesn’t make you less of a coder; it makes you more confident to learn, experiment, and create things you once thought were impossible.
From bare walls to modern living: How generative AI helped me renovate my apartment as a student
3
October
2025
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When I bought my first apartment I knew right away it would be a serious project. The walls were bare, the ceiling was full of old drill holes, the bathroom and toilet were completely outdated and the overall layout felt closed off. As a student it is very rare to even get the chance to buy an apartment, so when the opportunity came I had to take it, even though there was clearly a lot of work ahead. My budget was very limited but I was determined to turn the place into a fresh and modern home.
Hiring contractors was out of the question because of the costs. I decided to do as much as possible myself, even though I had very little experience with renovations. At first the work felt overwhelming. I had removed a wall to create an open kitchen and suddenly electrical cables were hanging out of the ceiling. In the bathroom I wanted to install a modern in wall toilet but I had no idea where to start. Even basic questions such as whether I should repair the holes in the ceiling or build a false ceiling left me uncertain.
That was when I turned to generative AI and discovered how powerful it could be as a guide during a renovation.
From overwhelmed to guided
As a student with little budget and no real experience, renovating my apartment felt overwhelming until I started using generative AI. Instead of endless searching, I received clear explanations and complete step by step guides that helped me build a false ceiling to hide and reroute cables, extend wiring through neat wall chases so I could place outlets and switches where I wanted, and even install and tile a modern in wall toilet. It also gave me advice on the best order of work, the right materials for wet rooms, and smart tips for my new open kitchen layout. What impressed me most was that it not only told me what to do but explained why, which gave me the confidence to tackle jobs I thought were impossible. In the end AI combined knowledge from countless sources into practical instructions that saved me money, taught me valuable skills, and turned my apartment into the modern home I wanted.
Saving on a student budget
Because I had to watch every euro I asked AI for ways to keep costs low. I received suggestions such as using large format tiles from a standard DIY store to achieve a spacious and modern look. I learned that it was smarter to rent or borrow expensive tools that I only needed once. I also discovered that using pre assembled systems such as toilet frames might cost a little more in the beginning but reduced the chance of expensive mistakes later.
These tips allowed me to achieve the modern look I wanted without overspending.
Reflection on generative AI
This renovation taught me that generative AI is more than a tool for writing or programming. It became a practical coach that helped me in three important ways. 1. It made knowledge accessible by condensing scattered information into one clear plan. 2. It accelerated decision making by showing me the options with their pros and cons. 3. It gave me the confidence to tackle jobs that I would never have attempted without guidance.
Looking ahead
I can also imagine how this technology will develop further in the future. Imagine AI that not only explains what you need but also prepares a complete shopping list connected to local stores. Imagine being able to preview your new kitchen or bathroom in a 3D simulation before doing any work. Imagine augmented reality guidance through glasses or a phone screen that shows exactly where you need to drill, cut or place a tile. Imagine AI that calculates the most effective way to achieve your vision within a given budget.
If such tools become available then AI will no longer be only an advisor. It will be a true renovation partner for beginning or advanced renovators standing beside you throughout the entire process.
Conclusion
Renovating my apartment as a student was challenging but also incredibly rewarding. With a limited budget and almost no experience I was able to complete complex tasks such as building a false ceiling, extending electrical wiring, and installing and tiling a modern toilet by relying on the guidance of generative AI. The technology not only gave me clear instructions but also explained the reasoning behind each step, which helped me avoid mistakes. It showed me how powerful AI can be as a practical coach. Looking ahead I see even greater potential in tools that create shopping lists, 3D previews, and augmented reality guidance. What started as an overwhelming project became a successful renovation and proved to me that generative AI can be more than just an advisor. It can be a true partner in learning new skills and achieving ambitious goals.
The Unfulfilled Promise of an AI that can take my Job
30
September
2025
5/5 (1)
With a background in Computer Science, I was able to enter the job market as a software engineer early on. This way I started to work as a programmer at a medium-sized Dutch software company after my first year of studying as a Bachelor. At that time, AI and Generative AI would not have an impact on our line of work for another one and a half years when ChatGPT 3 would launch for the first time.
When working on a large enterprise system for industries with unique and complex processes the complexity in software architecture and class structure increases exponentially. Where in class a coding exercise might have entailed creating a few classes, implementing a few constructors and running a specified set of methods all within a predefined programming language, coding at a software company involves countless additional steps. Even the simplest bug fixes and feature developments require deep knowledge over how a niche subset of the source code functions, a great ability in reading and understanding complex calculations/algorithms ran by the backend and intricate knowledge in not just multiple coding languages (Java, JavaScript, TypeScript…), but also frameworks that run on these languages such as React, Node or Ember.
It was no surprise therefore that all of us were quite intrigued by the potential of AI-assisted coding right from the release of ChatGPT 3. Coding plugins and extensions built into the Integrated Development Environments (IDEs) were already widely utilized within the company for many years and they helped focus on the underlying logical fallacies that need to be solved. With the new Generative AI, however, the premise was that the assistant could assist or take over even this work. After much experimentation and the implementation of an enterprise version of Google Gemini we quickly reached the limits of AI’s coding capabilities in today’s world. After so much drama in the news and a public perception of AI as the coding-killer we found that although Gemini could analyze and correct a couple of individual lines of code, it is not yet able to navigate or store a large codebase to analyze and understand the context of the problem or feature.
Even companies like Microsoft, Google and Meta, who are some of the only organizations on earth able to afford to train their own Gen AI models on their own code, are unable to rely on their AI to fix small bugs autonomously. Too much risk is involved in incorrect design choices, edge case bugs and most importantly the verification process. This process is critical and still requires testing by real humans, who are skilled and competent enough to assess the end results based on chosen requirements.
For us and the rest of the development world, AI coding assistants will stay limited to “chunking” code into deliberately chosen fragments, selected by the developer, that can help the AI in assessing a coding task. A great improvement that can yield automatic generation of “boilerplate code” (repeated code that is commonly used throughout a project), the generation of a “stub implementation” to go off or a list of suggested corrections in case a developer gets hard stuck. Though, generative AI does not nearly constitute a true job killer and even if it could, it will take additional years until full capabilities will be available to the large majority of software companies on earth.
The Job Hunt in the Age of Generative AI
29
September
2025
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One of the most interesting fields I have used Generative AI as of now, is in the context of job applications. I often struggled with wording when creating my resume and cover letters, such as how to appear professional without being generic or how to modify the same experience for different professions. I was able to more clearly reframe my job experience with the assistance of tools like ChatGPT or Perplexity. For instance, they recommended me to reformulate lines like “Supported business development” into “Conducted market analysis and prepared client proposals, helping to improve communication”. Although it didn´t create anything, it did assist me in using more powerful words to express my accomplishments.
Meanwhile, I noticed that businesses are also beginning to use AI in their hiring procedures. AI-powered applicant tracking systems, also known as ATS, that search resumes for keywords are already used by some HR departments. Now, generative AI goes one step further by being able to autonomously create applicant summaries, create job descriptions and even recommend interview questions. In theory, this may result in a quicker and more reliable hiring procedure.
However, the risks are easy to define. AI adoption by recruiters and applicants may turn the process into a sort of “autonomation arms race”. AI-generated keywords are used by applicants to optimise their resumes and by recruiters it is used to filter them. In such a system, the questions come up: What is happening to authenticity? And by favouring particular educational backgrounds or language patterns, who makes sure AI doesn´t reproduce biases?
In my view, generative AI is most useful when it increases clarity rather than when it replaces human decisions. I think the difficulty in hiring is finding a balance between fairness and efficiency. When applied properly, AI can help candidates express themselves more effectively and help businesses manage high application quantities. However, there is the risk of turning humans into automatic patterns and keywords if it takes over the process. It is impossible for an algorithm to fully imitate human characteristics like creativity, motivation and cultural fit.
The key question regarding this interesting topic remains: How can we make sure AI not only speeds up recruiting but also makes it more transparent and inclusive?
I invite you to read a collection of my thoughts and meditations, all relating to my own use of GenAI. The tone of this article is definitely different from my previous one, and I apologise in advance for that. With all that being said, I still hope that some of you may relate to what is written here today.
Foundations of Fear
I would be lying if I said that the past few years were not a complete nightmare for me. My lifelong aspirations of being a creative had never felt so threatened.
First it was the rise of image generators like Midjourney, which generate images while being trained on millions of artists’ stolen works (Goetze, 2024). It was an injustice which I had witnessed firsthand. I was scared, and it felt like something that I wanted to do for years was suddenly taken away from me.
But hey, maybe it would only be visual arts right? They would surely never come for music and video…
It was a truly naïve moment for me, as later other programs would arise that would be able to generate both music and video. Now did I particularly like or find merit in what was generated? No absolutely not, most of the music made on programs like Suno sounded abhorrent. Videos made by Stable Diffusion lacked any of the vision which someone like Denis Villeneuve could have. But that was my opinion, the general public seemed to think otherwise.
In any case, I was not too happy with the emergence of GenAI.
A Puppet on A String
Because of the views that I had previously held, it would come to no-one’s surprise that when I actually seriously had to use GenAI I was practically forced to.
I remember that day very clearly. It was during my second year at Erasmus in my BA bachelor. We had a course on Entrepreneurship, and had to use these resources to help us make a business. It seemed innocent enough, right? But I couldn’t help but feel horrible with every prompt I was typing.
I will be the first to say that when it comes to group work, I have no intention of pulling my group down because of my disdain towards GenAI. I understand that many students use it, and I will not push back. These are just the values that I hold.
And so, I fell into the trap that many students do: I kept on using ChatGPT, DeepSeek etc. I used it to summarise my articles, but never really to brainstorm on my own. Sometimes, I used it to see what grade I would get for an assignment, though the accuracy varied. In the Digital Business course that we followed in year 3, we had to write an entire Essay with AI.
I’ll be the first to say that I did not enjoy the process and I find that AI cannot write in the same way that I do. Even when I had fed the AI with essays and other writings of mine in the past it just really couldn’t compare. I do not know if I was just lucky or uncritical, but I do know that my grade for the essay that I wrote myself was higher than the AI-written one.
Still, I often ask myself if we are entering an era where critical cognitive skills are being eroded due to the overreliance on AI (Zhai et al., 2024). How are we going to move forward when we are unable to detect misinformation and just accept everything that a machine gives us?
Moreover, how am I supposed to not feel guilt for using such a technology? It is not only actively consuming major amounts of energy, but also causing me and my peers to have a harder time in the future job market due to entry-level positions declining (Jockims, 2025).
A Deal with The Devil
For a time, I became quite apathetic to it all as a bachelor’s in business tends to do that to you. So I decided to use GenAI for personal reasons too.
My first experience with this was when I used an AI beauty app to get rid of some acne on my forehead. My partner wanted to post a picture of me in a cat café on their story, but there was some visible acne on my forehead. I then had the “brilliant” idea to use an app to get rid of the Acne, and hey it worked. We were both happy, I got to look good, and they got to post.
I then tried to incorporate GenAI into my writing as my apathy had reached the point of “If you can’t beat them, join them”.
I wrote down lines, and tried to continue sharing ideas with ChatGPT. But still something was missing.
It wasn’t really the story that I wanted to tell. The story I wanted to tell was a lot softer, and more human. It was laced with quiet moments and thoughtful conversations about characters living in a Cyberpunk world. (Ironic I know)
What ChatGPT gave me was…closer to a Marvel movie or a rip-off of Blade Runner. It was instant gratification, and a story with no substance. Why would it be one? It was a story that no human had bothered to write before. Just an amalgamation of the average.
Don’t Let AI Steal Your Daydream
I obviously do not know all of you, but I do urge you to think more critically about your GenAI use and the impact you have by using it.
I know for myself that by using it, I am actively contributing to injustice. Every prompt and sentence will make the models better and with the massive network effects that platforms like ChatGPT have experienced, this trend will continue.
To be able to forgive myself, I first had to admit that what I did wasn’t aligned with my values.
Not all is lost though, as the section’s title suggests we should still be hopeful. When it comes to art, humans still tend to prefer human made art, when they know that something is made by AI according to Millet et al. (2023). They later also say that preserving art is important as it is one of the last beacons of human uniqueness.
I feel like this sentiment extends beyond just art though. All of your ideas are worth something and is part of what makes you human. I have also noticed that in the age of hyper-polished, well, ,everything (movies, music & artwork). I’ve become more drawn to the rawness and imperfection which can be found in a lot of older works. I remember not being able to listen to In Utero by Nirvana for a long time, but now I find myself appreciating the album’s rough edges.
I do not intend to say that I have a moral high ground. In fact, I am also extremely flawed. All of the times that I used GenAI on my own accord was to cope with some form of insecurity that I had. My appearance. my writing ability and even my grades. It was an instant fix for a problem, but it did not fix the underlying issues.
As a subtle form of rebellion, I decided to teach myself guitar. Yes, the process is hard but also gratifying. If I ever want to get on stage, I’ll have to work for it. There’s no instant fix. But that’s the thing, you can’t instantly become Kurt Cobain. It takes hours, days, years of hard work. And you know what? I find that to be beautiful.
I hope that we can take back some form of power. That we can live in a world where we are allowed to have and chase our daydreams. A world where our ideas do not serve as a means for profit to some megacorporation. I hope that I made you think about how our actions are impacting the people around us. I ask you not to be a revolutionary, but I do ask you to contribute to a world that is fairer towards all.
To you, dear reader, I ask the following questions: Do you think that I am overreacting or do you harbour similar feelings? Did your fears around GenAI cause you to change major life plans you had? (I know that it caused me to choose this master!) And finally, are you willing to sacrifice the instant gratification of AI in order to preserve our sense of being human?
References: Goetze, T. S. (2024). AI Art is Theft: Labour, Extraction, and Exploitation: Or, On the Dangers of Stochastic Pollocks. 2022 ACM Conference On Fairness, Accountability, And Transparency, 89, 186–196. https://doi.org/10.1145/3630106.3658898
Jockims, T. L. (2025, 7 september). AI is not just ending entry-level jobs. It’s the end of the career ladder as we know it. CNBC. https://www.cnbc.com/2025/09/07/ai-entry-level-jobs-hiring-careers.html
Millet, K., Buehler, F., Du, G., & Kokkoris, M. D. (2023). Defending humankind: Anthropocentric bias in the appreciation of AI art. Computers in Human Behavior, 143, 107707. https://doi.org/10.1016/j.chb.2023.107707
Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: a systematic review. Smart Learning Environments, 11(1). https://doi.org/10.1186/s40561-024-00316-7
Though I didn’t use it, I find these ones important too, they deal with the environmental aspects: De Vries, A. (2023). The growing energy footprint of artificial intelligence. Joule, 7(10), 2191–2194. https://doi.org/10.1016/j.joule.2023.09.004
Shukla, N. (2025, 19 augustus). Generative AI Is Exhausting the Power Grid. Earth.Org. https://earth.org/generative-ai-is-exhausting-the-power-grid/
Author: Ian Parabirsing
A lover of music, good coffee and cats. I'm a MSC student at RSM studying Business Information Management. In my blog posts I'll be attempting to write about how technology impacts the consumers and society at large.
View all posts by Ian Parabirsing
Enhancing Educational Support with GenAI: How Lyceo is Integrating AI into its Learning Framework
18
October
2024
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The ongoing teacher shortage in the Netherlands is a growing concern, creating disruptions that impact the quality of education and limiting students’ future opportunities. With some classes and even entire school days being canceled, and certain subjects no longer taught, education has taken a hit. As a response, many parents have turned to private tutoring or homework assistance for their children, while schools increasingly seek external educational services. Among these, Lyceo has emerged as the largest provider.
As more and more schools rely on Lyceo, the company is able to leverage AI technology to address various educational challenges and automate tasks. With the introduction of the Lyceo GenAI learning tool, the company’s virtual tutors will be able to support students by answering questions and providing timely feedback on assignments. The tool will offer personalized insights, highlighting students’ strengths and identifying areas where they can improve. By considering diverse learning preferences and abilities, Lyceo can create tailored teaching strategies and resources for each student. This technology not only provides real-time explanations but also extends continuous support, even during late-night study sessions. This self-paced approach is particularly beneficial for those students who prefer to study according to their own schedules.
Additionally, Lyceo’s GenAI-powered chatbots will enhance customer service by assisting parents in obtaining answers immediately. The chatbots are designed to provide information and perform tasks. The informative chatbots will deliver pre-set information to help parents with questions about pricing or suitable programs tailored to a student’s needs. In contrast, task-based chatbots are programmed to handle specific requests, such as scheduling tutoring sessions for students.
However, integrating GenAI into Lyceo’s business model involves considerable investment. The costs for implementing generative AI can range from minimal to several million euros, depending on the specific use case and scale. While smaller companies may benefit from free versions of generative AI tools, like ChatGPT, Lyceo will likely need to invest in customized AI services to develop the online learning tool and sophisticated chatbots tailored to their needs.
The potential benefits make this investment worthwhile, enabling Lyceo to improve its educational support services and continue to meet the evolving demands of schools, students and parents.
NutriNet – a personal assistant for your grocery shopping
18
October
2024
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Have you ever taken hours browsing around supermarkets searching for the most nutritious food options? Did it take you too much time figuring out which recipes to cook best, with the groceries you bought? Struggles when dealing with food are numerous, starting from choosing products with appropriate nutrients simply not knowing enough recipes.
Therefore, we introduce NutriNet – a mobile application that is going to make shopping and meal planning easier!
NutriNet simplifies grocery shopping and meal planning, by analyzing which products and recipes fit the users’ desired grocery item wishes, nutrition values and store preferences best. NutriNet aims to address the challenges being implicit to personalized nutrition and helps consumers make healthier food choices by simplifying the grocery shopping and meal planning process. This shall be done by eliminating the need to perfectly understand all nutrition values or to search for numerous recipes. NutriNet completes all these tasks for you in real-time and provides clear and accessible recommendations for you.
• Real-time personal assitant: NutriNet acts as a multifunctional application providing value to consumers by solving various food related problems in real-time. Appearing as a chatbot, it is aimed at taking general grocery shopping lists or meal wishes as input query, combined with preferences for food characteristics (e.g., nutrients, allergies) and grocery stores. It then provides brand specific and personalized grocery shopping lists, as well as meal recommendations, if so desired. Moreover, grocery items can also be added into the initial grocery shopping list query, by scanning them with the integrated AR tool. The product will then be detected visually and thus will be integrated into the shopping list input query.
• Long-term customer engagement: NutriNet distinguishes from competition by providing personalized and customized advice. This is possible, as NutriNet consists of a database, having incorporated stock and product information of the major supermarkets in the Netherlands. In contrast, classic applications, which try to meet similar needs (e.g., meal recommendation) rather focus on counting nutrients for the purpose of short-term weight loss, instead of personalizing grocery shopping lists and meal recommendation to enable a healthier lifestyle for users. Those applications are usable on short term but are proven to have low adherence over the time (Chen et al., 2015).
• Personalized recommendations: NutriNet leverages generative AI to offer accurately personalized recommendations. Users can simply enter their preferences, while prompting a grocery list or a meal, such as gluten-free or high in protein, and the generative AI powered application will provide accurately personalized results. However, personalization and raising awareness for healthy foods are not the only purposes of NutriNet. It also addresses sustainability issues that supermarkets are facing. By gathering consumer purchase and search data in the application, consulting services can be offered to supermarkets, enabling them to plan ordering and stockholding processes more efficient. Hence, supermarkets should be able to reduce food waste due to overstocking on long-term.
NutriNet offers a 2-sided network platform, yielding important value to both, consumers as well as supermarkets.
Contributors
574051 – Duong Dao 728070 – David Wurzer 738898 – David Do 562387 – Roxi Ni
References
Chen, J., Berkman, W., Bardouh, M., Ng, C. Y. K., & Allman-Farinelli, M. (2019). The use of a food logging app in the naturalistic setting fails to provide accurate measurements of nutrients and poses usability challenges. Nutrition, 57, 208-216.
From Dense Texts to Dynamic Videos: The Synopsis.ai Web App
17
October
2024
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Team 6: Noah van Lienden, Dan Gong, Ravdeep Singh & Maciej Wiecko.
Ever found yourself staring blankly at a 50-page academic paper, wondering if there’s a faster, more engaging way to grasp the key points? What if that dense text could transform into a lively video, complete with animations and a friendly narrator? Welcome to the future of learning with our Synopsis.ai web app!
The Education Technology (EdTech) market is skyrocketing. In 2023, the global EdTech market hit a whopping $144.6 billion and is projected to triple by 2032. With advancements in AI, augmented reality (AR), virtual reality (VR), and more, the way we learn is evolving faster and changing day to day. Generative AI is the new superstar in the EdTech universe. Tools like Scholarcy are helping students by turning lengthy texts into bite-sized summaries. But let’s face it—reading summaries can still feel like, well, reading. How great would it be if you could watch a video instead?
Enter Synopsis, the groundbreaking web app that’s set to revolutionize how we digest academic content. Synopsis uses advanced AI to convert scholarly articles into short, engaging videos. It’s like having your own personal explainer video for every complex paper you need to read. You can customize these videos and choose either a lecture format or an animated video format. Furthermore, users can select their desired video length, content granularity and even add subtitles!
All this new content is not only wonderful for student learning with our web app, but also Researches, Educators and even Content Creators! All these different users can have different uses of our platform, and can each bring value in new ways to themselves, or even to others!
So how does this magic work behind the scenes? Synopsis leverages state-of-the-art AI models like GPT-4 and BERT, fine-tuned on vast academic datasets. It collaborates with AI research institutions to stay ahead of technological advancements and works with designers to create customizable templates and animations. While there are tools that summarize texts or create videos, none combine both in an educational context. Synopsis fills this market gap by offering a seamless solution that transforms academic articles into personalized video summaries.
In a world where attention spans are dwindling, and visual content reigns supreme, Synopsis is poised to make a significant impact. By making learning more accessible and enjoyable, it’s not just keeping up with the future of education—it’s helping to shape it!
Using Generative AI as My Marathon Training Coach
11
October
2024
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I want to run a marathon next summer, so I turned to ChatGPT to help me out. Not with the running part, but mostly everything else. With generative AI, I can have a personal marathon coach that understands my needs, optimizes my training plan, and continually adapts as my fitness level evolves.
Setting Up My AI Coach
To create my AI coach, I used the MyGPT function offered by OpenAI. It allows you to customize a ChatGPT model to your liking by providing sets of instructions and information. I instructed it to act as my professional marathon coach who understands my goals, fitness level, and the specific challenges of marathon training. I explained my current abilities, body metrics, and general athletic history. I also provided my time goal, the marathon course info, and the date of the event. Moreover, I uploaded important data:
Running metrics for current, regular runs (distance, time, heart rate zone info, cadence, pace, split pace, effort level, etc.)
Apple Watch training data
Calendar data
This data ensures that the model can give more specific advice and training plans. For example, it uses my previous data to set pace targets considering terrain and what heart rate zone I should be in for a particular session.
Phased Training Plan
The AI-generated plan broke my preparation into phases, each with a specific focus. The initial phase was about building endurance with consistent mileage and easy runs. Later phases introduced speed work and tempo runs to improve stamina and pace. The AI explains the purpose behind each phase, which keeps me motivated and committed.
The phased approach means I’m not overwhelmed. Instead of seeing it as one long journey, I focus on each phase, trusting that the AI has mapped out the best way to build my fitness gradually. Each phase has unique challenges and goals, which helps me stay focused and structured. Here is an example of what it created:
Phase 2: Strength & Speed Building (Weeks 11–20)
Goal: Increase mileage, speed, and introduce hill work. Continue to improve cadence and manage heart rate.
Weekly Structure:
5 days running:
1 long run (gradually increasing distance)
1 interval session (e.g., 4 x 1 km at 5:00/km)
1 tempo run at marathon pace (5:40–5:50/km)
2 easy runs (HR < 150 bpm)
1–2 days cross-training or strength training focused on core and lower body strength.
Example Week 15:
Monday: Rest or cross-training
Tuesday: 8 km easy run (HR < 150 bpm)
Wednesday: Interval run: 5 x 1 km at 4:55–5:05/km with 2-min recovery
Thursday: Cross-training (strength-focused)
Friday: 8 km tempo run at marathon pace (5:40/km)
Saturday: 18 km long run (pace 6:10–6:30/km)
Sunday: Rest or mobility work
Long Run Progression:
Week 11: 18 km at 6:10/km
Week 14: 22 km at 6:00–6:20/km (HR under 160 bpm)
Week 17: 26 km at 6:10/km with last 3 km at marathon pace (5:40/km)
Week 20: 30 km long run (gradual build, HR < 160 bpm)
End of Phase Goal:
Cadence: Improve to around 165–170 SPM.
Long Runs: 30 km long run with a fast finish.
Tempo Pace: Consistent pacing at 5:40/km for up to 12 km.
How AI Adjusts Based on Real-Time Data
The AI adjusts based on real-time data from my Apple Watch, including cadence, heart rate, pace, and distance. If my heart rate is higher than usual during long runs, the AI might recommend more rest or adjust upcoming workouts. On strong days, it suggests more intensity or extra mileage. These adjustments keep my plan in sync with my body’s needs. The AI doesn’t just follow a set schedule—it adapts continuously to prevent burnout or injuries and to push me when I’m ready.
Personalized Insights & Accountability
The AI can provide insights into my performance, spotting patterns I might not notice. I can ask it to analyze my recent running data and give me advice if it is necessary to adjust the training plan. For example, when my pace stagnated, the AI suggested changes—more recovery days and different speed workouts. This helped me break through the plateau, improving my pace within weeks. These insights are invaluable and something I wouldn’t easily notice on my own.
Looking Ahead: My Marathon Journey Continues
With about eight months until the marathon, I’m excited to continue building on the progress I’ve made. Having a personalized plan gives me confidence that I will be well-prepared when race day arrives. Generative AI has transformed my training goals. It’s personalized, adaptive, and insightful. It helps me understand my capabilities, push limits, and gain confidence in my training approach.
Have you ever considered using AI to help with your fitness goals? I’d love to hear your thoughts or experiences—feel free to share in the comments below!
