December 2025
How do people experience new technologies and generative AI?
Key messages
Copy link to Key messagesThis brief analyses data collected in early 2025 for a few countries (Australia, Brazil, Canada, France, Germany, India, Italy, Japan, Korea, Mexico, the Netherlands, South Africa, the United Kingdom, and the United States) through a poll included in the OECD Digital Well-being Hub, developed with Cisco. While nationally representative, the results should be interpreted with caution due to the poll’s nature – as explained further in this brief.
Digital engagement varies by age, gender, and countries, with younger adults leading across key dimensions. Social networking is highest among 18-25-year-olds, with women participating more than men, particularly in Latin America (Mexico and Brazil). The group of 26-45-year-olds is the one engaging the most digitally through remote work. Use of internet-connected devices is strongest among 26-35-year-olds, especially in India, Brazil, and Mexico, and lowest in Japan, France, and Italy. Generative AI adoption is highest among 18-35-year-olds, with India, Brazil, and South Africa leading, while Germany, France, and Italy showing lower uptake.
High levels of screen time raise substantial concerns. About 38% of respondents report over five hours of daily recreational screen use, the most (41%) among those aged 18-25. Women report slightly more screen time than men. Mexico (50%), Brazil (48%), and South Africa (45%) show the highest shares. Experts recommend limiting recreational screen use to under 3 hours per day for adults and minimal exposure for children.
Perceptions of technology’s impact differ: 39% of all respondents say digital tools have strengthened their relationships. Adults aged 18-45 are most positive about it, while most of those aged 56+ are not. Respondents from emerging economies like India and Brazil show stronger positive perceptions. Women are four percentage points more likely than men to report a positive effect of technology on their relationships.
Generative AI evokes optimism among the young and in emerging economies, while scepticism is stronger among older adults. Over 75% of respondents under 35 view it as useful. Trust and ethical confidence in AI declines with age. Among men aged 26-35, over half believe AI will significantly impact their careers, with 19% assigning it the maximum impact score.
AI-related training is growing among younger, highly educated individuals, reflecting awareness of career shifts. In the last 12 months, 46% of those aged 26-35, 39% aged 18-25, and 38% aged 36-45 have undertaken AI-related training, compared to 27% for those aged 46-55 and under 20% for those 55+. Educational attainment matters as 36% of those with tertiary education pursued AI training versus 18% among those with only upper secondary education.
Introduction
Copy link to IntroductionDigital technologies are reshaping human experience across nearly every dimension of life, from how we work, learn and interact, to how we access health services, engage civically, and define personal fulfilment. This transformation has brought with it both remarkable opportunities and pressing challenges, particularly in terms of people’s well-being. While digital innovations have helped streamline services, improve productivity, and expand access to information, their effects on health, social connectedness, and subjective life satisfaction remain nuanced and, at times, contradictory.
The OECD’s 2024 working paper “The impact of digital technologies on well-being” (2024[1]) underscores this complexity. It highlights that digital tools and platforms can enhance mental and physical health (e.g. through AI-powered assistive care and telemedicine) but can also contribute to rising rates of anxiety, loneliness, and behavioural issues, particularly among youth and women. These divergent outcomes raise critical questions for policymakers, who must understand not only how much people use technology, but also the quality and context of their engagement from a wellbeing perspective.
Recognising this complexity, the OECD Centre on Well-being, Inclusion, Sustainability and Equal Opportunity (WISE) has advanced a framework to assess the effects of digitalisation on well-being, grounded in both subjective and objective indicators. The Centre’s approach distinguishes between passive digital exposure (e.g. screen time) and active engagement (e.g. social media communication), and links them to core dimensions of the OECD Well-being Framework, such as health, social connections, civic engagement, personal safety, and work-life balance, complementary with other OECD work such as Measuring the Digital Transformation: A Roadmap for the Future (2019[2]). It also highlights the importance of understanding purpose and reasons for using new technologies, as well as of adopting a life-cycle perspective to analysis how risks and vulnerabilities change.
Drawing on this conceptual foundation, a new poll was designed to examine the impacts of emerging technologies on specific well-being measures, where the evidence is either more complex or less established (Box 1). It captures cross-national perceptions and experiences related to digital technology use, allowing for an analysis of key demographic and geographic disparities. For example, findings from the poll show that younger adults in emerging economies of Brazil and India are more prone to use generative AI and participate in remote work than their peers in advanced economies, while also experiencing higher emotional volatility tied to screen overuse.
Box 1. The poll on digital well-being by the OECD in collaboration with Cisco
Copy link to Box 1. The poll on digital well-being by the OECD in collaboration with CiscoIn collaboration with Cisco, the OECD WISE Centre conducted a poll to collect insights on individuals’ experiences with digital technology, as featured in the Digital Well-being Hub. The poll was carried out by a specialised company in collaboration with Cisco, however, the poll is also available as a crowd-sourcing tool which has allowed the collection of data in real time. This brief analyses only the data collected by this specialised company and follows OECD best practices regarding sample size, stratification, and quality control, which are essential for ensuring data reliability, comparability, and national representativeness. Methodological guidance to develop the poll was drawn from OECD approaches including the OECD Guideline on Measuring Subjective Well-being, Risk That Matters survey and the WHO Five Well-Being Index. A sample size of approximately 1 000 respondents per country was used.
The sample was stratified by key demographic characteristics including country and gender. A stratified sampling method was employed in each country to ensure proportional representation across the variables, providing a statistically sound basis for subgroup analysis and cross-national comparisons.
The data collection yielded statistically valid responses from a total of 14 611 individuals across 14 countries. The country sample was selected to reflect a broad range of socio-economic and cultural contexts, including both OECD and non-OECD members, including Americas: Canada, United States; East Asia and Oceania: Australia, Japan, Korea; Africa: South Africa; Europe: France, Germany, Italy, the Netherlands, United Kingdom; Latin America: Brazil, Mexico; South Asia: India.
Each country is represented by a sample of just over 1 000 respondents, except for India, which includes 1 500 respondents. All participants completed the full 20-question poll as well as the accompanying demographic and profile questions in the OECD poll on digital well-being.
In this context, the OECD poll on digital well-being serves as a timely tool to fill the current data gap between fast-evolving digital environments and slower-moving empirical research. It expands on earlier OECD efforts, such as How’s Life in the Digital Age? (OECD, 2019[3]), by providing new insights into how individuals perceive the effects of digital engagement on their lives and well-being. This includes self-reported data on emotional responses to technology use, screen time thresholds, and the perceived impact on social connectedness. These dimensions are otherwise difficult to measure through statistical and administrative data alone.
By triangulating the poll’s findings with insights from the academic literature (i.e. see Lee and Žarnic (2024[1])), the poll allows for a more comprehensive analysis relevant for better understanding how the technology influences people's lives across different dimensions. It highlights not just the benefits of digital connectivity, but also the importance of mitigating the associated risks by fostering digital literacy, managing screen fatigue, and addressing mental health concerns in a digitised society. As digital adoption deepens globally, ensuring that it supports rather than erodes well-being is a growing imperative for governments, researchers, and technology providers alike.
How much technology is used and what for?
Copy link to How much technology is used and what for?Engagement with digital technology varies widely across age groups, genders, and countries. Across all dimensions, age emerges as a primary driver of digital behaviour, with younger individuals consistently leading in adoption and use. As shown in Figure 1, four areas of digital technology adoption concerning social networking, remote working, the use of internet-connected devices, and the use of generative AI show consistent divides across demographics and countries:
Social networking (Panel A) engagement peaks among 18-25-year-olds, declining steadily in older cohorts. Women are more active than men, and use is particularly high in emerging economies such as Mexico and Brazil, compared to lower levels in East Asia (Japan and Korea).
Remote working (Panel B) is most common among 26-45-year-olds, followed by 36-45-year-olds, suggesting that digital technologies are most embedded in the lives of people in their prime working and parenting years.
Use of internet-connected devices (Panel C) is highest among those aged 26-35, with marginally higher usage among females. It is strong in India, Brazil, and Mexico, and relatively weaker in Japan, France, and Italy.
The use of generative AI (Panel D) is high among younger adults (18-35 years), with a steep decline with age. India, Brazil and South Africa are leading the way, while Germany, France, Italy and Japan show less active use.
Additionally, younger adults, especially women aged 18-25 and men aged 26-35, report that most or all of their social interactions take place online. This implies a strong generational divide in communication habits, with more than 75% of young adults using at least three different social media platforms for interpersonal connections at least every 2-3 days. In contrast, older adults rely much less on digital communication, digital communication declines with age, with usage dropping significantly after the age of 45 and up to 5% of those aged 66 and over not using internet-based contact at all.
Figure 1. Technology use
Copy link to Figure 1. Technology usePercentage of respondents
Note: Frequent Social Networking Service (SNS) users are defined as people who used SNS multiple times a day (at least 3 of Facebook, Instagram, Snapchat and/or TikTok) in the last 3 months. Teleworking share is based on the percentage of people having worked at home for their main job in the last 4 weeks. The definition of the use of internet-connected devices is the use of a smart watch, a fitness band, connected goggles or headsets, safety-trackers, internet-connected accessories, internet-connected clothes or shoes, or devices connected to the internet for monitoring blood pressure, sugar level, body weight (e.g. smart scales) or other internet-connected, devices for health and medical care. Active users of generative AI are defined as people who answered 7 or more on a scale of 10 when asked about their use of any generative AI tool in the last 3 months.
Source: OECD poll on digital well-being, https://www.oecd.org/en/blogs/2024/11/oecd-poll-on-digital-well-being.html.
In addition to communication patterns, younger adults, particularly men, are more environmentally conscious when purchasing goods, considering eco-design and energy efficiency. Environmentally conscious behaviour declines with age, especially among older women. Overall, while younger generations embrace digital tools for social connection and lifestyle decisions, older adults show lower engagement, higher non-response rates, and a greater tendency to perceive technology as having no or even negative effects on their communication.
Consequently, the widespread adoption of digital technologies has markedly increased screen time in daily life, which raises concerns about sedentary behaviour and its implications for both physical and mental health. The World Health Organization (2020[4]) highlights the need to limit sedentary time and promote regular aerobic activity, particularly recommending restrictions on recreational screen use among children and adolescents. Similarly, the Canadian Society for Exercise Physiology (2020[5]) advises adults aged 18 to 64 to integrate daily physical activity, maintain adequate sleep, and limit sedentary behaviour to no more than 8 hours per day, including no more than 3 hours of recreational screen time.
Prolonged recreational screen use, including time spent on computers, tablets, mobile phones, televisions, and gaming consoles, is widespread across countries (Figure 2). Overall, 38% of respondents report more than five hours of daily recreational screen time, with prevalence highest among individuals aged 18 to 35 (41%).
While gender differences are modest, women report slightly higher screen time than men, potentially reflecting distinct patterns of technology use such as greater engagement with social networking platforms. Country-level variation is substantial, with Mexico (50%), Brazil (48%), and South Africa (45%) showing the highest shares of prolonged screen use by their citizens.
Figure 2. Screen time
Copy link to Figure 2. Screen timePercentage of respondents
Note: Young is defined as 18-35 years old, middle is defined as 36-55 years old, and old is defined as 56 years old and over.
Source: OECD poll on digital well-being, https://www.oecd.org/en/blogs/2024/11/oecd-poll-on-digital-well-being.html.
These findings suggest that excessive screen time is a concern, with potential consequences for cognitive strain, mental fatigue and displacement of offline activities, highlighting the need for coordinated public health strategies aimed at balancing digital engagement with social well-being. When combining patterns from Figure 1 and Figure 2, we observe that younger individuals and those in emerging economies are at the forefront of digital technology use. Unsurprisingly, these populations report higher levels of screen time as greater engagement correlates with increased time spent on digital devices. This finding highlights an important concern: the same groups driving digital transformation may also be vulnerable to its potential downsides. They could be less aware of digital risks or underestimate them, making them more susceptible to the negative effects of high screen exposure.
How do people perceive the impacts of digital technology on their well-being?
Copy link to How do people perceive the impacts of digital technology on their well-being?Individual perceptions of digital technology’s impacts are just as varied as its patterns of use. Change in communication has had different consequences across groups, with mixed feelings about technology use and prolonged screen time. As digital innovation reshapes communication, experiences diverge along demographic and regional lines.
Overall, 39% of respondents say that technology has strengthened relationships with those closest to them. However, younger generations widely perceive digital tools as enhancing their social ties, but also report more negative experiences compared to older groups (55+ age) who remain more neutral or ambivalent (Figure 3). Respondents from emerging economies, notably India and Brazil, express particularly strong positive perceptions, whereas responses from Europe and East Asia are more moderate.
Figure 3. Perceived effect of technology on social connections
Copy link to Figure 3. Perceived effect of technology on social connectionsPercentage of respondents
Note: Based on the perception over the last 3 months.
Source: OECD poll on digital well-being, https://www.oecd.org/en/blogs/2024/11/oecd-poll-on-digital-well-being.html.
Prolonged screen time for recreational purposes associated with digital technology evokes mixed feelings among people. About 29% report feeling “happy” with their digital engagement, while a similar share reports feelings of tiredness. Around 20% experience no emotional shift at all (Figure 4).
Figure 4. Perceived effects of prolonged screen time
Copy link to Figure 4. Perceived effects of prolonged screen timePercentage of respondents
Note: Based on the perception over the last 3 months. The definition of prolonged screen time in this chart is recreational screen time of more than four hours per day.
Source: OECD poll on digital well-being, https://www.oecd.org/en/blogs/2024/11/oecd-poll-on-digital-well-being.html.
In addition to the general pattern, there is a variation between people related to the effects of prolonged screen time, depending on demographics and countries (Figure 5). Younger adults (ages 18-35) exhibit most intense subjective responses: while many report feelings of happiness, they also show higher levels of fatigue and distraction linked to extended screen use.
Women appear more prone to experiencing tiredness and reduced focus. By contrast, older adults (66+) tend to report minimal emotional changes, indicating that extended digital engagement has a more neutral effect on their mood. Regionally, Korea leads in reported screen-induced fatigue, while respondents from India and Japan are more likely to associate screen use with positive emotional outcomes. These findings underscore the complex nature of digital engagement, which can simultaneously foster stimulation and exhaustion, that is, the effects that vary by age, gender, and countries.
Figure 5. Perceived effects of prolonged screen time, by demographics and countries
Copy link to Figure 5. Perceived effects of prolonged screen time, by demographics and countriesPercentage of respondents
Note: Based on the perception over the last 3 months. Young denotes 18-35-year-olds, middle 36-55-year-olds, and old refers to 56 years old and beyond. The definition of prolonged screen time in this chart is recreational screen time of more than four hours per day.
Source: OECD poll on digital well-being, https://www.oecd.org/en/blogs/2024/11/oecd-poll-on-digital-well-being.html.
How do people perceive the emergence of generative AI tools?
Copy link to How do people perceive the emergence of generative AI tools?There are marked differences in perceptions of an emerging issue, the use of generative AI. Perceptions of generative AI diverge sharply across demographic and regional contexts, with younger individuals and those in emerging economies expressing markedly higher levels of optimism.
Figure 6 illustrates public views on AI’s usefulness, trustworthiness, ethical standing, and anticipated effects on employment, in particular:
Over 75% of respondents under 35 rate AI as useful (Panel A), while uncertainty is most pronounced among older adults, particularly those aged 65 and above. In emerging economies of India, Brazil, and South Africa, trust in generative AI is stronger while scepticism higher in Japan and across Europe.
Panels B and C reveal similar patterns in confidence and ethical perceptions concerning generative AI. While many consider AI to be trustworthy and ethical, strong confidence remains rare and declines with age. Generational optimism is especially prominent among individuals aged 18-35, more than 80% of whom perceive AI as at least somewhat useful, and who consistently assign higher ratings across all dimensions. Respondents in emerging economies are also generally more trusting of generative AI, while those in advanced economies express greater hesitation or uncertainty. This global variability highlights an enduring gap in understanding and confidence around AI’s role in society.
Expectations surrounding AI’s impact on employment further reflect this generational divide. Panel D shows that those aged 18-45 anticipate significant effects on their careers, with the majority choosing a score of 7 or above. Conversely, older respondents (i.e. particularly those over 45) are more inclined to believe that AI will have less or little effect. These divergent expectations likely reflect both differing levels of exposure to AI tools and broader generational contrasts in digital adaptability and career outlook in view of generative AI development in the future.
Overall, younger people (18-35) are the most optimistic about AI’s usefulness, trustworthiness, and ethical behaviour, with over 80% rating AI positively. In contrast, older users express more hesitation, often selecting “Don’t know” or lower trust scores. This highlights an emerging digital divide, with younger generations more inclined to embrace AI’s evolving role in society and work. This trend is pronounced among younger professionals who foresee substantial impacts of AI on their careers. Over the past 12 months, 45% of individuals aged 26-35, 39% of those aged 18-25, and 38% of those aged 36-45 reported undertaking such training. Participation declines with age, with 27% of those aged 46-55 and fewer than 20% of those over 55 reporting similar efforts.
The data also informs on educational attainment, showing that it influences AI-related training uptake: 36% of individuals with tertiary education engaged in AI-related learning, compared to 18% among those whose highest education level is upper secondary. A similar generational and educational attainment split also exists for intentions to undertake AI training in the coming year.
Figure 6. Perceptions of AI and its anticipated influence
Copy link to Figure 6. Perceptions of AI and its anticipated influencePercentage of respondents
Note: Panels A-C (usefulness, trustworthiness and ethics) are based on the use of AI in the last 3 months. Panel D is based on the anticipated influence of generative AI tools over the next 5 years.
Source: OECD poll on digital well-being, https://www.oecd.org/en/blogs/2024/11/oecd-poll-on-digital-well-being.html.
Key insights for consideration
Copy link to Key insights for considerationHigh recreational screen time (i.e. over five hours daily for 38% of respondents) raises concerns about physical inactivity and mental fatigue. More detailed studies are needed to assess how digital engagement affects cognitive development and health outcomes in the context of broader subjective well-being specific to different age groups, particularly young adults and adolescents.
Younger individuals (i.e. 18-35-year-olds) and those in emerging economies show high optimism toward generative AI, but older adults and those in Europe and Japan are more sceptical. Further analysis is needed to explore how educational and occupational factors as well as country-specific contexts influence current use, acceptance and future anticipation of generative AI, and how these differences might shape workforce transitions and societal integration.
The poll reveals patterns of a persistent digital divide across age, gender, and geography, with younger generation and emerging economies leading the way in adoption but possibly also at a greater risk of negative side effects (e.g. screen fatigue and reliance on digital socialising). More research is needed to explore how digital inequalities impact social connectedness, career opportunities, civic participation, and well-being overall across different subjective dimensions to inform how public policies can balance digital innovation with protecting vulnerable populations.
Further reading
Copy link to Further reading| truent:unstructuredoecd/components/text/v1/text
Lee, J. and Ž. Žarnic (2024), “The impact of digital technologies on well-being: Main insights from the literature”, OECD Papers on Well-being and Inequalities, No. 29, OECD Publishing, Paris, https://doi.org/10.1787/cb173652-en. |
[1] |
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OECD (2019), How’s Life in the Digital Age?: Opportunities and Risks of the Digital Transformation for People’s Well-being, OECD Publishing, Paris, https://doi.org/10.1787/9789264311800-en. |
[3] |
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OECD (2019), Measuring the Digital Transformation: A Roadmap for the Future, OECD Publishing, https://doi.org/10.1787/9789264311992-en. |
[2] |
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Organization, W. (2020), WHO guidelines on physical activity and sedentary behaviour, https://www.who.int/publications/i/item/9789240015128. |
[4] |
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Physiology, C. (2020), Canadian 24-Hour Movement Guidelines, https://csepguidelines.ca/. |
[5] |
Resources
Copy link to ResourcesThe OECD Digital Well-being Hub with Cisco: https://www.oecd.org/en/data/tools/digital-well-being-hub.html
How’s your digital well-being? https://oecdstatistics.blog/2025/02/26/hows-your-digital-well-being/
Contacts
For more information contact us: wellbeing@oecd.org
OECD Centre on Well-being, Inclusion, Sustainability and Equal Opportunity (WISE)
www.oecd.org/wise