ChatGPT-5.2 Now Achieves “Expert-Level” Performance — Is this the Holiday Gift Research Communications Professionals Needed?

With OpenAI’s latest release, GPT-5.2, AI has crossed an important threshold in performance on professional knowledge-work benchmarks. Peter Evans, Co-Founder & CEO of ExpertFile, outlines how these technologies will fundamentally improve research communications and shares tips and prompts for PR pros.

OpenAI has just launched GPT-5.2, describing it as its most capable AI model yet for professional knowledge work — with significantly improved accuracy on tasks like creating spreadsheets, building presentations, interpreting images, and handling complex multistep workflows. And based on our internal testing, we're really impressed.

For communications professionals in higher education, non-profits, and R&D-focused industries, this isn’t just another tech upgrade — it’s a meaningful step forward in addressing the “research translation gap” that can slow storytelling and media outreach.

According to OpenAI, GPT-5.2 represents measurable gains on benchmarks designed to mirror real work tasks.  In many evaluations, it matches or exceeds the performance of human professionals.

Also, before you hit reply with “Actually, the best model is…” — yes, we know. ChatGPT-5.2 isn’t the only game in town, and it’s definitely not the only tool we use. Our ExpertFile platform uses AI throughout, and I personally bounce between Claude 4.5, Gemini, Perplexity, NotebookLM, and more specialized models depending on the job to be done. LLM performance right now is a full-contact horserace — today’s winner can be tomorrow’s “remember when,” so we’re not trying to boil the ocean with endless comparisons. We’re spotlighting GPT-5.2 because it marks a meaningful step forward in the exact areas research comms teams care about: reliability, long-document work, multi-step tasks, and interpreting visuals and data.

Most importantly, we want this info in your hands because a surprising number of comms pros we meet still carry real fear about AI — and long term, that’s not a good thing. Used responsibly, these tools can help you translate research faster, find stronger story angles, and ship more high-quality work without burning out.

When "Too Much" AI Power Might Be Exactly What You Need

AI expert Allie K. Miller's candid but positive review of an early testing version of ChatGPT 5.2 highlights what she sees as drawbacks for casual users: "outputs that are too long, too structured, and too exhaustive."  She goes on to say that in her tests, she observed that ChatGPT-5,2 "stays with a line of thought longer and pushes into edge cases instead of skating on the surface."

Fair enough.

All good points that Allie Miller makes (see above).  However, for communications professionals, these so-called "downsides" for casual users are precisely the capabilities we need. When you're assessing complex research and developing strategic messaging for a variety of important audiences, you want an AI that fits Miller's observation that GPT-5.2 feels like "AI as a serious analyst" rather than "a friendly companion."

That's not a critique of our world—it's a job description for comms pros working in sectors like higher education and healthcare. Deep research tools that refuse to take shortcuts are exactly what research communicators need. 

So let's talk more specifically about how comms pros can think about these new capabilities:

1. AI is Your New Speed-Reading Superpower for Research

That means you can upload an entire NIH grant, a full clinical trial protocol, or a complex environmental impact study and ask the model to highlight where key insights — like an unexpected finding — are discussed. It can do this in a fraction of the time it would take a human reader.

This isn’t about being lazy. It’s about using AI to assemble a lot of tedious information you need to craft compelling stories while teams still parse dense text manually.

2. The Chart Whisperer You’ve Been Waiting For

We’ve all been there — squinting at a graph of scientific data that looks like abstract art, waiting for the lead researcher to clarify what those error bars actually mean.

Recent improvements in how GPT-5.2 handles scientific figures and charts show stronger performance on multimodal reasoning tasks, indicating better ability to interpret and describe visual information like graphs and diagrams. 

With these capabilities, you can unlock the data behind visuals and turn them into narrative elements that resonate with audiences.

3. A Connection Machine That Finds Stories Where Others See Statistics

Great science communication isn’t about dumbing things down — it’s about building bridges between technical ideas and the broader public.

GPT-5.2 shows notable improvements in abstract reasoning compared with earlier versions, based on internal evaluations on academic reasoning benchmarks. 

For example, teams working on novel materials science or emerging health technologies can use this reasoning capability to highlight connections between technical results and real-world impact — something that previously required hours of interpretive work.

These gains help the AI spot patterns and relationships that can form the basis of compelling storytelling.

4. Accuracy That Gives You More Peace of Mind...When Coupled With Human Oversight

Let’s address the elephant in the room: AI hallucinations.

You’ve probably heard the horror stories — press releases that cited a study that didn’t exist, or a “quote” that was never said by an expert.

GPT-5.2 has meaningfully reduced error rates compared with its predecessor, by a substantial margin, according to OpenAI 

Even with all these improvements, human review with your experts and careful editing remain essential, especially for anything that will be published or shared externally.

5. The Speed Factor: When “Urgent” Actually Means Urgent

With the speed of media today, being second often means being irrelevant.  GPT-5.2’s performance on workflow-oriented evaluations suggests it can synthesize information far more quickly than manual review, freeing up a lot more time for strategic work. 

While deeper reasoning and longer contexts — the kinds of tasks that matter most in research translation — require more processing time and costs continue to improve.

Savvy communications teams will adopt a tiered approach: using faster models of AI for simple tasks such as social posts and routine responses, and using reasoning-optimized settings for deep research.

Your Action Plan: The GPT-5.2 Playbook for Comms Pros

Here’s a tactical checklist to help your team capitalize on these advances.

#1 Select the Right AI Model for the Job: Lowers time and costs

• Use fast, general configurations for routine content

• Use reasoning-optimized configurations for complex synthesis and deep document understanding

• Use higher-accuracy configurations for high-stakes projects

#2 Find Hidden Ideas Beyond the Abstract: Deeper Reasoning Models do the Heavy Work

• Upload complete PDFs — not just the 2-page summary you were given

• Use deeper reasoning configurations to let the model work through the material

Try these prompts in ChatGPT5.2

“What exactly did the researchers say about this unexpected discovery that would be of interest to my <target audience>? Provide quotes and page references where possible.”

“Identify and explain the research methodology used in this study, with references to specific sections.”

“Identify where the authors discuss limitations of the study.”

“Explain how this research may lead to further studies or real-world benefits, in terms relatable to a general audience.”

#3 Unlock Your Story

Leverage improvements in pattern recognition and reasoning.

Try these prompts:

“Using abstract reasoning, find three unexpected analogies that explain this complex concept to a general audience.”

“What questions could the researchers answer in an interview that would help us develop richer story angles?”

#4 Change the Way You Write Captions

Take advantage of the way ChatGPT-5.2 translates processes and reasons about images, charts, diagrams, and other visuals far more effectively.

Try these prompts:

Clinical Trial Graphs: “Analyze this uploaded trial results graph upload image. Identify key trends, and comparisons to controls, then draft a 150-word donor summary with plain-language explanations and suggested captions suitable for donor communications.”

Medical Diagrams: “Interpret these uploaded images. Extract diagnostic insights, highlight innovations, and generate a patient-friendly explainer: bullet points plus one visual caption.”

A Word of Caution: Keep Experts in the Loop to Verify Information

Even with improved reliability, outputs should be treated as drafts.  If your team does not yet have formal AI use policies, it's time to get started, because governance will be critical as AI use scales in 2026 and beyond. 

A trust-but-verify policy with experts treats AI as a co-pilot — helpful for heavy lifting — while humans remain accountable for approval and publication. 

The Importance of Humans (aka The Good News)

Remember: the future of research communication isn’t about AI taking over — it’s about AI empowering us to do the strategic, human work that machines cannot.

That includes:

• Building relationships across your institution

• Engaging researchers in storytelling

• Discovering narrative opportunities

• Turning discoveries into compelling narratives that influence audiences

With improvements in speed, reasoning, and reliability, the question isn’t whether AI can help — it’s what research stories you’ll uncover next to shape public understanding and impact.

FAQ

How is AI changing expectations for accuracy in research and institutional communications?

AI is shifting expectations from “fast output” to defensible accuracy. Better reasoning means fewer errors in research summaries, policy briefs, and expert content—especially when you’re working from long PDFs, complex methods, or dense results. The new baseline is: clear claims, traceable sources, and human review before publishing.

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Why does deeper AI reasoning matter for communications teams working with experts and research content?

Comms teams translate multi-disciplinary research into messaging that must withstand scrutiny. Deeper reasoning helps AI connect findings to real-world relevance, flag uncertainty, and maintain nuance instead of flattening meaning. The result is work that’s easier to defend with media, leadership, donors, and the public—when paired with expert verification.

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When should communications professionals use advanced AI instead of lightweight AI tools?

Use lightweight tools for brainstorming, social drafts, headlines, and quick rewrites. Use advanced, reasoning-optimized AI for high-stakes deliverables: executive briefings, research positioning, policy-sensitive messaging, media statements, and anything where a mistake could create reputational, compliance, or scientific credibility risk. Treat advanced AI as your “analyst,” not your autopilot.

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How can media relations teams use AI to find stronger story angles beyond the abstract?

AI can scan full papers, grants, protocols, and appendices to surface where the real story lives: unexpected findings, practical implications, limitations, and unanswered questions that prompt great interviews. Ask it to map angles by audience (public, policy, donors, clinicians) and to point to the exact sections that support each angle.

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How should higher-ed comms teams use AI without breaking embargoes or media timing?

AI can speed prep work—backgrounders, Q&A, lay summaries, caption drafts—before embargo lifts. The rule is simple: treat embargoed material like any sensitive document. Use approved tools, restrict sharing, and avoid pasting embargoed text into unapproved systems. Use AI to build assets early, then finalize post-approval at release time.

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What’s the best way to keep faculty “in the loop” while still moving fast with AI?

Use AI to produce review-friendly drafts that reduce load on researchers: short summaries, suggested quotes clearly marked as drafts, and a checklist of claims needing verification (numbers, methods, limitations). Then route to the expert with specific questions, not a wall of text. This keeps approvals faster while protecting scientific accuracy and trust.

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How should teams handle charts, figures, and visual data in research communications?

AI can turn “chart confusion” into narrative—if you prompt for precision. Ask it to identify trends, group comparisons, and what the figure does not show (limitations, missing context). Then verify with the researcher, especially anything involving significance, controls, effect size, or causality. Use the output to write captions that are accurate and accessible.

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Do we need an AI Use policy in comms and media relations—and what should it include?

Yes—because adoption scales faster than risk awareness. A practical policy should define: approved tools, what data is restricted, required human review steps, standards for citing sources/page references, rules for drafting quotes, and escalation paths for sensitive topics (health, legal, crisis). Clear guardrails reduce fear and prevent preventable reputational mistakes.

If you’re using AI to move faster on research translation, the next bottleneck is usually the same one for many PR and Comm Pros: making your experts more discoverable in Generative Search, your website, and other media. ExpertFile helps media relations and digital teams organize their expert content by topics, keep detailed profiles current, and respond faster to source requests—so you can boost your AI citations and land more coverage with less work.                                           

For more information visit us at www.expertfile.com