Write on your own where/how you think AI is currently being used, or might be used in the near future in your field, whether academically while studying or in the workplace (1 point)
I personally believe that AI is not used as widely as it should be in the construction industry; and in the areas it is used, it is not used to its full potential. There are many areas in construction that can benefit from the streamlining of gruesome tasks that can be fully automated by technology and AI. Tasks such as document control and review is one of the areas where AI can fully take over and automate the process rather than having a dedicated department to manually upload and review documents.
Use one of the AI tools to see what it will suggest are ways AI will be used in your field. Cite the AI with the link and your prompt (1 point)
Prompt: “Tell me in what ways you think AI is currently being used or might be used in the near future in Construction engineering, whether academically while studying or in the workplace. Tell me if the change to AI would be easy or not in those certain areas”
Answer:
“The integration of AI into construction engineering is transforming the field from an academic level through to on-site execution, though the ease of adoption varies significantly across these domains. In academia, students are increasingly utilizing AI for generative structural design and physics-aware simulations to optimize buildability, a transition that is relatively manageable but often slowed by the pace of curriculum updates. In the professional office, AI serves as a powerful co-pilot for predictive risk analytics and automated clash detection in BIM, yet this shift remains difficult due to complex legal liabilities and the need for updated insurance frameworks. On the physical job site, AI-driven computer vision and autonomous equipment offer revolutionary safety and progress tracking, but these face the steepest climb toward adoption because of the unpredictable nature of construction environments, high capital costs, and a traditionally resistant industry culture. Overall, while the technical capability for AI exists across the board, its successful implementation depends on balancing innovative efficiency with the rigorous safety and legal standards inherent to the built environment.”
Tool: Gemini
Chat Link:
Interview a professor at AUC about this – you can ask them about both academic and professional uses. You can take permission to record the interview in audio and put the recording on your blog, with a summary of your key findings. Or you can take notes. Maybe take a photo with the professor as well 🙂 (2 points)
Interview questions:
“How do you foresee AI transforming core engineering workflows over the next decade, and which areas do you believe are currently most ‘ripe’ for disruption?”
“The traditional career ladder in construction engineering is being upended because the tasks typically assigned to entry-level roles, such as data entry, basic drafting, and repetitive calculations, are the easiest to automate. We are entering an era where a lower year-of-experience requirement correlates directly with a higher potential for total automation, effectively removing the “training wheels” of the profession. This shift forces younger engineers to leapfrog the technical drudgery that once defined their early careers, requiring them to immediately adopt a managerial and oversight mindset. The challenge for the industry will be finding new ways to cultivate “gut feeling” and professional intuition in a generation that may never have to perform the manual foundational work that used to build that very expertise.”
“What are the most significant ethical or practical challenges we face as we integrate AI-driven decision-making into safety-critical infrastructure projects?”
“There is a dangerous distinction between a result that is logically consistent and one that is practically sound, and we are currently seeing a trend toward over-dependence on AI outputs simply because they appear reasonable. While an AI can optimize a structure for efficiency, its lack of “soundness” means it may fail to account for the unpredictable, non-linear realities of the physical world, creating a veneer of accuracy that can be misleading. This risk extends into the moral fabric of engineering; delegating ethical questions, such as whether a project’s social or environmental cost is justifiable, to an algorithm is fundamentally flawed. AI can tell us the “how,” but it lacks the human conscience required to answer the “should,” making it a tool for calculation but never a substitute for the ethical accountability of a professional engineer.”
“How should the current construction engineering curriculum evolve to ensure students are prepared to collaborate effectively with AI agents in their future careers?”
“The current educational challenge is not just about changing our teaching methods, but about aggressively pruning the curriculum of content that has become obsolete. Just as the calculator and the computer rendered the slide rule a relic, AI is turning many traditional manual redundancies into “technical weight” that no longer serves the modern practitioner. We must stop teaching students to compete with the machine in areas of speed and repetition and instead focus on constant evolution and AI orchestration. This means shifting the core of engineering education away from the “manual grind” and toward the critical auditing of automated results, ensuring students are masters of verifying soundness and asking the right questions rather than just executing formulas that a computer can solve in a millisecond.”
Interview a person in the profession that you know – an older sibling, family friend, etc. If you cannot think of someone you know, you could interview an additional professor who works a lot in the field (some part-time professors are like that) or interview someone from the career center. Again, you can audio-record or take notes, and provide a summary. (2 points)
Interview questions:
“How do you foresee AI transforming core engineering workflows over the next decade, and which areas do you believe are currently most ‘ripe’ for disruption?”
“From my experience in planning, AI will mainly transform scheduling and delay analysis. A lot of what we do updating programs, tracking progress, identifying delays is repetitive and data-heavy. I see AI helping generate smarter schedules, predict delays earlier, and automate parts of claims analysis. Those areas are definitely the most ripe for disruption.”
“What are the most significant ethical or practical challenges we face as we integrate AI-driven decision-making into safety-critical infrastructure projects?”
“In planning for large projects, the risk is relying too much on AI outputs without fully understanding them. If an AI suggests a cause of delay or mitigation strategy, I’m still responsible for defending that logic. So transparency and accountability are key. AI should support my decisions, not replace my judgment.”
“How should the current construction engineering curriculum evolve to ensure students are prepared to collaborate effectively with AI agents in their future careers?”
“I think students should be exposed to real tools used in planning like scheduling software enhanced with AI along with basic data analysis skills. More importantly, they need to learn how to question outputs and think critically, because that’s what will differentiate a good engineer when AI becomes part of daily workflows.”
Provide your reflection: were you surprised? What about your findings excite or scare you? Are you feeling anything else? What do you think you need to be doing right now about what you’ve just learned? (4 points). There are 2 points on how critical your interview questions and reflection are. In order to question any hype or exaggeration or unsubstantiated claims in your interview, see the tips below on how to be critical. (2 points)
Comparing these two interviews makes it clear that the role of a junior engineer is being completely redefined. The Professor pointed out that the “boring” entry-level tasks are the first to be automated, and honestly, I am happy about that. I don’t think we need to go through gruesome, repetitive calculations just to prove our engineering acumen. By letting AI handle the manual grind, we can redirect our energy toward high-level critical thinking and complex problem-solving which are the things that actually make a difference on a project. Instead of being a human calculator, I see myself as a practitioner who uses these tools to get better results faster, focusing on the big picture rather than doing the data entry work that used to define a junior’s first few years.
However, both the Professor and the Professional raised a valid warning about over-dependence. Even though the AI’s output might look logical and polished, I have learned that it doesn’t always mean it is sound or practical for the real world. I have realized that I can’t and shouldn’t rely 100% on the machine’s logic. My goal is to use my own engineering acumen to scrutinize every output. As the Professional mentioned, the responsibility for a project ultimately falls on the human engineer. I want to be the safety net that catches the major errors that can truly divert a project, making sure that whatever the AI suggests is actually safe, ethical, and buildable before I put my name on it.
Finally, looking at the curriculum, there is a clear disconnect between the classroom and the industry. While my university is starting to adopt certain technologies, it still feels like we are stuck in traditional models of teaching that do not account for the constant, rapid changes happening in the field. We are still being taught obsolete methods as if the calculator and the computer do not exist. To be ready for the future, the content of what we learn needs to evolve just as fast as the tools we use. Instead of just learning old formulas, we need to be learning how to audit AI, how to manage data, and how to stay adaptable in a career that will likely look completely different every five years.
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