Skip to content
‹ Back to IndustriesSciences

Can Professors Use ChatGPT for Academic Research?

We have yet to reach the limit of what generative AI can consistently create. In fact, hobbyists and professionals are just getting started with leveraging ChatGPT for a variety of long-form text use cases. In just a few seconds, the tool can spit out five-paragraph essays on the metaphors of Romeo and Juliet. What about…

This story was produced through MarketScale. See how Sciences teams put it to work with Executive Thought Leadership.

By Education Technology · · Academic ResearchAutomationChatgptGenerative Ai
Share

Key takeaways

01

We have yet to reach the limit of what generative AI can consistently create.

02

In fact, hobbyists and professionals are just getting started with leveraging ChatGPT for a variety of long-form text use cases.

03

In just a few seconds, the tool can spit out five-paragraph essays on the metaphors of Romeo and Juliet.

Free workspace

Turn your Sciences expertise into content.

Record interviews, organize footage, and write with AI on a free trial of the MarketScale platform for qualifying companies. No demo required, no credit card.

Try it Free

We have yet to reach the limit of what generative AI can consistently create. In fact, hobbyists and professionals are just getting started with leveraging ChatGPT for a variety of long-form text use cases. In just a few seconds, the tool can spit out five-paragraph essays on the metaphors of Romeo and Juliet. What about a scientific essay? What about supporting the presentation of scientific theories? Could professors use ChatGPT for academic research?

Generative AI and ChatGPT automation allow for near-instant searches of massive datasets and the ability to spot inconsistencies that us users might have missed. While this can be a massive boon for scientists and researchers, issues arise with generative AI’s propensity for confidently presenting false information. For example, when asked to write that five-paragraph essay on Romeo and Juliet, if asked to show its sources, ChatGPT will simply make them up.

Despite these issues, René Morkos, the founder and CEO at generative AI company ALICE Technologies and Adjunct Professor of Construction Management at Stanford is confident that Generative AI and ChatGPT automation’s effects will ripple up and down the science vertical and larger academic field. He and other Stanford academics have already been deep in discussion around how to use ChatGPT for academic research. Here’s his take on how ChatGPT could support his own research.

René’s Thoughts:

“ChatGPT is going to change your life, but not the way you think. My name is René Morkos, and I’m an adjunct professor of construction management at Stanford University. I’m also the founder and CEO of ALICE Technologies, a generative AI company for construction that can generate millions of different ways to build a given construction project, reducing construction durations by 17%, and labor costs by 13%.

So, what does ChatGPT and other generative AI mean for the science vertical? We’ve been having that discussion amongst Stanford researchers for some time, and whether or not ChatGPT will fundamentally change how research is done. For example, not only are we asking if it can create papers but can it grade them?

When I’m doing research, I split my time into three categories: creating, arranging, and polishing. Which I work on is usually dictated by how much sleep I’ve gotten. Creating is making the actual pieces that compose the original body of intellectual work. This is a theoretical contribution, the hard part that you need to be fully present and creative for. Arranging is moving those pieces around to create a narrative. And polishing is ironing out the grammar, the formatting, or even writing a short introduction using other sources, stuff that’s relatively easy. ChatGPT is really good at receiving a query and outputting words in the order which it thinks is correct, but it uses large existing data sets to do so, and therefore is limited by what those data sets contain.

It can easily create inaccurate content. It does not have the ability to create new original contributions. It’ll be used for arranging and polishing in the research fields, even creating standard introductions, but it cannot create an original theory yet.”

Your experts belong here

Every story in MarketScale Sciences starts with a company putting its lab directors, applications scientists, and field specialists on the record. Buyers are already reading this topic. The only question is whose experts they find.

Lab and research buyers verify before they trial, and your scientists give them something credible to verify against.

Book DemoSee how it works15 minutes, straight to a calendar.

About the author

ET
Education Technology
B2B Weekly

The week in Sciences, and sixteen other industries, every Monday.

Ten stories, one-line takes, five minutes. Free.

Sciences: are you visible to AI?

Before they reach out, Sciences buyers ask AI engines which vendors to trust. Explore how your experts, customers, and partners can become useful content for buyers and AI search.

Free Trial

You just read one Sciences expert. Your company is full of them.

This article was produced through MarketScale. The same platform turns your lab directors, applications scientists, and field specialists into the articles, video, and social content Sciences buyers are searching for. Start a free trial and see it with your own people. For qualifying companies, no credit card, no demo required.

NPS +73 · 1,000+ creators · 38+ countries

What your free trial includes

Hands-on access to the MarketScale platform
Media requests to your crowd, remote recording, AI writing tools
No demo required. No credit card.
For qualifying companies. Company confirmation required.

More Sciences Insights

Why shared auto-injectors keep forcing rebuilt device files

Why shared auto-injectors keep forcing rebuilt device files

Vertex Knight co-founder Fubin Wu cites digitalization, personalization and large-volume delivery as trends reshaping drug delivery devices, arguing structured data can enable reuse and cut duplicated design-file work for shared auto-injectors. ISPE says no regulatory pathway exists for platform devices, and ONdrugDelivery counts 182 large-volume subcutaneous biologics, about 15% of IV and SC agents.

  • 01A device shared across drug products still triggers repeated design control work for each new combination product, and ISPE's November 2025 article says no regulatory pathway yet exists to approve a platform device.
  • 02FDA's May 2024 draft platform technology designation under section 506K is written for drug product applications, so device design files sit outside it.
  • 03Large-volume subcutaneous biologics above 2 mL numbered 182 in a January 2025 ONdrugDelivery review, about 15% of IV and SC agents; Advanced Drug Delivery Reviews reported in July 2026 that volumes up to 25 mL are now pursued.

Oct 22, 2026

Hepalink's FOYA-winning syringe plant inspects 24,000 units an hour

Hepalink's FOYA-winning syringe plant inspects 24,000 units an hour

Hepalink's Pingshan prefilled syringe plant in Shenzhen won the Innovation (Novel Technology) category at ISPE's 2026 Facility of the Year Awards. In the transcript ISPE posted with its podcast, CaiXue Wei describes vertical design, AGV-assisted transfer, AI inspection and 5G-MEC as one integrated system built around a 330 million-syringe annual capacity; Hepalink's own release adds a 24-meter warehouse, sub-10 millisecond 5G and 24,000-unit-an-hour inspection.

  • 01Hepalink's 24-meter automated warehouse raised space utilization 52 percent over a traditional layout on a land-scarce Shenzhen site supporting 330 million syringes a year.
  • 02AGVs pass material through vaporized hydrogen peroxide chambers straight into Grade A isolators, and Hepalink credits the zero-touch flow for a 100 percent first media fill.

Oct 22, 2026

Chalmers' AI scientist turned 60,000 yeast records into nearly 2,000 testable ideas

Chalmers' AI scientist turned 60,000 yeast records into nearly 2,000 testable ideas

Chalmers University of Technology's AI scientist Eve analyzed about 60,000 public yeast records, came up with almost 2,000 testable ideas, and took full charge of planning the experiments and telling other robots what to do, according to CORDIS. Humans handled safety checks and plate moving.

  • 01The key step for company labs is translation: turning hypotheses into machine-executable protocols and getting contextualized results back to the reasoning layer fast.

Oct 19, 2026

Explore More Sciences Insights

Read more expert perspectives from across Sciences.

Browse Sciences Hub

About the Expert

ET
Education Technology

For B2B teams

Your experts could be publishing here

Stories like this one run on content MarketScale captures from real practitioners. See how your team's expertise becomes coverage in Sciences and beyond.

Book a Demo

Or call us. No forms required. We pick up. 214-945-2512