Emotional Intelligence 101

edward pappoe Deputy Director engineering

At first glance, the topics looked familiar, but the depth surprised me. Design thinking often gets treated as a soft skill, yet this session connected it to real engineering work. Coming from projects in aerospace systems integration and automotive ADAS development, the framing around problem definition hit home. Too often, requirements flow-down or DFMEA starts before the actual user problem is clear. One challenge was compressing the full design thinking cycle into a one-hour format. Some exercises felt rushed, especially when trying to map empathy insights to technically constrained environments like avionics certification or EV thermal management. Still, the examples helped bridge that gap. A useful takeaway was the emphasis on reframing problem statements before locking architectures. That’s something already applied on an automotive HMI update, where a quick stakeholder mapping exercise exposed a missed serviceability issue. The course also filled a knowledge gap around how design thinking can coexist with regulated aerospace processes instead of fighting them. The content stayed practical and didn’t overpromise career transformation, which was refreshing. Overall, it sharpened how early decisions can reduce downstream rework. It definitely strengthened my technical clarity.

RISHI DEV CAD AND MECHANICAL

Coming into this course, I had some prior exposure to the subject, mostly from applying lightweight design sprints inside larger programs. The session did a decent job grounding design thinking in concrete steps rather than abstract diagrams. From an aerospace perspective, the discussion around problem framing resonated, especially when contrasted with how requirements are usually locked early due to certification constraints. In automotive programs, where platform reuse and supplier lead times dominate, the emphasis on early user validation highlighted a gap with typical V-model workflows. One challenge was compressing meaningful empathy work into a one-hour format. That’s an edge case the course acknowledged but couldn’t fully resolve, particularly for safety-critical systems where user input has to be filtered through regulatory and systems engineering layers. Still, the comparison with industry practices helped clarify where design thinking fits and where it realistically doesn’t. A practical takeaway was reframing requirements as testable hypotheses before committing to architecture decisions. That small shift has system-level implications, especially when managing interfaces across subsystems. The content felt aligned with practical engineering demands.

Sagar Laishetti

Initially, I wasn’t sure what to expect from this course. As someone working across automotive and a bit of aerospace programs, most AI content I see is either hype or too academic. This one landed somewhere practical. The overview of how large language models work helped close a gap I had around why tools like ChatGPT behave inconsistently, which matters when you’re dealing with requirements flow-down or documentation tied to ISO 26262 or even DO‑178C style processes. One useful angle was seeing how generative AI can support early-phase tasks like requirements clarification, test case brainstorming, or summarizing CFD or simulation results for reviews. A real challenge during the course was separating realistic use cases from things that are still risky, especially around hallucinations when asking domain-specific questions about control systems or vehicle architecture. The most immediate takeaway was learning how to structure prompts with constraints and verification steps, instead of treating ChatGPT like a search engine. That alone made the outputs more usable on an active project. It’s not a replacement for engineering judgment, but it does save time in the margins. I can see this being useful in long-term project work.

edward pappoe Deputy Director engineering

Initially, I wasn’t sure what to expect from this course, especially given it was only an hour and I already use ChatGPT casually at work. Coming from an automotive background with some exposure to aerospace systems, I was curious whether it would actually add value beyond basic prompts. What worked well was the explanation of how large language models reason and where they break down. That helped me rethink how to use ChatGPT for tasks like drafting requirements for an automotive ECU update and summarizing aerospace-style verification documents. One challenge was translating the generic examples into engineering-specific workflows; the course doesn’t fully walk you through domain-heavy use cases like thermal analysis notes or failure mode discussions. Still, it highlighted the importance of structured prompts and iteration, which was a gap in my understanding. A practical takeaway was learning how to constrain outputs so they’re more usable for real projects—like asking for assumptions, limitations, or step-by-step logic instead of a polished answer. I’ve already applied this while reviewing design trade-offs and preparing internal technical summaries. The course isn’t deep, but it’s grounded enough to be useful for working engineers. It definitely strengthened my technical clarity.