AI Research
How artificial intelligence is reprogramming the future of games
Artificial intelligence has been transforming the world in recent years across nearly every field, from medicine and education to transportation, finance, design, and art. In the gaming industry, one of the most creative and fastest-growing sectors, it is driving profound change: it’s no longer just about advanced graphics or new combat mechanics, but about a sweeping revolution that brings intelligent characters, real-time story generation, and personalized experiences that reshape the relationship between player and game.
AI not only upgrades the look of characters and generates new worlds, it is also changing workflows, transforming business models, and, most importantly, creating a more personalized and complex gaming experience than ever before.
A 2022 Deloitte report showed that more than 70% of large game development companies had already set up dedicated AI teams working across departments. These teams are responsible for developing and implementing AI technologies while overseeing internal governance and quality control.
Ubisoft, for example, uses Ghostwriter to write initial dialogue for NPCs (non-player characters), saving the writing team hundreds of hours and freeing them to craft richer, more complex storylines. EA, for its part, uses AI models to detect bugs and test game balance, dramatically shortening testing cycles while maintaining product stability over time.
This trend is also evident in Israel. Uri Rubin, CTO at Playtika, says the company is adapting its operations to meet new realities. “One of the best examples is moving an independent business unit (studio) into the technology division,” he says. “Changing an organizational culture that has existed for years requires external momentum, and that’s exactly what AI provides. The idea is to break out of the traditional structure where the studio and technology teams work separately, and instead combine forces with a shared focus. For the first time, teams are working together toward one goal.”
Rubin notes that integrating AI into the studio’s workflows allows the company to deliver more precise and efficient solutions: “This change ensures that the studio and technology teams stay aligned and focused on shared tasks. We’re learning as we go and believe that if this transformation succeeds, we can replicate it across other units.”
According to Shahar Sorek, CMO at Overwolf, “Every department in the company already uses AI. In my department alone, we rely on at least ten different tools daily – for design, writing, video editing, supporting internal workflows, and even accelerating coding. These tools let us work at a much larger scale.”
However, Sorek stresses that adopting AI requires extra caution for sensitive tasks, especially those involving a product’s core elements. “The more critical the product, like the code, game engine, or sensitive data, the more carefully you need to introduce AI. After all, it’s not your own tool, it’s not built in-house, and you can’t always know where your exposed data might end up or how it could be used.”
Despite the enormous savings in time and resources, Sorek insists the new technology does not come at the expense of human workers. “I’m not laying people off,” he clarifies. “The more complex the product, the more people you still need.”
“Technology isn’t going to replace people, it’s going to upgrade them,” Rubin adds. “We’ll always need the human factor on every team, but at the same time, AI will play a significant role in informing decisions. AI will handle tedious, repetitive tasks and free up teams for creativity, critical thinking, and faster, more dynamic decision-making.”
Where the industry once focused on linear, fixed, and predefined games, AI is now making gameplay dynamic, multi-layered, and deeply personal. There are already plenty of examples that show how far the technology has come.
In Minecraft, for instance, the world’s best-selling game, modding communities (independent developers who expand the base game) have created AI-powered add-ons that let players generate entire worlds automatically. The player simply enters a short description, for example, “a medieval-style underground dungeon with traps and rare creatures,” and the system builds it in minutes, complete with enemies, quests, hidden items, and surprises. Such add-ons have become especially popular on private servers, where developers craft unique adventures for each player or group.
There’s also been a major leap in NPC technology. Inworld AI, an American company, has developed a platform for creating interactive characters that understand context, retain memory, and can hold complex conversations with players. These characters learn player behavior and adapt their responses in real time, expressing emotions, responding with humor or anger, and maintaining a history of interactions so players can enjoy long, playful exchanges or even philosophical debates.
Ubisoft has also integrated characters whose shifting personalities affect how the entire game unfolds: if a player chooses a violent approach with an NPC, the game opens new subplots that can change the ending entirely.
CD Projekt Red, the developer behind Cyberpunk 2077, is experimenting with AI systems that track players’ play styles over time. If a player often helps weaker characters, the game will offer more emotional and layered plots; if they play aggressively, it will unlock storylines packed with battles and challenges.
“The goal is for every player to ultimately have an experience tailored to their preferences and play style,” says Rubin. “AI will help us produce far more content, giving players a richer, more personalized experience.”
Sorek believes the next big leap will be full-game generation, an AI engine that receives a general prompt and builds an entire custom game. “The AI will learn who you are, what you like, and generate a game for you as you play,” he says. “Of course, there are still challenges, games are complex, both visually and emotionally. The AI isn’t quite there yet, but we’re getting closer. New companies are already developing AI that can manage interactions, build mini-games, and even run entire game engines.”
AI Research
Report shows China outpacing the US and EU in AI research
Governments now face the reality that falling behind in AI capability could have serious geopolitical consequences, warns a new research report.
AI is increasingly viewed as a strategic asset rather than a technological development, and new research suggests China is now leading the global AI race.
A report titled ‘DeepSeek and the New Geopolitics of AI: China’s ascent to research pre-eminence in AI’, authored by Daniel Hook, CEO of Digital Science, highlights how China’s AI research output has grown to surpass that of the US, the EU and the UK combined.
According to data from Dimensions, a primary global research database, China now accounts for over 40% of worldwide citation attention in AI-related studies. Instead of focusing solely on academic output, the report points to China’s dominance in AI-related patents.
In some indicators, China is outpacing the US tenfold in patent filings and company-affiliated research, signalling its capacity to convert academic work into tangible innovation.
Hook’s analysis covers AI research trends from 2000 to 2024, showing global AI publication volumes rising from just under 10,000 papers in 2000 to 60,000 in 2024.
However, China’s influence has steadily expanded since 2018, while the EU and the US have seen relative declines. The UK has largely maintained its position.
Clarivate, another analytics firm, reported similar findings, noting nearly 900,000 AI research papers produced in China in 2024, triple the figure from 2015.
Hook notes that governments increasingly view AI alongside energy or military power as a matter of national security. Instead of treating AI as a neutral technology, there is growing awareness that a lack of AI capability could have serious economic, political and social consequences.
The report suggests that understanding AI’s geopolitical implications has become essential for national policy.
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AI Research
Effects of generative artificial intelligence on cognitive effort and task performance: study protocol for a randomized controlled experiment among college students | Trials
Intervention description {11a}
In the intervention group, the computer screen will be set up in a split-screen format. On the left side of the screen, the participant will receive instructions on the writing prompt, writing requirements, time requirements, grading feedback, and the grading rubric. The instructions will also highlight to the participant that they can use ChatGPT in any way they like to assist their writing, and there is no penalty in their writing score for how ChatGPT is used. The right side of the screen will display a blank ChatGPT interface where the participant can prompt questions and receive answers.
Explanation for the choice of comparators {6b}
In the control group, as in the intervention group, the computer screen will be set up in a split-screen format. On the left side of the screen, the participant will receive the same instructions on the writing prompt, writing requirements, time requirements, grading feedback, and the grading rubric. Additionally, the instructions will highlight to the participant that they can use a text editor in any way they like to assist their writing. On the right side, instead of ChatGPT, a basic text editor interface will be displayed. In summary, this comparator will keep the split-screen format consistent between the two groups and ensure that participants in the control group will complete the writing task with minimal support.
Criteria for discontinuing or modifying allocated interventions {11b}
This study is of minimal risk, and we do not anticipate needing to discontinue or modify the allocated interventions during the experiment. Participants can withdraw from the study at any time.
Strategies to improve adherence to interventions {11c}
Adherence to the interventions will be high because the procedures are straightforward and will be clearly explained in the step-by-step instructions on the computer screen. The participant will be alone in a noise-canceling room during the entire experiment. The participant can reach out to the experimenter through an intercom if they need any clarification.
Relevant concomitant care permitted or prohibited during the trial {11d}
Not applicable. This is not a clinical study.
Provisions for post-trial care {30}
Not applicable. This is a minimal-risk study.
Outcomes {12}
The study has two primary outcomes. First, we will measure participants’ writing performance scores on the analytical writing task. The task is adapted from the Analytical Writing section in the GRE, a worldwide standardized computer-based exam developed by the Educational Testing Service (ETS) [27]. Participants’ writing performance will be scored using the GRE 0–6 rubric and by an automatic essay-scoring platform called ScoreItNow!, which is powered by ETS’s e-rater engine [32, 33]. We chose to adapt from the GRE writing materials for two reasons. First, their writing task and grading rubrics were established writing materials designed to measure critical thinking and analytical writing skills and have been used in research as practice materials for writing (e.g. [34]). Second, OpenAI’s technical report shows that ChatGPT (GPT-4) can score 4 out of 6 (~ 54th percentile) on the GRE analytical writing task [31]. This gives us a benchmark for assessing the potential increase in writing performance when individuals collaborate with generative AI.
Second, we will measure participants’ cognitive effort during the writing process. Participants’ cognitive effort will be measured using a psychophysiological proxy—i.e., changes in pupil size [35, 36]. Pupil diameter and gaze data will be collected using the Tobii Pro Fusion eye tracker at a sampling rate of 120 Hz. During the preparation stage of the study, the room light will be adjusted so that the illuminance at the participants’ eyes is at a constant value of 320 LUX. Baseline pupil diameters will be recorded during a resting task in the experiment preparation stage that asks the participant to stare at a cross that will appear for 10 s each on the left, center, and right sections of the computer screen. Pupil diameters and gaze data will be recorded throughout the writing process.
The study has several secondary outcomes. First, to identify the neural substrates of cognitive effort during the writing process, we developed an additional psychophysiological proxy, changes in the cortical hemodynamic activity in the frontal lobe of the brain. Specifically, we will examine hemodynamic changes in oxyhemoglobin (HbO). Brain activity will be recorded throughout the writing process using the NIRSport 2 fNIRS device and the Aurora software with a predefined montage (Fig. 2). The montage consists of eight sources, eight detectors, and eight short-distance detectors. The 18 long-distance channels (source-detector distance of 30 mm) and eight short-distance channels (source-detector distance of 8 mm) are located over the prefrontal cortex (PFC) and supplementary motor area (SMA) (Fig. 2). The PFC is often involved in executive function (e.g., cognitive control, cognitive efforts, inhibition) [37, 38]. The SMA is associated with cognitive effort [39, 40]. The sampling rate of the fNIRS is 10.2 Hz. Available fNIRS cap sizes are 54 cm, 56 cm, and 58 cm. The cap size selected will always be rounded down to the nearest available size based on the participant’s head measurement. The cap is placed on the center of the participant’s head based on the Cz point from the 10–20 system.
Third, we will measure participants’ subjective perceptions of the writing task by self-reported survey measures in the post-survey (Table 1). We will measure participants’ subjective perceptions of the two primary outcomes—that is, their self-perceived writing performance and self-perceived cognitive effort. Self-perceived writing performance will be measured with a one-item scale using the same grading rubric described in the instructions for their writing task and used in the scoring tool. Self-perceived cognitive effort will be measured using a one-item scale adapted from the National Aeronautics and Space Administration-task load index (NASA-TLX) [41, 42]. We will also measure participants’ subjective perceptions of several mental health and learning-related outcomes, including stress, challenge, and self-efficacy in writing. Self-perceived stress will be measured using a one-item scale adapted from the Primary Appraisal Secondary Appraisal scale (PASA) [43, 44]. Self-perceived challenge will be measured using a one-item sub-scale adapted from the Primary Appraisal Secondary Appraisal scale (PASA) [43, 44]. Self-efficacy in writing will be measured using a 16-item scale that measures three dimensions of writing self-efficacy: ideation, convention, and self-regulation [45]. Furthermore, we will measure participants’ situational interest in analytical writing using a four-item Likert scale adapted from the situational interest scale [46]. Additionally, we will measure participants’ behavioral intention to use ChatGPT in the future for essay writing tasks [47].
Participant timeline {13}
The time schedule is provided via the schematic diagram below (Fig. 3). The entire experiment will last for approximately 1–1.5 h for each participant.
Sample size {14}
To estimate the required sample size, we conducted a simulation analysis on the intervention effect on writing performance using ordinary least squares (OLS) regression. Recent empirical evidence suggests that the effect size of generative AI on writing tasks ranges around Cohen’s d = 0.4–0.5, such as [1, 48]. In our simulation analysis, the simulated data assumes normally distributed data, equal and standardized standard deviations between the two conditions, and an anticipated effect size of Cohen’s d = 0.45. In the end, our analysis indicated that recruiting a minimum of 160 participants would be necessary to achieve a statistical power greater than 0.8 under an alpha level of 0.05. The simulation was implemented in R, and the corresponding code is available at the Open Science Framework (OSF) via https://osf.io/9jgme/.
We opt to base our sample size estimation on writing performance, but not on the other primary outcome, cognitive effort, for two reasons. First, the effect of generative AI on performance outcomes has been studied [1, 48], but we did not find prior evidence on the effect size of generative AI on cognitive effort using physiological measures. Second, our physiological measure of cognitive effort may likely be powered once the sample size satisfies our behavioral measure of writing performance. Pupillometry studies on cognitive efforts, such as the N-back test, typically recruit 20–50 participants in short, repeated, within-subject trials (e.g., [49]). These studies provide a general estimation of participants needed. Although our study design (i.e., a between-subject RCT) differs from common pupillometry studies, cognitive effort is still a repeated outcome measure using time series pupil data throughout the entire writing process. Repeated outcome measures generally can enhance statistical power by taking into account within-subject variability [50].
Recruitment {15}
The recruitment will follow a convenience sampling strategy. To aim for a student population with diverse academic backgrounds, participants will be recruited broadly through social media platforms, email lists, and flyers at the research university where the experiment will be conducted. Given that the experiment will start during the summer, the research team can recruit summer school students as participants. Thus, the study sample will not be limited to the students presently at the university. The recruitment materials include a brief description of the study, the eligibility criteria for participation, and the compensation for participation. Individuals who are interested in participation can sign up on a calendar by selecting available time slots provided by the experimenters. Participants will receive 30 euros in compensation upon completion of the experiment. Participants who withdraw in the middle of the experiment will receive partial compensation, prorated based on the amount of time they spend in the experiment.
AI Research
Canadian Scientists Pioneer Made-in-Canada Quantum-powered AI Solution
Insider Brief
- A Canadian-led research team from TRIUMF and the Perimeter Institute has developed a quantum-assisted AI model to simulate particle collisions more efficiently, addressing global computational challenges.
- The study demonstrates that combining deep learning with quantum computing—using technology from D-Wave—can significantly reduce the time and cost of high-energy physics simulations.
- Published in npj Quantum Information, the work supports future upgrades to CERN’s Large Hadron Collider and underscores Canada’s growing leadership in quantum and AI-driven scientific research.
PRESS RELEASE — In a landmark achievement for Canadian science, a team of scientists led by TRIUMF and the Perimeter Institute for Theoretical Physics have unveiled transformative research that – for the first time – merges quantum computing techniques with advanced AI to model complex simulations in a fast, accurate and energy-efficient way.
“This is a uniquely Canadian success story,” said Wojciech Fedorko, Deputy Department Head, Scientific Computing at TRIUMF. “Uniting the expertise from our country’s research institutions and industry leaders has not only advanced our ability to carry out fundamental research, but also demonstrated Canada’s ability to lead the world in quantum and AI innovation.”
Traditional simulations of particle collisions are already both time-consuming and costly, often running on massive supercomputers for weeks or months. By leveraging quantum processes and technology made possible by California-based D-Wave Quantum Inc., the researchers were able to create a new “quantum-assisted” generative model capable of running simulations and open new opportunities to cost-effectively analyze rapidly growing data sets.
The research, published today in npj Quantum Information, is part of a worldwide effort to create the tools needed to accommodate upgrades to CERN’s particle accelerator, the Large Hadron Collider (LHC), and alleviate a computational bottleneck that would impact researchers all over the world.
“Our method shows that quantum and AI technologies developed here in Canada can solve real-world scientific bottlenecks,” said Javier Toledo-Marín, joint appointee at TRIUMF and Perimeter Institute. “By combining deep learning with quantum technology, we are forging a new path for both theoretical experimentation and technological application.”
In addition to TRIUMF and Perimeter, contributions to the published research came from the National Research Council of Canada (NRC), the University of British Columbia and the University of Virginia, showcasing not only the wealth of research talent and scientific ingenuity across the country, but also the international collaboration that places Canada at the forefront of worldwide scientific innovation.
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