CORE 2096 End-of-Semester Reflection (Fall 2025) — What Actually Changed In Me
This course didn’t just “teach” me things. It made me learn how I learn, how I speak, and how I deal with other people’s opinions. The biggest shift is that I stopped being a passive receiver of information. I became someone who interrogates things, politely, but stubbornly, until they make sense.
1) Most impactful learning experiences (and what specifically helped me learn them)
A) About myself: I learned how to reflect without guilt, and it made me braver
Before this course, I always had a strange internal conflict: I love talking about myself, but culturally I grew up believing that self-expression equals arrogance. Even when I was proud of something, I would shrink it, hide it, or feel guilty for acknowledging it. The reflection work in this class did the opposite. It forced me to look at myself honestly, not as someone being self-centered, but as a person with real experiences and stories worth examining and sharing. Through that process, I started noticing things I genuinely hadn’t seen before: how hard I actually work (and how much I underestimate that effort), how harsh I am with myself, and how much I fear interacting with people even when I pretend I don’t.
All of this became especially clear during what I now see as the turning point of the course for me: the AI in my profession interviews assignment. The assignment required interviewing professors and professionals in our majors, so I kept reaching out to professors. I was getting ghosted, rejected, or, when interviews did happen, receiving answers that felt shallow and unengaging. The old Ahmed would have accepted that, written the answers down, and submitted the assignment like a robot just to get it done. But something changed. Instead of settling, I pushed back. I asked deeper questions, challenged assumptions, and in one case even made a professor slightly defensive.
For instance, during one AI interview, I challenged a professor’s insistence that AI misuse is entirely the student’s responsibility. I pushed back on the idea that students should be expected to use AI correctly without guidance, questioned her rejection of AI tools in education (especially when tools like course chatbots are clearly part of the future), and disagreed with her comparison of AI to search engines like Google, since AI is far more intricate and adaptive. That moment made me realize I was no longer just listening to authority, I was actively interrogating it. That was when something fundamental shifted: Ahmed no longer fears asking questions.
This realization made me think back to my first year at university. I remember being genuinely afraid to ask professors questions, especially in difficult classes like accounting, where expectations felt intimidatingly high. I worried I would sound stupid, slow, or even aggressive. That fear cost me a lot. There were questions about the balance sheet that I never asked, questions that could have helped me truly understand the logic behind it instead of just memorizing formulas. Looking back, I honestly believe that asking those questions might have changed how I understood accounting altogether and possibly even influenced the academic paths I considered later on. That fear wasn’t just about asking questions; it was about being judged or made fun of by professors or classmates. So I stayed quiet. I chose safety over understanding.
Now, after this course, I genuinely find myself thinking: who cares? I came to university to learn. Asking questions is not a weakness, and it’s definitely not something anyone has the right to judge. That’s why, during the profession assignment, challenging professors felt different from before. I wasn’t challenging the professors to provoke, impress, or argue. I was challenging the professors because I wanted to understand. I wasn’t asking aimlessly or hesitantly anymore, I was asking with intention. This shift from fear-driven silence to purpose-driven questioning is one of the most important changes I’ve experienced, and it’s something I know will stay with me far beyond this class.
Part of that growth came from doing reflections consistently, and part of it came from the stories you shared in class. Dr. You often said that you challenge people about everything, especially when it comes to AI, because that’s how learning actually happens. You even mentioned being invited to a conference specifically to challenge a speaker. That story shifted something in me completely. It made me realize that learning isn’t about passive listening, because what’s the point of listening if you’re not analyzing what’s being said? It is about actually analyzing what the person in front of you is saying.
B) In my academic life: I learned a real research workflow and ask more
Before this semester, my research process was honestly chaotic. I relied heavily on normal ChatGPT or Claude, asked for sources, checked them one by one, and almost always ended up discovering that most citations were fake, irrelevant, or didn’t actually support the claims being made. That turned every assignment into a long cycle of fixing damage rather than learning. I had a particularly bad experience before this course with my FDI Singapore project. ChatGPT generated a long list of sources, but only two of them were actually valid. I spent seven straight hours correcting citations, replacing papers, and rewriting arguments just to make the paper academically acceptable. That experience left me distrustful of AI and completely exhausted by research.
This course forced me to confront that frustration and build something more professional, especially through the AI Tool Reflection. What made the difference wasn’t just being told to “use AI tools,” but being pushed to use specific tools and then reflect on how and why they work. I have chosen Rabbit and deep research Chatgbt as my two tools to reflect on. For Rabbit, works like a search-engine map for research: I put in a topic, and it instantly shows me clusters of real studies, how they connect, and which ones are closest to my topic of research. Here it changed how I do research completely, for instance in the project of Capital markets which i had to discuss the difference between Ai bubble Vs Dot-com bubble I used Rabbit as my research tool where I looked up the relationship between the dot-com boom and the current AI boom, I expected confirmation that the AI bubble will repeat what happened in the dot-com bubble in terms of extreme growth and then recession caused by investors investing in growth companies in dot-com era and these companies failed leading to people losing money which caused recession (this was my initial thesis). Instead, Rabbit led me to papers that argued the opposite, that the AI boom is structurally different, with stronger fundamentals, real revenue, and wider adoption. It didn’t just “give me an answer”; it forced me to see where my assumption might be incomplete. It helped me build a more accurate analysis of technological bubbles and understand why the dot-com crash unfolded the way it did, inflated valuations, lack of real business models, and companies going public based on hype rather than fundamentals. Having access to real research made the historical comparison stronger and more grounded.
Deep Research chatgbt, on the other hand, approached the same topic from the opposite direction (I asked this time for a case study on the AI bubble and how it will be close to dot-com bubble companies). Rather than mapping the literature, it synthesized it. I liked how it asked me clarifying questions, for example, whether I wanted a case study on a specific company like Nvidia or Amazon, because it made the final output more tailored to my exact research question. And unlike normal ChatGPT, the links actually worked, the sources were real, and the structure felt much closer to a mini-review paper. It definitely saved time because it summarized and organized the main findings from multiple sources into something coherent.
Moreover, in my academic life, I learned to challenge my professors more, regardless of the fear of embarrassment or sounding uninformed. As I explained earlier in the “About myself” section, this course helped me unlearn the idea that asking hard questions is a sign of weakness.
C) In my social life: I learned how to talk to strangers without overthinking it
I always knew I didn’t enjoy talking to strangers, but I didn’t realize how much I overthought it. I used to treat simple interactions like a risk calculation: “What if I sound stupid?” “What if they don’t like me?” “What if it’s awkward?” This course pushed me into social situations repeatedly, class activities, discussions, Soliya, GCC interactions, and it basically proved my anxiety wrong. Most interactions were… normal. Some were even fun. And the biggest proof is that I met people through this course that I now consider friends: Youssef, Mohammed, Carl, and Shady. That wouldn’t have happened if the course didn’t force interaction. So the learning here is simple but important: my fear is louder than reality. This will push me further to interact with people more.
D) In my career: I learned that feedback is not optional, it’s the work
The Startup Quest game wasn’t just a creative project for me. It turned into one of the clearest lessons I’ve had about how real work actually improves: through feedback. We didn’t just design a game; we designed something, watched people interact with it, realized it didn’t work the way we thought it would, and then fixed it. That loop changed how I think about building anything professionally. The most important realization came when testers played the game and told us the scoring system didn’t feel alive. On paper, it was “logical”: four bars updating quietly in the background. But people didn’t feel the consequences of their choices. That feedback forced us to rethink the entire system. We separated the Budget as its own resource, added red and green visual signals, and made consequences emotionally visible instead of hidden. The moment we did that, the game stopped being a concept and started feeling real.
That experience translates directly into how I think about my future career, especially in finance. A financial model can be perfectly built, fully linked, and technically correct, and still be useless if it doesn’t respond to how real people interpret it. Just like the game, a model isn’t successful because it’s complex; it’s successful because decision-makers can feel what happens when assumptions change. Sensitivities, scenarios, and stress tests are basically feedback systems. If no one reacts to them, then the model fails, not the user.
Building Startup Quest taught me that feedback isn’t something you collect at the end to “validate” your work. It’s something that actively reshapes the work while it’s being built. In finance, that means listening when an investor doesn’t trust an assumption, when a manager gets confused by a result, or when a scenario doesn’t reflect reality. Just like in the game, those moments aren’t criticism, they’re signals. And learning to treat feedback as data, not as judgment, is one of the most career-defining skills I’m taking from this course.
2) If I had to show someone my learning, I’d show these 3 things I created (and why)
1) Startup Quest: The Founder Journeys (Final Game + Reflection)
This project best represents how I learned to combine empathy, systems thinking, and iteration. It shows that I can take abstract ideas like “empathy” and translate them into concrete design decisions that respond to real users. The final version reflects extensive effort: testing the game with others, merging male and female founder paths, rewriting large parts of the narrative, debugging the code, and redesigning the scoring system to make consequences emotionally visible through visual feedback. More importantly, the reflection accompanying the game shows how I learned to treat feedback as data and how that feedback reshaped the final product. This artifact captures my growth in turning intention into execution.
2) AI Tool Reflection (ResearchRabbit vs. ChatGPT Deep Research)
This reflection demonstrates my growth in research maturity and critical thinking. Instead of either blindly trusting AI or rejecting it entirely, I learned how to evaluate tools based on what they are actually good at and where they fail. Through this assignment, I developed a structured research workflow: discovery, verification, synthesis, and interpretation. ResearchRabbit helped me map real academic literature and challenge my assumptions, while ChatGPT Deep Research helped me synthesize verified sources into coherent analysis. This shows that I can engage with AI responsibly, critically, and academically rather than using it as a shortcut.
ChatGPT Deep Research link:
ResearchRabbit library link:
3) AI in Finance Field Assignment (Interviews + Reflection)
This assignment shows my growth in academic confidence and intellectual courage. Through interviewing finance and accounting professors, I moved from passively recording answers to actively questioning assumptions, pushing back on ideas that didn’t fully make sense to me, and engaging in meaningful dialogue. The reflection demonstrates my ability to analyze expert opinions critically rather than accepting them because of authority. It also shows my evolving perspective on AI in finance, balancing fear, realism, and responsibility. This shows my growth in challenging people.
Interview audio (Part 1 & 2) and transcript:
4) End of Semester Self-Assessment and Feedback – CORE 2096 (Fall 2025)
This self-assessment reflects my ability to evaluate my own learning honestly and take responsibility for both strengths and weaknesses. It shows that I didn’t just focus on outcomes or grades, but on process: attendance, participation, effort, accountability, and growth over time. Writing it forced me to articulate where I genuinely improved and where I fell short, such as lateness, without deflecting blame. This matters because it demonstrates self-awareness, reflection with evidence, and the ability to assess my own performance critically.
5) GCC Slack Activity: “Holiday or Tourist Photos and the Self”
This activity represents my engagement with the GCC experience and my willingness to participate socially and reflectively on slack. Posting and responding on Slack pushed me to connect personal experience with identity and perception, rather than treating GCC as a passive requirement. It also shows how I used informal digital platforms meaningfully, not just for submission, but for dialogue and self-expression. This matters because it captures my growth in expressing myself publicly, engaging with others’ perspectives, and reflecting on identity in a way that felt personal rather than performative.
File with my reflection of holiday
3) If I could change 3 things about the course to improve it
1) Integrate real-world trips or field exposure tied to course themes
One change that would genuinely strengthen this course is incorporating one or two real-world trips that directly relate to its core themes. The course deals heavily with empathy, identity, dialogue, and lived experience, concepts that become far more powerful when they are experienced rather than only discussed in class. For example, a visit to a Sudanese neighborhood in Egypt could help students observe daily realities, social dynamics, and access to resources, and then directly connect those observations to activities like the Privilege Walk we did in class. From my own experience, the moments that stayed with me the most were the ones where ideas felt real instead of theoretical. Adding field exposure like this would deepen the “learning by feeling” aspect of the course and give students shared, concrete experiences to reflect on more meaningfully.
2) Add a structured, feedback-driven game exchange and competition
Another improvement would be to build a more structured feedback cycle around the game projects. Instead of feedback coming from a limited number of students (our game only had 2 people who gave feedback), every student in the class could be required to play each other’s games and provide written feedback as part of an assignment. This would ensure that feedback is diverse, thoughtful, and taken seriously, rather than optional or rushed. It would also push students to engage deeply with their classmates’ work, not just their own. This could be taken a step further by turning the final version of the games into a small competition, either across sections of the course or with an external audience. For example, students from other classes or people outside the course could play the games and rate their experience. The winning game would be the one that audiences found the most engaging or meaningful. This would reinforce one of the core lessons of the course: that empathy, clarity, and user experience matter more than ambition alone. It would also mirror real-world creative and professional environments, where feedback from actual users, not just instructors, shapes what succeeds.
3) Invite guest speakers who actively use AI in their fields
A third improvement would be bringing in guest speakers who actively use AI in their professional fields, such as finance, engineering, or other disciplines that students in this course are currently studying. Hearing from them on how they use AI in real work environments would help bridge the gap between theory and practice, especially given how central AI has become across industries. These sessions could focus not only on how AI is used, but also on its limitations, ethical concerns, and future implications within each field. Making these talks interactive, especially in our circular seating setup, would encourage open discussion rather than passive listening. Students could ask questions, raise concerns, and challenge assumptions in real time. This could be reinforced by a short reflection assignment after each talk, pushing students to analyze what was said rather than simply summarize it. I think this would be particularly powerful because it aligns with one of the most important lessons I learned in this course: learning happens when we question, not when we just listen. If professors actively encourage students to challenge speakers during these sessions, it would normalize critical engagement and help students practice analyzing expert opinions respectfully and thoughtfully.
4) In one sentence: what kind of person should take this course?
This course is for anyone who wants to understand themselves better while learning how to think critically, question ideas without fear, and engage with other people’s perspectives in a way that actually changes how they learn and interact.
5) Reflection on the tools used in this course
This course didn’t just introduce tools; it fundamentally changed how I think about using them. What mattered most was not learning what tools exist, but learning how and when to use them critically, responsibly, and intentionally.
1) ResearchRabbit
This was one of the most valuable academic tools I used throughout the course. What I liked most about ResearchRabbit is that it forced me to engage with real research rather than summaries or surface-level claims. Instead of giving me answers, it showed me how studies connect, where ideas cluster, and where assumptions in the literature get challenged. Academically, it completely changed how I discover knowledge. I will continue using it for literature reviews, research-heavy assignments, and any future academic or professional work that requires evidence-based thinking.
2) ChatGPT Deep Research
Unlike normal ChatGPT, Deep Research helped me with synthesis rather than hallucination. I liked that it asked clarifying questions before producing output, which made the results feel tailored rather than generic. The fact that the sources were real and the links actually worked made it feel much closer to a mini literature review than an AI shortcut. I will use it academically and professionally to organize complex information, compare viewpoints, and structure arguments. However, I also learned that it still requires constant verification and human judgment, especially when it comes to recency and interpretation.
3) Google Gemini
Google Gemini played a role in helping us build Startup Quest, but it was also one of the most frustrating tools I used in this course. While it was helpful in theory for generating and structuring code, in practice it repeatedly failed to understand what I needed. It kept producing non-functioning or partially broken code, especially when it came to connecting story paths, managing variables, and making the game logic work smoothly. As someone who is not a computer science major, this made the process significantly more difficult. I had to spend countless hours fixing HTML and logic issues manually, often switching back and forth between Gemini and ChatGPT just to get something functional. What should have been a supportive tool turned into a five-hour debugging process simply because Gemini refused to get the code right in early attempts. At the same time, this frustration taught me an important lesson. I could not rely on the tool blindly. I had to understand the logic myself, question every output, and actively fix what didn’t work. Professionally, this experience showed me that AI tools like Gemini can speed things up only if the user already understands the underlying logic.
4) Reflect blog platform
The Reflect blog platform was useful in theory, but in practice it was one of the most stressful tools for me to work with. I liked the idea of publicly documenting our learning and having a space that showed progress over time. It helped make reflection feel more real and visible rather than just another private submission. However, I repeatedly struggled with technical limitations. The platform had serious issues handling large pieces of writing. Many times, when I worked on long reflections, the content would fail to load, delete itself, or not save properly. Even now, with the length of this final reflection, I am genuinely worried it may not process or upload correctly. This caused unnecessary stress and wasted time. I often had to rewrite sections, repost content multiple times, or break work into pieces just to make sure nothing was lost. Going forward, I would only want to use a tool like this if it were more stable and reliable for long-form writing. The experience taught me that even reflective tools need strong technical support
5) Pixabay (copyright-safe media)
Pixabay was an easy and helpful tool because it removed the stress of accidentally stealing images or using copyrighted content without realizing it. I didn’t have to constantly check labels or worry about licensing details. It made it much easier to quickly find images that fit what I needed while staying within copyright rules. Having a clear, safe source for visuals made the creative process smoother and more efficient.
6) GCC reflection: what would have made it better for me
I’ll be honest: my GCC experience felt a bit incomplete because I did not attend the first GCC session (I missed it because I was working). So most of my “GCC learning” came from the one thing I actually produced and engaged with, which was the Holiday or Tourist Photos and the Self reflection. Writing that document was still valuable because it forced me to think about the gap between what I mean in a photo and what people project onto it, and I saw that clearly when people misunderstood one of my posts and built a whole false story around it. We also had the Slack discussion about voluntourism, and I’ll say my opinion clearly: it felt shallow. When I shared my perspective, even though it was posted late, no one engaged with it at all. There was no response, no challenge, no follow-up. It felt like most people treated the activity as something to submit and move on from, rather than an actual discussion. What made this more frustrating is that my viewpoint was more controversial than most of the other posts, and it could have easily sparked debate or disagreement. Instead, it led to nothing.
Here’s the post link for the GCC conversations: Slack link.
What would have made GCC better for me is basically more structure & more balance:
- A smoother way to catch up when someone misses the first session (like a short recap, or a required catch-up session or even more sessions with gcc students) so you don’t feel like you’re joining mid-story.
- More guided debate in discussions like voluntourism: not just “here’s what’s wrong”, but also “what’s the strongest argument on the other side?” and “what would ethical voluntourism look like?” That would’ve fit the whole theme of the course, questioning instead of just absorbing.
AI Disclosure Statement
I used AI tools (mainly ChatGPT) for language polishing, restructuring sentences, and improving clarity and flow in this reflection. All ideas, experiences, opinions, and arguments are my own. AI was not used to generate the core content or reflections; rather, it was used to refine wording, improve organization, and occasionally help continue or rephrase ideas based strictly on the detailed content and experiences I had already provided it regarding the things we did throughout the semester.
Below is the prompt I used for ChatGPT to help me with the reflection https://chatgpt.com/share/6946de0a-59a8-800c-9436-d3869dfbacb9
Provide Feedback