My CliftonStrengths are Futuristic, Empathy, Believer, Achiever, and Significance. Honestly, I think they fit me pretty well. I’m always thinking about what I want my future to look like and what I want to accomplish. I also tend to care a lot about the people around me and think about how they’re feeling. Believer stood out to me because I like having a purpose behind what I do, and my faith is a big part of that. Achiever also makes sense because I’m usually working toward several goals at once and feel like I always need to be doing something. Significance connects to wanting my work to actually matter and make an impact.

I think the most interesting part is how these strengths work together. I have a picture of where I want to go, I care about why I’m doing it, and then I push myself to actually make it happen. At the same time, I probably need to be careful about putting too much pressure on myself or always thinking about what I need to accomplish next instead of noticing the progress I’ve already made.

I think Achiever is one of the strengths I see the most in myself. I usually have a lot going on between school, robotics, work, Perfect Pixel, and now college. I like feeling productive and knowing I’m making progress.

At the same time, being an Achiever can make me feel like I’m not doing enough, even when I’ve already accomplished a lot. I think having a lot of responsibilities has made being busy feel normal to me. I want to pay more attention to whether I’m doing something because I actually want to or because I feel like I always need to accomplish something.

The organizations I explored also made me realize I want to be around people and opportunities where I can grow and use my creative and business skills. That connects a lot to my other strengths, especially Futuristic, Believer, and Significance.

One thing I took away from this module is that AI decisions involve more than just the technology. The people, goals, rules, information, and biases behind it can all affect the outcome. I think AI can be helpful for giving information and supporting decisions, but people should still be responsible for the final decision.

I also realized that relying on AI too much could affect how we think and make decisions for ourselves. If AI is always doing the thinking for us, we might become less confident in our own judgment. At the same time, I don’t think AI needs to be 

avoided completely. It can be a useful tool when people understand its limits and actually think about the information it gives them.

I’m still interested in figuring out where the line is between using AI to support a decision and letting AI make the decision for us.

I chose Achiever because I naturally focus on progress, goals, and getting things done. When looking at the University of Michigan grading policy, I noticed myself thinking about how the policy could affect students’ motivation and how they measure their progress.

My Achiever strength makes me pay attention to the outcomes of the policy, but it could also make me overlook things like stress, learning, or students who may not be motivated mainly by grades. Someone with Empathy, for example, might focus more on how the policy affects students emotionally and 

personally.

One thing I learned is that my strengths can influence

what I notice in a system, but they don’t give me the whole picture. I would need to slow down and consider perspectives that don’t come naturally to me before making a judgment.

At first, I was mainly thinking about making the scholarship process fair and consistent. The discussion made me think more about how decisions can affect future scholarship cycles. The idea that students who already receive opportunities can keep getting more because those opportunities give them stronger evidence of readiness really stood out to me. It made me think about students who have potential but have not had the same chances.

Our group also had to balance fairness with efficiency. I think AI could help organize applications and identify patterns, but

people should still make the final decisions and monitor the results. Human reviewers need to be able to recognize when the process is creating an unfair pattern and change it.

One leverage point was changing what counts as evidence of readiness. Looking at growth and potential, instead of only previous accomplishments, could help prevent the same students from continuing to receive opportunities. One question I would ask is: How will we know if the new process is actually fair?

One thing that could make Northbridge miss the feedback loop is that the effects may take several scholarship cycles to become noticeable. It might not be obvious right away that students who already have opportunities are continuing to receive more opportunities.

The biggest thing I took away was that looking only at the immediate decision can hide what happens later. My strengths helped me focus on people and how decisions affect them, but I also had to think beyond the individual applicant and look at the whole system.

Before the workshop, I was more focused on choosing a fair review process. After looking at the feedback loops, I think the process also needs to be monitored and adjusted over time. In a Purdue system, I want to ask more questions about who is affected, what might happen next, and whether the results are actually what the system was intended to accomplish.

A question I want to keep asking is: “What could happen because of this decision that we aren't seeing yet?”