AI Research

Artificial Intelligence in Games Market Size to Hit USD 37.89 Billion by 2034

Published

1 month ago

July 23, 2025

Artificial Intelligence (AI) in Games Market Size and Forecast 2025 to 2034

The global artificial intelligence (AI) in games market size was estimated at USD 5.85 billion in 2024 and is predicted to increase from USD 7.05 billion in 2025 to approximately USD 37.89 billion by 2034, expanding at a CAGR of 20.54% from 2025 to 2034. The growing popularity of online gaming and the rising demand for realistic gaming experiences boost the growth of the market.

Artificial Intelligence (AI) in Games Market Key Takeaways

In terms of revenue, the global artificial intelligence (AI) in games market was valued at USD 5.85 billion in 2024.

It is projected to reach USD 37.89 billion by 2034.

The market is expected to grow at a CAGR of 20.54% from 2025 to 2034.

North America dominated the artificial intelligence (AI) in games market with the largest market share of 39% in 2024.

Asia Pacific is expected to expand at the fastest CAGR during the forecast period.

By game genre, the action and adventure segment captured the biggest market share of 64% in 2024.

By game genre, the simulation segment is expected to grow at the fastest CAGR in the coming years.

By application, the game development and design segment contributed the highest market share of 41% in 2024.

By application, the NPC behavior/character AI segment is expected to expand at the fastest CAGR in the upcoming period.

By component/technology, the hardware (CPUs/GPUs) segment held a major market share of 62.5% in 2024.

By component/technology, the software and middleware segment is likely to expand at the fastest CAGR over the forecast period.

By platform/device, the mobile (smartphone & tablet) segment held approximately 52% in 2024.

By platform/device, the cloud / VR & AR segment is expected to grow at the fastest CAGR in the coming years.

By technology type, the machine learning/deep learning segment generated the major market share of 38% in 2024.

By technology type, the generative AI segment is expected to grow at the fastest CAGR during the projection period.

U.S. Artificial Intelligence (AI) in Games Market Size and Growth 2025 to 2034

The U.S. artificial intelligence (AI) in games market size was exhibited at USD 1.60 billion in 2024 and is projected to be worth around USD 10.55 billion by 2034, growing at a CAGR of 20.76% from 2025 to 2034.

U.S. Artificial Intelligence (AI) in Games Market Size 2025 to 2034

What Made North America the Dominant Region in the Artificial Intelligence (AI) In Games Market?

North America dominated the market with the largest share in 2024. This is mainly due to the presence of a large number of market players and a robust gaming ecosystem, creating a fertile ground for innovation. There is a strong focus on artificial intelligence technology, with significant investments in research and development. The presence of leading institutions like MIT and Stanford, along with private corporations such as Google and Microsoft, continuously pushes the boundaries of AI in gaming. Furthermore, the early adoption of new technologies by the American audience has also fueled the growth of AI in the gaming industry.

The U.S. is a major contributor to the market. This is mainly due to the increased popularity of mobile games. The country is an early adopter of AI technology in every field, supporting market growth. The country is home to leading AI companies and gaming developers, bringing innovations into the market.

Artificial Intelligence (AI) in Games Market Share, By Region, 2024 (%)

Why is Asia Pacific Experiencing the Fastest Growth in the Artificial Intelligence (AI) In Games Market?

The Asia Pacific region is expected to experience the fastest growth in the upcoming period due to a combination of factors. A primary factor is the large and ever-growing gaming population across the region, particularly in countries like China, India, and South Korea. This expansion is fueled by increased smartphone penetration, affordable internet access, and the rising popularity of mobile gaming. Furthermore, the presence of major players such as Tencent and NetEase, who are at the forefront of gaming innovation, supports regional market growth. The cultural affinity for gaming and the increasing acceptance of video games and e-sports also contribute to market growth.

Market Overview

The artificial intelligence (AI) in games market is experiencing rapid growth, driven by advancements in machine learning and deep learning. Artificial intelligence is revolutionizing the gaming industry by speeding up development, improving graphics, and enhancing overall game quality. AI enables the development of intelligent NPCs, dynamic environments, and personalized gameplay, while also preventing cheating in multiplayer games. AI-driven testing and optimization accelerate game development, with technologies like cloud, AR, VR, ML, and generative AI further transforming gaming. AR and VR, in particular, are creating more realistic gaming experiences, blurring the lines between the real and virtual worlds. Experts predict that advancements in computing power and GPUs will lead to incredibly realistic games, making it hard to tell the difference between reality and the game.

In game development, AI is bringing a revolution. Historically, game development has always been an expensive process. It required large hardware resources, a team of coders, graphic designers, sound designers, and game writers. Now, generative AI and agent-based AI tools allow even a single person with basic knowledge to create games using prompt engineering. Experienced developers are also using AI tools for rapid, cost-effective development of advanced games.

Artificial Intelligence (AI) in Games Market Growth Factors

The rising demand for personalized gaming experience boosts the growth of the market. AI is revolutionizing gaming by enabling realistic gameplay, dynamic storytelling, and more autonomous NPCs, creating an adaptive and personalized environment for players. Moreover, AI integration in AR and VR enhances immersion, adaptability, and personalization, creating a more realistic gaming experience.

Advancements in ML, deep learning, and natural language processing provide tools to create more sophisticated AI systems for gaming. These advancements allow for more complex NPC behaviors, improved game environments, and realistic player interactions.

The growing popularity of online games is likely to support market growth. Integrating AI algorithms into gaming platforms helps analyze player performance, predict outcomes, and create training tools for esports players.

Market Scope

Report Coverage	Details
Market Size by 2034	USD 37.89 Billion
Market Size in 2025	USD 7.05 Billion
Market Size in 2024	USD 5.85 Billion
Market Growth Rate from 2025 to 2034	CAGR of 20.54%
Dominating Region	North America
Fastest Growing Region	Asia Pacific
Base Year	2024
Forecast Period	2025 to 2034
Segments Covered	Game Genre, Application, Component/Technology, Platform/Device, Technology Type, and Region
Regions Covered	North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa

Market Dynamics

Drivers

Demand for Enhanced Gameplay Experience

The rising demand for enhanced gameplay experiences drives the growth of the artificial intelligence (AI) in games market. AI enhances gameplay through personalized experiences, intelligent NPCs, and dynamic environments, attracting more players. As games become more sophisticated, the demand for AI tools increases. AI also enables the creation of personalized gaming experiences that cater to individual player preferences and skill levels.

Technological advancements in GPUs are boosting performance while decreasing costs, leading to rapid growth in ultra-HD graphics and high-configuration video games across PC and console. This demand is further driving the need for more immersive games, where AI provides development tools, optimization during runtime, and, most importantly, enhances realism in non-player characters, thus transforming the overall gaming experience.

Restraint

Higher Investment, Integration Complexity, and Ethical Concerns

The implementation of AI-based features in games can be expensive, requiring specialized expertise, powerful hardware, and significant development time. This is not feasible for smaller game developers and independent studios, which creates an entry barrier for them. Integrating AI features in a game is a very complex process, requiring a thorough understanding of machine learning, game design, and programming. This complexity can lead to development challenges, delays, and the need for specialized talent. Furthermore, the use of AI in gaming raises concerns related to data privacy, algorithmic bias, and the potential for AI-driven manipulation, hampering the growth of the market.

Opportunity

Generative AI Leading to Limitless Possibilities

Generative AI possesses the capability to generate nearly infinite, dynamic worlds. This technology allows for the dynamic generation of game content, personalized player experiences, and streamlined development processes. This can lead to increased player engagement, reduced development costs, and the creation of more immersive and adaptive gaming worlds. This innovative approach allows for the unique generation of procedural content, dynamically adjusting the game’s difficulty by analyzing player gameplay patterns, and enabling NPCs to engage in dynamic, responsive interactions. Furthermore, the narrative can be intelligently directed based on the player’s individual interests, thus enhancing overall engagement. In the realm of game development, AI can significantly expedite the coding process, automate rigorous testing procedures, and efficiently identify and rectify bugs, ultimately leading to a reduction in both the time and associated costs required for game development.

Game Genre Insights

Why Did the Action & Adventure Segment Dominate the Artificial Intelligence (AI) in Games Market in 2024?

The action & adventure segment dominated the market with a major revenue share in 2024. This dominance stems from the capacity of these games to deliver high-intensity, engaging gameplay, complemented by dynamic storytelling within immersive, open-world environments. Action and adventure games predominantly incorporate real-time combat mechanics, expansive environments, and challenges enhanced by AI-driven tools, collectively elevating the overall player experience within the gaming environment.

The simulation segment is expected to experience rapid growth in the upcoming period, driven by the increasing adoption of advanced AI technology by game developers to create interactive gaming experiences. AI-generated content, such as 3D representations of the real world and physics-based simulations, is transforming the gaming experience and revolutionizing the development environment. Simulation games are becoming more realistic and immersive due to AI, driving user engagement and retention, supporting segmental growth.

Application Insights

How Does the Game Development & Design Segment Dominate the Artificial Intelligence (AI) in Games Market in 2024?

The game development & design segment dominated the artificial intelligence (AI) in games market in 2024. The dominance of the segment is attributed to the extensive use of generative and agentic AI tools in the development process. These tools are used for code development, initial setups, debugging, graphic design, sound optimization, and various other tasks, providing high-speed, high-quality, and cost-effective implementation. AI can automate many aspects of game development, such as level design, content generation, and testing, which reduces development time and costs.

The NPC behavior/character AI segment is expected to grow at the highest CAGR over the forecast period due to the increasing need for realistic player interactions. The integration of AI in games allows non-player characters to have dynamic, autonomous behaviour, which significantly enhances the overall gaming experience. Interactions and realistic behaviors lead to a complete change in the entire gaming experience, which is leading to the rapid adoption of this technology in various games and bolstering the segment’s growth.

Component/Technology Insights

What Made Hardware (CPUs/GPUs) the Dominant Segment in the Artificial Intelligence (AI) in Games Market in 2024?

The hardware (CPUs/GPUs) segment dominated the market in 2024. This is mainly due to the strong demand for CPUs and GPUs, driven by the increasing computational needs of high-configuration games. These games often require more powerful hardware to support their graphics and size. The rising integration of AI in games requires powerful hardware to process complex computations, driving the demand for high-performance CPUs and GPUs. The rising development of specialized GPUs for AI workloads facilitates the long-term growth of the segment.

The software & middleware segment is likely to grow at the fastest rate in the market over the forecast. This is mainly due to the increased need to analyze user behavior to enhance gameplay. Software solutions are crucial in streamlining game development on a large scale. They process automation, such as game testing, bug detection, and quality assurance, helping developers rapidly create game features.

Platform/Device Insights

Why Did the Mobile Segment Dominate the Artificial Intelligence (AI) in Games Market in 2024?

The mobile (smartphone and tablet) segment dominated artificial intelligence (AI) in games market in 2024. The segment’s dominance is attributed to the high convenience and low cost of smartphones. Smartphones are highly accessible and require minimal setup compared to PCs and consoles, and are also very inexpensive, making them widely adopted by game enthusiasts. The increased popularity of mobile games further bolstered segmental growth.

The cloud/ VR & AR segment is expected to expand at the fastest growth rate in the coming years due to the increasing demand for cloud gaming. Cloud gaming allows users to play processing-intensive games that require significant memory, GPU, and CPU, which may not be available on native machines. Users can play games on a cloud platform for a nominal fee, which is also cost-effective. virtual reality (VR) and augmented reality (AR) technologies, with AI integration, are poised to revolutionize the gaming industry, potentially eliminating the distinction between real life and the virtual gaming world.

Technology Type Insights

How Does the Machine Learning/Deep Learning Segment Dominate the Artificial Intelligence (AI) in Games Market in 2024?

The machine learning/deep learning segment dominated the artificial intelligence (AI) in games market in 2024. The dominance of this segment stems from the crucial role of machine learning in games, providing personalization & customization, calibrating difficulty levels, and setting the game narrative based on the player’s gaming patterns. This enhances the game’s immersive and engaging experience by learning from historical data, player patterns, and developing dynamic game scenarios.

The generative AI segment is expected to grow at the fastest rate. AI is showing great potential in game development and gameplay, especially in open-world games, for dynamic stages and character building. It supports developers during the development phase, enabling faster and higher-quality game development.

Artificial Intelligence (AI) in Games Market Companies

Activision Blizzard Inc.

Amazon Web Services Inc.

Bandai Namco Entertainment Inc.

DeepMind (Google)

Electronic Arts Inc.

Epic Games Inc.

Google LLC

IBM Corporation

Intel Corporation

Microsoft Corporation

NetEase Inc.

Niantic Inc.

NVIDIA Corporation

Sony Interactive Entertainment

Square Enix Holdings Co. Ltd.

Supercell Oy

Take-Two Interactive Software Inc.

Tencent Holdings Limited

Ubisoft Entertainment SA

Unity Technologies

Recent Developments

In July 2025, Dell Technologies expanded its consumer and gaming portfolio in India with the launch of its new Dell Plus AI PC range and Alienware Area-51 and Aurora gaming laptops. This launch underlines Dell’s focus on delivering powerful performance, immersive experiences, and next-generation AI capabilities to Indian users. (Source: https://www.moneycontrol.com)

In November 2024, Elon Musk announced the creation of an AI-powered games studio, which will revolutionize the gaming industry. As per the initiative taken by Musk, it will leverage cutting-edge AI to develop innovative games to challenge current industry norms. (Source: https://www.firstpost.com)

Segments Covered in the Report

By Game Genre

Action & Adventure

RoleâPlaying Games (RPG)

Strategy Games

Simulation

By Application

Game Development & Design

Gameplay Optimization / Player Experience

NPC Behavior / Character AI – Fastest

Game Testing & QA

Procedural Content Generation

InâGame Marketing

Cloud Gaming Optimization

By Component / Technology

Hardware (CPUs/GPUs)

Software & Middleware

AIâEnhanced Platforms / Engines

By Platform / Device

Mobile (Smartphone & Tablet)

Console

Cloud / VR & AR

By Technology Type

Machine Learning / Deep Learning

Generative AI – Fastest-growing

AI Agents

Computer Vision

By Region

North America

Europe

Asia Pacific

Latin America

Middle East & Africa

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AI Research

Prediction: This Artificial Intelligence (AI) Semiconductor Stock Will Join Nvidia, Microsoft, Apple, Alphabet, and Amazon in the $2 Trillion Club by 2028. (Hint: Not Broadcom)

Published

2 hours ago

August 30, 2025

Adam Levy

This company is growing quickly, and its stock is a bargain at the current price.

Big tech companies are set to spend $375 billion on artificial intelligence (AI) infrastructure this year, according to estimates from analysts at UBS. That number will climb to $500 billion next year.

The biggest expense item in building out AI data centers is semiconductors. Nvidia (NVDA -3.38%) has been by far the biggest beneficiary of that spend so far. Its GPUs offer best-in-class capabilities for general AI training and inference. Other AI accelerator chipmakers have also seen strong sales growth, including Broadcom (AVGO -3.70%), which makes custom AI chips as well as networking chips, which ensure data moves efficiently from one server to another, keeping downtime to a minimum.

Broadcom’s stock price has increased more than fivefold since the start of 2023, and the company now sports a market cap of $1.4 trillion. Another year of spectacular growth could easily place it in the $2 trillion club. But another semiconductor stock looks like a more likely candidate to reach that vaunted level, joining Nvidia and the four other members of the club by 2028.

Image source: Getty Images.

Is Broadcom a $2 trillion company?

Broadcom is a massive company with operations spanning hardware and software, but its AI chips business is currently steering the ship.

To that end, AI revenue climbed 46% year over year last quarter to reach $4.4 billion. Management expects the current quarter to produce $5.1 billion in AI semiconductor revenue, accelerating growth to roughly 60%. AI-related revenue now accounts for roughly 30% of Broadcom’s sales, and that’s set to keep climbing over the next few years.

Broadcom’s acquisition of VMware last year is another growth driver. The software company is now fully integrated into Broadcom’s larger operations, and it’s seen strong success in upselling customers to the VMware Cloud Foundation, enabling enterprises to run their own private clouds. Over 87% of its customers have transitioned to the new subscription, resulting in double-digit growth in annual recurring revenue.

But Broadcom shares are extremely expensive. The stock garners a forward P/E ratio of 45. While its AI chip sales are growing quickly and it’s seeing strong margin improvement from VMware, it’s important not to lose sight of how broad a company Broadcom is. Despite the stellar growth in those two businesses, the company is still only growing its top line at about 20% year over year. Investors should expect only incremental margin improvements going forward as it scales the AI accelerator business. That means the business is set up for strong earnings growth, but not enough to justify its 45 times earnings multiple.

Another semiconductor stock trades at a much more reasonable multiple, and is growing just as fast.

The semiconductor giant poised to join the $2 trillion club by 2028

Both Broadcom and Nvidia rely on another company to ensure they can create the most advanced semiconductors in the world for AI training and inference. That company is Taiwan Semiconductor Manufacturing (TSM -3.05%), which actually prints and packages both companies’ designs. Almost every company designing leading-edge chips relies on TSMC for its technological capabilities. As a result, its market share of semiconductor manufacturing has climbed to more than two-thirds.

TSMC benefits from a virtuous cycle, ensuring it maintains and grows its massive market share. Its technology lead helps it win big contracts from companies like Nvidia and Broadcom. That gives it the capital to invest in expanding capacity and research and development for its next-generation process. As a result, it maintains its technology lead while offering enough capacity to meet the growing demand for manufacturing.

TSMC’s leading-edge process node, dubbed N2, will reportedly charge a 66% premium per silicon wafer over the previous generation (N3). That’s a much bigger step-up in price than it’s historically managed, but the demand for the process is strong as companies are willing to spend whatever it takes to access the next bump in power and energy efficiency. While TSMC typically experiences a significant drop off in gross margin as it ramps up a new expensive node with lower initial yields, its current pricing should help it maintain its margins for years to come as it eventually transitions to an even more advanced process next year.

Management expects AI-related revenue to average mid-40% growth per year from 2024 through 2029. While AI chips are still a relatively small part of TSMC’s business, that should produce overall revenue growth of about 20% for the business. Its ability to maintain a strong gross margin as it ramps up the next two manufacturing processes should allow it to produce operating earnings growth exceeding that 20% mark.

TSMC’s stock trades at a much more reasonable earnings multiple of 24 times expectations. Considering the business could generate earnings growth in the low 20% range, that’s a great price for the stock. If it can maintain that earnings multiple through 2028 while growing earnings at about 20% per year, the stock will be worth well over $2 trillion at that point.

Adam Levy has positions in Alphabet, Amazon, Apple, Microsoft, and Taiwan Semiconductor Manufacturing. The Motley Fool has positions in and recommends Alphabet, Amazon, Apple, Microsoft, Nvidia, and Taiwan Semiconductor Manufacturing. The Motley Fool recommends Broadcom and recommends the following options: long January 2026 $395 calls on Microsoft and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy.

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AI Research

Physicians Lose Cancer Detection Skills After Using Artificial Intelligence

Published

3 hours ago

August 30, 2025

Paul Hsieh

Artificial intelligence shows great promise in helping physicians improve both their diagnostic accuracy of important patient conditions. In the realm of gastroenterology, AI has been shown to help human physicians better detect small polyps (adenomas) during colonoscopy. Although adenomas are not yet cancerous, they are at risk for turning into cancer. Thus, early detection and removal of adenomas during routine colonoscopy can reduce patient risk of developing future colon cancers.

But as physicians become more accustomed to AI assistance, what happens when they no longer have access to AI support? A recent European study has shown that physicians’ skills in detecting adenomas can deteriorate significantly after they become reliant on AI.

The European researchers tracked the results of over 1400 colonoscopies performed in four different medical centers. They measured the adenoma detection rate (ADR) for physicians working normally without AI vs. those who used AI to help them detect adenomas during the procedure. In addition, they also tracked the ADR of the physicians who had used AI regularly for three months, then resumed performing colonoscopies without AI assistance.

The researchers found that the ADR before AI assistance was 28% and with AI assistance was 28.4%. (This was a slight increase, but not statistically significant.) However, when physicians accustomed to AI assistance ceased using AI, their ADR fell significantly to 22.4%. Assuming the patients in the various study groups were medically similar, that suggests that physicians accustomed to AI support might miss over a fifth of adenomas without computer assistance!

This is the first published example of so-called medical “deskilling” caused by routine use of AI. The study authors summarized their findings as follows: “We assume that continuous exposure to decision support systems such as AI might lead to the natural human tendency to over-rely on their recommendations, leading to clinicians becoming less motivated, less focused, and less responsible when making cognitive decisions without AI assistance.”

Consider the following non-medical analogy: Suppose self-driving car technology advanced to the point that cars could safely decide when to accelerate, brake, turn, change lanes, and avoid sudden unexpected obstacles. If you relied on self-driving technology for several months, then suddenly had to drive without AI assistance, would you lose some of your driving skills?

Although this particular study took place in the field of gastroenterology, I would not be surprised if we eventually learn of similar AI-related deskilling in other branches of medicine, such as radiology. At present, radiologists do not routinely use AI while reading mammograms to detect early breast cancers. But when AI becomes approved for routine use, I can imagine that human radiologists could succumb to a similar performance loss if they were suddenly required to work without AI support.

I anticipate more studies will be performed to investigate the issue of deskilling across multiple medical specialties. Physicians, policymakers, and the general public will want to ask the following questions:

1) As AI becomes more routinely adopted, how are we tracking patient outcomes (and physician error rates) before AI, after routine AI use, and whenever AI is discontinued?

2) How long does the deskilling effect last? What methods can help physicians minimize deskilling, and/or recover lost skills most quickly?

3) Can AI be implemented in medical practice in a way that augments physician capabilities without deskilling?

Deskilling is not always bad. My 6th grade schoolteacher kept telling us that we needed to learn long division because we wouldn’t always have a calculator with us. But because of the ubiquity of smartphones and spreadsheets, I haven’t done long division with pencil and paper in decades!

I do not see AI completely replacing human physicians, at least not for several years. Thus, it will be incumbent on the technology and medical communities to discover and develop best practices that optimize patient outcomes without endangering patients through deskilling. This will be one of the many interesting and important challenges facing physicians in the era of AI.

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AI Research

AI exposes 1,000+ fake science journals

Published

3 hours ago

August 30, 2025

The Editors

A team of computer scientists led by the University of Colorado Boulder has developed a new artificial intelligence platform that automatically seeks out “questionable” scientific journals.

The study, published Aug. 27 in the journal “Science Advances,” tackles an alarming trend in the world of research.

Daniel Acuña, lead author of the study and associate professor in the Department of Computer Science, gets a reminder of that several times a week in his email inbox: These spam messages come from people who purport to be editors at scientific journals, usually ones Acuña has never heard of, and offer to publish his papers — for a hefty fee.

Such publications are sometimes referred to as “predatory” journals. They target scientists, convincing them to pay hundreds or even thousands of dollars to publish their research without proper vetting.

“There has been a growing effort among scientists and organizations to vet these journals,” Acuña said. “But it’s like whack-a-mole. You catch one, and then another appears, usually from the same company. They just create a new website and come up with a new name.”

His group’s new AI tool automatically screens scientific journals, evaluating their websites and other online data for certain criteria: Do the journals have an editorial board featuring established researchers? Do their websites contain a lot of grammatical errors?

Acuña emphasizes that the tool isn’t perfect. Ultimately, he thinks human experts, not machines, should make the final call on whether a journal is reputable.

But in an era when prominent figures are questioning the legitimacy of science, stopping the spread of questionable publications has become more important than ever before, he said.

“In science, you don’t start from scratch. You build on top of the research of others,” Acuña said. “So if the foundation of that tower crumbles, then the entire thing collapses.”

The shake down

When scientists submit a new study to a reputable publication, that study usually undergoes a practice called peer review. Outside experts read the study and evaluate it for quality — or, at least, that’s the goal.

A growing number of companies have sought to circumvent that process to turn a profit. In 2009, Jeffrey Beall, a librarian at CU Denver, coined the phrase “predatory” journals to describe these publications.

Often, they target researchers outside of the United States and Europe, such as in China, India and Iran — countries where scientific institutions may be young, and the pressure and incentives for researchers to publish are high.

“They will say, ‘If you pay $500 or $1,000, we will review your paper,'” Acuña said. “In reality, they don’t provide any service. They just take the PDF and post it on their website.”

A few different groups have sought to curb the practice. Among them is a nonprofit organization called the Directory of Open Access Journals (DOAJ). Since 2003, volunteers at the DOAJ have flagged thousands of journals as suspicious based on six criteria. (Reputable publications, for example, tend to include a detailed description of their peer review policies on their websites.)

But keeping pace with the spread of those publications has been daunting for humans.

To speed up the process, Acuña and his colleagues turned to AI. The team trained its system using the DOAJ’s data, then asked the AI to sift through a list of nearly 15,200 open-access journals on the internet.

Among those journals, the AI initially flagged more than 1,400 as potentially problematic.

Acuña and his colleagues asked human experts to review a subset of the suspicious journals. The AI made mistakes, according to the humans, flagging an estimated 350 publications as questionable when they were likely legitimate. That still left more than 1,000 journals that the researchers identified as questionable.

“I think this should be used as a helper to prescreen large numbers of journals,” he said. “But human professionals should do the final analysis.”

A firewall for science

Acuña added that the researchers didn’t want their system to be a “black box” like some other AI platforms.

“With ChatGPT, for example, you often don’t understand why it’s suggesting something,” Acuña said. “We tried to make ours as interpretable as possible.”

The team discovered, for example, that questionable journals published an unusually high number of articles. They also included authors with a larger number of affiliations than more legitimate journals, and authors who cited their own research, rather than the research of other scientists, to an unusually high level.

The new AI system isn’t publicly accessible, but the researchers hope to make it available to universities and publishing companies soon. Acuña sees the tool as one way that researchers can protect their fields from bad data — what he calls a “firewall for science.”

“As a computer scientist, I often give the example of when a new smartphone comes out,” he said. “We know the phone’s software will have flaws, and we expect bug fixes to come in the future. We should probably do the same with science.”

Co-authors on the study included Han Zhuang at the Eastern Institute of Technology in China and Lizheng Liang at Syracuse University in the United States.

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