Generation Z (Gen Z) is widely considered the first cohort to come of age entirely in the epoch of generative artificial intelligence (AI). There is no universally agreed-upon definition of the age range for Gen Z. Australia's McCrindle Research uses those born between 1995 and 2009 to address Gen Z; McKinsey & Company defines Gen Z as those born between 1996 and 2010; Pew Research Centre refers to Gen Z as those born between 1997 and 2012. While Gen Z are the most active adopters of generative AI, existing scholarship argues that adoption has outpaced institutional preparation and service delivery. An influx of press and social media articles and videos addressing how the labour market entry points Gen Z expected upon university graduation are vanishing, and their mental health, which was already deteriorated in the midst of the social media age, has compounded alongside an explosion in algorithmically mediated social life.
This analysis synthesises data-driven and peer-reviewed sources—large-scale surveys, payroll data, and experimental studies—to examine how Gen Z are experiencing the AI transition across three domains: (1) education, (2) social life and well-being, and (3) the labour market. The analysis is designed to allow for enriching the understanding among professional AI and social science researchers, as well as the general public.
In today’s landscape, we witness an unbalanced relationship between AI adoption and literacy. In the US, 79% of Gen Z have used AI tools, but only 28% of K-12 students attend schools with explicit AI policies. Students whose schools allow AI are 25 percentage points more likely to feel prepared to use it after graduation (57% vs. 32%). Such a gap is largest for students in low-income and rural communities.
Also, Gen Z well-being is deteriorating. Only 45% of Gen Z are thriving in 2025—reaching a three-year low, down from 49% in 2024. Among Gen Z women, the thriving rate fell from 46% to 37% between 2024 and 2025. Additionally, UK Gen Z report that social media negatively affects their self-esteem and discourages them from building healthy self-relationships.
In addition, we address the unemployment-induced anxiety, where the number of available entry-level job postings requiring 0-2 years of experience has been cut by 29 percentage points since January 2024. Researchers from the Stanford Digital Economy Lab, using Automatic Data Processing (ADP) payroll records covering millions of US workers, find a 16% relative employment decline for ages 22–25 in AI-exposed occupations, when comparing with their older counterparts.
Therefore, we witness cross-domain impacts on Gen Z induced by AI advancement. This analysis dissects the nuances in each domain with supporting data, so as to present how the behaviours and encounters of such a highly AI-adopted generation have changed, relative to their older counterparts.
1. AI in Education
1.1 AI Adoption Rates Across Generations
Like during the age of social media and digitalisation, today’s Gen Z has actively and willingly incorporated AI into both learning and non-educational activities. According to a Gallup survey (2025) commissioned by the Walton Family Foundation (WFF) (n = 3,465), we see that 79% of Gen Z in the US have used AI tools, and 47% do so at least on a weekly basis. In another report published by Pew Research Centre (2026) (n = 1,458; US teens aged 13 - 17), 57% of respondents have used chatbots to search for information, 54% to help with schoolwork and 47% for entertainment purposes. For these US teens, one in 10 now does all or most of their schoolwork with AI assistance. AI adoption has become widely popularised among Gen Z. For those adopting AI in educational settings, there are concerns about overreliance and subsequent barriers to independent and critical thinking development. For those who lack access to AI tools, such a circumstance deepens the ongoing sociological discourse on whether AI technology accelerates the entrenched digital inequality, partly inflicted by socio-economic factors.
SurveyMonkey disseminated the outputs of a study of 25,030 US adults in 2025. Their team carried out an AI sentiment study between October and December of 2024 among 25,030 US adults. The survey outputs were weighted to reflect the US demographic composition. They found that 61% of Gen Z use AI for education, relative to 53% of Gen X and 50% of millennials, who primarily use AI for workplace responsibilities. While the data support our argument that Gen Z is the quickest and most willing AI adopters, Gen X and millennials, especially those who are active in the labour market, are not far behind. Instead, half or more of US adults, across all three generations, have used AI for their educational or professional tasks.
Table 1 summarises the AI use rates by generation, with data extracted from major survey reports. While as many as 79% of Gen Z have ever used AI tools, only 47% reported that they use them on a weekly or daily basis. This tells that 32% of Gen Z have either tried using AI tools out of curiosity or decided not to highly adopt AI use in their tasks, so they are not frequent AI users. Another valuable insight is that 75% of Gen Z reported that they use AI to upskill, relative to 71%, 56% and 49% for millennials, Gen X and Boomers, respectively. Such figures inform that the majority of older generations (more than half of millennials and Gen X, and almost half of Boomers) are willing to use AI for upskilling. Such a trend indicates that the majority of the older generations are actively reskilling themselves to incorporate AI technology into their professional or everyday tasks, signalling that their skillsets should remain relevant in the labour market despite the rapid AI disruption.
Table 1: AI Use Rates by Generation — Key Figures from Major Surveys
While AI usage is high across generations, especially among Gen Z, it is necessary to understand whether there are available and sufficient institutional policies to support the young generation’s AI adoption in educational settings. The Gallup survey (2025) shows that only 28% of K-12 students in the US claimed that their schools explicitly allow the use of AI, and nearly half (i.e., 49%) reported that their schools have no policies on AI use or they are not sure of any presence of relevant policies. Among students whose schools have available AI policies, only about one-third (i.e. 36%) describe those policies as extremely clearly outlined.
Further data suggest how the AI readiness level affects students’ post-schooling outcomes. The Gallup survey (2025) indicates that students in schools with AI policies are 25 percentage points more likely to feel prepared to use AI after graduation (57% vs. 32%). Another recent study (2025) commissioned by Heartland Forward, Gallup and the WFF surveyed 1,474 Gen Z in 20 US heartland states. The study found that only 10% of K-12 students said their teachers have helped prepare them to use AI in future jobs, and as low as 9% of working Gen Z feel extremely prepared to use AI at work. Furthermore, schools in low-income areas are significantly less likely to have AI policies permitting its use. Such a report reveals the equity gap in AI-impacted K-12 education in the US.
Table 2: The Preparation Gap—AI Policy Presence and Student Readiness
1.3 Gen Z Students vs. Gen X/Y Teachers: Attitudes on the Use of AI
AI disruption is not restricted to the US or the West at large. Gen Z students in many high-income economies are affected by AI advancement in educational settings. Chan & Lee (2023) conducted a survey with 399 Gen Z students and 184 Gen X or Gen Y (meaning millennial) teachers at the University of Hong Kong on their experiences, perceptions and concerns about generative AI in higher education. Diagram 1 highlights the key findings in mean agreement scores that follow a five-point Likert scale on each statement. The value of one represents strongly disagree, and the value of five means strongly agree. All findings reported in Diagram 1 have p-values lower than 0.05. The data reveal an insightful generational divide. We can see that Gen Z respondents have higher mean scores than their Gen X/Y teachers on all positive statements about the use of AI, while the former have lower mean scores, relative to the latter, on all negative statements about using AI. The results show that Gen Z students are more optimistic, and enjoy more perceived benefits, from the use of AI than their teachers. Of course, the societal roles for both survey cohorts are different: one group represents the students in higher education, and the other belongs to their teachers. Perhaps Gen X/Y respondents share more concerns about the use of AI not because of generational factors but because of their societal role (as teachers/educators) in the higher education settings.
Diagram 1: Gen Z Students vs. Gen X/Y Teachers—Mean Agreement Scores on Key AI Attitudes (Chan & Lee, 2023)
Source: Chan & Lee (2023)—“The AI Generation Gap” (Smart Learning Environments, Springer / arXiv:2305.02878). n=399 Gen Z students, n=184 Gen X/Y teachers, University of Hong Kong. Mean scores on 5-point Likert scale (1=Strongly Disagree, 5=Strongly Agree). All items p <.05.
1.4 Academic Integrity
Another widely discussed issue in the intersection between AI and social science is whether and how AI disrupts academic integrity. Academic and research integrity have been the core foundation of ethics in educational settings. Given the extensive reporting on AI-induced hallucinations, for example, academic and research integrity might be significantly challenged without appropriate policy interventions. Table 3 summarises major recent studies on AI influence on academic integrity, including the key findings and the corresponding policy implications. In a report released by the publishing firm Wiley (Coffey, 2024), over 2,000 students and instructors were surveyed. Findings suggest that 47% of students say AI has made cheating easier, and 68% of instructors anticipate a negative impact from the use of AI on academic integrity. Comparatively, Lee et al. (2024) conducted a survey around November 2022 and found that high school cheating rates before and after the introduction of ChatGPT remained broadly stable. It is noteworthy that Lee et al.’s (2024) study focused on data collected around the time when ChatGPT was first publicly introduced. It is reasonable that high school students did not experience higher academic cheating rates upon the arrival of generative AI, because such an AI chatbot was not capable and functional enough to encourage academically dishonest behaviours. However, as indicated in the aforementioned report released by Wiley (Coffey, 2024), as AI tools have become far more capable over the years, cheating behaviours have been encouraged, and academic integrity has been at stake to a certain degree.
It is noteworthy that Bittle & El-Gayar (2025) carried out a systematic review of 41 studies and found that peer influence and personal ethics shape AI misuse more than institutional policies. Such an understanding reinforces the argument that heavy-handed AI bans may be less effective than co-shaping the development of AI literacy with honesty and values concentration between educators, scholars and policymakers.
Other than academic behaviours and integrity, Gen Z’s social media dynamics and personal well-being are worth discussing. First, this section, in addition to AI, includes the investigation of social media dynamics because nowadays social media is heavily AI-integrated, and both social media and AI are known as the most revolutionary digital technologies affecting the Gen Z population. Second, extensive studies address that mental health and well-being are a priority for Gen Z. Therefore, this section discusses the intersection between AI, social media and well-being among this generation.
Gen Z well-being, according to publicly available data, is declining. The aforementioned Gallup survey (2025) reveals that only 45% of Gen Z are thriving per Gallup’s Life Evaluation Index—which was the lowest between 2023 and 2025 of the study. Table 4 highlights the thriving rates of Gen Z. The largest decline can be seen among Gen Z women, whose thriving rates dropped from 46% in 2024 to 37% in 2025. Comparatively, Gen Z men’s thriving rates remain relatively stable (44% in 2024 vs. 45% in 2025). The survey further indicates that Gen Z adults are about 10 percentage points less likely to be thriving than older generations.
The Cybersmile Foundation carried out a national UK study examining the impact of social media use on Gen Z well-being. They surveyed 1,000 UK participants aged between 16 and 24. Their data suggest that social media has negative impacts on Gen Z. Diagram 2 extracts and highlights some key statistics indicating social media impact on Gen Z well-being. The findings, for example, show that 82% of respondents said social media negatively affected how they feel about their own bodies. 83% said content on social media made them feel pressured to be perfect. Also, 48% said their sleep had been negatively impacted by time spent online. There were, moreover, 38% who said social media made them want to permanently change a part of their body through surgery. Such a figure rose to 51% when we focus on female respondents. These survey outputs imply that social media causes Gen Z not to be able to develop healthy self-relationships, and their self-esteem and behavioural well-being are jeopardised.
Shehab et al. (2025) synthesise existing research and document that depression, anxiety and insomnia are among the most robustly evidenced harms for Gen Z inflicted by the addiction to Instagram and TikTok. In addition, the Gallup survey (2025) data shows that 41% of Gen Z have anxiety about AI specifically, compared to 36% who feel excited and 27% who feel hopeful. Additional data unveils that 49% of Gen Z believe AI will harm their critical thinking skills, which can lead to broader anxiety about dependency and cognitive overload in a way similar to how social media has affected this generation.
Diagram 2: Social Media Impact on Gen Z Well-being
As of writing this analysis, AI disruption in the labour market is notable and expanding. Mass unemployment, especially youth unemployment, has been widely reported internationally. In Randstad's latest report, entitled “The Gen Z Workplace Blueprint: Future Focused, Fast Moving”, findings from job posting analysis of 11,250 global workers and 126 million job postings show that global postings for roles requiring 0-2 years of experience (meaning entry-level jobs) have fallen by an average of 29 percentage points since January 2024. The collapse of the entry-level job market is more salient in several positions, such as junior technology roles (down by 35%), logistics (down by 25%) and finance (down by 24%) (See Table 5).
Brynjolfsson et al. (2025) from the Stanford Digital Economy Lab analysed ADP payroll records covering millions of US workers. They found that early-career workers (aged 22 to 25) in AI-exposed occupations experienced 16% relative employment declines, controlling for firm-level shocks, while employment for experienced workers remained stable. The labour market adjustments occur primarily via employment rather than compensation, with employment changes concentrated in occupations where AI automates rather than augments labour (such as software development, customer service and clerical work).
Table 5: Entry-Level Job Posting Declines by Sector
3.2 Gen Z Career Strategy in Response to AI Disruption
From the same Randstad report, we can see that Gen Z is actively adapting to the AI-induced changes in the labour market by altering their career behaviours and decisions. Table 6 presents Gen Z’s career behaviours and decisions for AI adoption, relative to those from older generations. As discussed in Table 1, 75% of Gen Z use AI to learn new skills. In addition, from Table 6, 55%, 50% and 46% (up from 40% in 2024) use AI for professional problem-solving, use AI for job search and are worried about AI impact on their occupational opportunities, respectively. Their AI-induced career behavioural adjustments not only imply that they are active and willing AI adopters, but also reflect the circumstance that this generation is mostly impacted by AI disruption in the labour market due to the loss of entry-level jobs.
Table 6: AI Adoption in the Workplace—Gen Z Relative to Older Generations
Although media portrayals and general perceptions claim that Gen Z, unlike older generations, lack loyalty to an organisation due to laziness and low resilience, the Randstad report might suggest otherwise. Table 7 presents the job tenure and employment structure by generation. When comparing the first five years of career (instead of the current tenure), we can see that the average job tenure for Gen Z is 1.1 years, relative to 1.8 years for millennials, 2.8 years for Gen X and 2.9 years for Boomers. Here, we see the generational shift where early career job-taking patterns have constantly changed over the last few decades. There are almost half (45%) of Gen Z who are currently working full-time, and 52% are actively looking for a new role. While we cannot tell how many of those 52% who actively seek new roles already have a full-time position, at least the figures suggest that the majority of Gen Z choose to stay active in the labour market (either by finding jobs or already working full-time). Such figures dismiss the general perception that Gen Z are lazy to a certain degree. In fact, the change of the labour market structure, especially the AI disruption that causes the loss of entry-level jobs, may explain why staying active professionally nowadays seems far harder than that of previous generations. The data, moreover, shows that only 11% of Gen Z plan to stay in their current job long term. While this figure ostensibly shows that Gen Z has little loyalty to an organisation or workplace, in fact, the lack of available full-time entry-level positions might justify why many Gen Z members do not want to stay in their current job long term. This is because many might be working part-time or on a freelance and ad hoc basis. Therefore, staying in the current job does not offer any promising career path or financial security for Gen Z to display their loyalty at work.
Table 7: Job Tenure and Employment Structure by Generation
Source: Randstad (2025). Comparison figures reflect the same career stage (first 5 years), not current tenure.
4. Summary and Implications
We can see substantial behavioural changes between Gen Z and their older counterparts. This analysis finds that Gen Z is adapting to AI faster than institutional responses. Their well-being is constantly being jeopardised, caused by both the popularisation of AI and social media. Also, this generation is most vulnerable to AI-induced economic risks, since the labour market disruption mostly erases entry-level job opportunities.
In education, this analysis identifies a policy gap in institutional readiness, which lags AI adoption by a wide margin. Furthermore, the need for AI adoption in educational settings is compounding spatial and socio-economic inequalities. Regarding their well-being, this analysis shows that AI is disrupting rather than enriching Gen Z’s social and mental health. While there is research-based evidence identifying how (AI-integrated) social media are harming this generation, corresponding regulatory responses remain fragmented and not sufficiently effective. In the labour market, although Gen Z actively use AI for upskilling, they are the cohort whose job opportunities are technologically disrupted the most. There is a lack of policy responses on formal AI training access for the young generations, including Gen Z. Even if some jurisdictions may deliver policy responses on formalising AI training and upskilling opportunities for students, there remains an absence of any clear solution on how Gen Z, once sufficiently upskilled, can gain access to entry-level job opportunities.
Summary
Generation Z (Gen Z) is widely considered the first cohort to come of age entirely in the epoch of generative artificial intelligence (AI). There is no universally agreed-upon definition of the age range for Gen Z. Australia's McCrindle Research uses those born between 1995 and 2009 to address Gen Z; McKinsey & Company defines Gen Z as those born between 1996 and 2010; Pew Research Centre refers to Gen Z as those born between 1997 and 2012. While Gen Z are the most active adopters of generative AI, existing scholarship argues that adoption has outpaced institutional preparation and service delivery. An influx of press and social media articles and videos addressing how the labour market entry points Gen Z expected upon university graduation are vanishing, and their mental health, which was already deteriorated in the midst of the social media age, has compounded alongside an explosion in algorithmically mediated social life.
This analysis synthesises data-driven and peer-reviewed sources—large-scale surveys, payroll data, and experimental studies—to examine how Gen Z are experiencing the AI transition across three domains: (1) education, (2) social life and well-being, and (3) the labour market. The analysis is designed to allow for enriching the understanding among professional AI and social science researchers, as well as the general public.
In today’s landscape, we witness an unbalanced relationship between AI adoption and literacy. In the US, 79% of Gen Z have used AI tools, but only 28% of K-12 students attend schools with explicit AI policies. Students whose schools allow AI are 25 percentage points more likely to feel prepared to use it after graduation (57% vs. 32%). Such a gap is largest for students in low-income and rural communities.
Also, Gen Z well-being is deteriorating. Only 45% of Gen Z are thriving in 2025—reaching a three-year low, down from 49% in 2024. Among Gen Z women, the thriving rate fell from 46% to 37% between 2024 and 2025. Additionally, UK Gen Z report that social media negatively affects their self-esteem and discourages them from building healthy self-relationships.
In addition, we address the unemployment-induced anxiety, where the number of available entry-level job postings requiring 0-2 years of experience has been cut by 29 percentage points since January 2024. Researchers from the Stanford Digital Economy Lab, using Automatic Data Processing (ADP) payroll records covering millions of US workers, find a 16% relative employment decline for ages 22–25 in AI-exposed occupations, when comparing with their older counterparts.
Therefore, we witness cross-domain impacts on Gen Z induced by AI advancement. This analysis dissects the nuances in each domain with supporting data, so as to present how the behaviours and encounters of such a highly AI-adopted generation have changed, relative to their older counterparts.
1. AI in Education
1.1 AI Adoption Rates Across Generations
Like during the age of social media and digitalisation, today’s Gen Z has actively and willingly incorporated AI into both learning and non-educational activities. According to a Gallup survey (2025) commissioned by the Walton Family Foundation (WFF) (n = 3,465), we see that 79% of Gen Z in the US have used AI tools, and 47% do so at least on a weekly basis. In another report published by Pew Research Centre (2026) (n = 1,458; US teens aged 13 - 17), 57% of respondents have used chatbots to search for information, 54% to help with schoolwork and 47% for entertainment purposes. For these US teens, one in 10 now does all or most of their schoolwork with AI assistance. AI adoption has become widely popularised among Gen Z. For those adopting AI in educational settings, there are concerns about overreliance and subsequent barriers to independent and critical thinking development. For those who lack access to AI tools, such a circumstance deepens the ongoing sociological discourse on whether AI technology accelerates the entrenched digital inequality, partly inflicted by socio-economic factors.
SurveyMonkey disseminated the outputs of a study of 25,030 US adults in 2025. Their team carried out an AI sentiment study between October and December of 2024 among 25,030 US adults. The survey outputs were weighted to reflect the US demographic composition. They found that 61% of Gen Z use AI for education, relative to 53% of Gen X and 50% of millennials, who primarily use AI for workplace responsibilities. While the data support our argument that Gen Z is the quickest and most willing AI adopters, Gen X and millennials, especially those who are active in the labour market, are not far behind. Instead, half or more of US adults, across all three generations, have used AI for their educational or professional tasks.
Table 1 summarises the AI use rates by generation, with data extracted from major survey reports. While as many as 79% of Gen Z have ever used AI tools, only 47% reported that they use them on a weekly or daily basis. This tells that 32% of Gen Z have either tried using AI tools out of curiosity or decided not to highly adopt AI use in their tasks, so they are not frequent AI users. Another valuable insight is that 75% of Gen Z reported that they use AI to upskill, relative to 71%, 56% and 49% for millennials, Gen X and Boomers, respectively. Such figures inform that the majority of older generations (more than half of millennials and Gen X, and almost half of Boomers) are willing to use AI for upskilling. Such a trend indicates that the majority of the older generations are actively reskilling themselves to incorporate AI technology into their professional or everyday tasks, signalling that their skillsets should remain relevant in the labour market despite the rapid AI disruption.
Table 1: AI Use Rates by Generation — Key Figures from Major Surveys
Source: Gallup / WFF (2025) | SurveyMonkey (2025) | Randstad (2025) | Deloitte (2025)
1.2 The Lack of Institutional AI Readiness
While AI usage is high across generations, especially among Gen Z, it is necessary to understand whether there are available and sufficient institutional policies to support the young generation’s AI adoption in educational settings. The Gallup survey (2025) shows that only 28% of K-12 students in the US claimed that their schools explicitly allow the use of AI, and nearly half (i.e., 49%) reported that their schools have no policies on AI use or they are not sure of any presence of relevant policies. Among students whose schools have available AI policies, only about one-third (i.e. 36%) describe those policies as extremely clearly outlined.
Further data suggest how the AI readiness level affects students’ post-schooling outcomes. The Gallup survey (2025) indicates that students in schools with AI policies are 25 percentage points more likely to feel prepared to use AI after graduation (57% vs. 32%). Another recent study (2025) commissioned by Heartland Forward, Gallup and the WFF surveyed 1,474 Gen Z in 20 US heartland states. The study found that only 10% of K-12 students said their teachers have helped prepare them to use AI in future jobs, and as low as 9% of working Gen Z feel extremely prepared to use AI at work. Furthermore, schools in low-income areas are significantly less likely to have AI policies permitting its use. Such a report reveals the equity gap in AI-impacted K-12 education in the US.
Table 2: The Preparation Gap—AI Policy Presence and Student Readiness
Source: Gallup / WFF (2025) | Heartland Forward / Gallup / WFF (2025)
1.3 Gen Z Students vs. Gen X/Y Teachers: Attitudes on the Use of AI
AI disruption is not restricted to the US or the West at large. Gen Z students in many high-income economies are affected by AI advancement in educational settings. Chan & Lee (2023) conducted a survey with 399 Gen Z students and 184 Gen X or Gen Y (meaning millennial) teachers at the University of Hong Kong on their experiences, perceptions and concerns about generative AI in higher education. Diagram 1 highlights the key findings in mean agreement scores that follow a five-point Likert scale on each statement. The value of one represents strongly disagree, and the value of five means strongly agree. All findings reported in Diagram 1 have p-values lower than 0.05. The data reveal an insightful generational divide. We can see that Gen Z respondents have higher mean scores than their Gen X/Y teachers on all positive statements about the use of AI, while the former have lower mean scores, relative to the latter, on all negative statements about using AI. The results show that Gen Z students are more optimistic, and enjoy more perceived benefits, from the use of AI than their teachers. Of course, the societal roles for both survey cohorts are different: one group represents the students in higher education, and the other belongs to their teachers. Perhaps Gen X/Y respondents share more concerns about the use of AI not because of generational factors but because of their societal role (as teachers/educators) in the higher education settings.
Diagram 1: Gen Z Students vs. Gen X/Y Teachers—Mean Agreement Scores on Key AI Attitudes (Chan & Lee, 2023)
Source: Chan & Lee (2023)—“The AI Generation Gap” (Smart Learning Environments, Springer / arXiv:2305.02878). n=399 Gen Z students, n=184 Gen X/Y teachers, University of Hong Kong. Mean scores on 5-point Likert scale (1=Strongly Disagree, 5=Strongly Agree). All items p <.05.
1.4 Academic Integrity
Another widely discussed issue in the intersection between AI and social science is whether and how AI disrupts academic integrity. Academic and research integrity have been the core foundation of ethics in educational settings. Given the extensive reporting on AI-induced hallucinations, for example, academic and research integrity might be significantly challenged without appropriate policy interventions. Table 3 summarises major recent studies on AI influence on academic integrity, including the key findings and the corresponding policy implications. In a report released by the publishing firm Wiley (Coffey, 2024), over 2,000 students and instructors were surveyed. Findings suggest that 47% of students say AI has made cheating easier, and 68% of instructors anticipate a negative impact from the use of AI on academic integrity. Comparatively, Lee et al. (2024) conducted a survey around November 2022 and found that high school cheating rates before and after the introduction of ChatGPT remained broadly stable. It is noteworthy that Lee et al.’s (2024) study focused on data collected around the time when ChatGPT was first publicly introduced. It is reasonable that high school students did not experience higher academic cheating rates upon the arrival of generative AI, because such an AI chatbot was not capable and functional enough to encourage academically dishonest behaviours. However, as indicated in the aforementioned report released by Wiley (Coffey, 2024), as AI tools have become far more capable over the years, cheating behaviours have been encouraged, and academic integrity has been at stake to a certain degree.
It is noteworthy that Bittle & El-Gayar (2025) carried out a systematic review of 41 studies and found that peer influence and personal ethics shape AI misuse more than institutional policies. Such an understanding reinforces the argument that heavy-handed AI bans may be less effective than co-shaping the development of AI literacy with honesty and values concentration between educators, scholars and policymakers.
Table 3: Academic Integrity — Key Evidence Summary
Source: Coffey (2024) | Bittle & El-Gayar (2025) | Lee et al. (2024) | Mcclain et al. (2026)
2. AI, Social Media, and Well-being
2.1 The Well-being Crisis
Other than academic behaviours and integrity, Gen Z’s social media dynamics and personal well-being are worth discussing. First, this section, in addition to AI, includes the investigation of social media dynamics because nowadays social media is heavily AI-integrated, and both social media and AI are known as the most revolutionary digital technologies affecting the Gen Z population. Second, extensive studies address that mental health and well-being are a priority for Gen Z. Therefore, this section discusses the intersection between AI, social media and well-being among this generation.
Gen Z well-being, according to publicly available data, is declining. The aforementioned Gallup survey (2025) reveals that only 45% of Gen Z are thriving per Gallup’s Life Evaluation Index—which was the lowest between 2023 and 2025 of the study. Table 4 highlights the thriving rates of Gen Z. The largest decline can be seen among Gen Z women, whose thriving rates dropped from 46% in 2024 to 37% in 2025. Comparatively, Gen Z men’s thriving rates remain relatively stable (44% in 2024 vs. 45% in 2025). The survey further indicates that Gen Z adults are about 10 percentage points less likely to be thriving than older generations.
Table 4: Gen Z Thriving Rates—Three-Year Decline
Source: Gallup / WFF (2025)
2.2 Social Media and Body Image
The Cybersmile Foundation carried out a national UK study examining the impact of social media use on Gen Z well-being. They surveyed 1,000 UK participants aged between 16 and 24. Their data suggest that social media has negative impacts on Gen Z. Diagram 2 extracts and highlights some key statistics indicating social media impact on Gen Z well-being. The findings, for example, show that 82% of respondents said social media negatively affected how they feel about their own bodies. 83% said content on social media made them feel pressured to be perfect. Also, 48% said their sleep had been negatively impacted by time spent online. There were, moreover, 38% who said social media made them want to permanently change a part of their body through surgery. Such a figure rose to 51% when we focus on female respondents. These survey outputs imply that social media causes Gen Z not to be able to develop healthy self-relationships, and their self-esteem and behavioural well-being are jeopardised.
Shehab et al. (2025) synthesise existing research and document that depression, anxiety and insomnia are among the most robustly evidenced harms for Gen Z inflicted by the addiction to Instagram and TikTok. In addition, the Gallup survey (2025) data shows that 41% of Gen Z have anxiety about AI specifically, compared to 36% who feel excited and 27% who feel hopeful. Additional data unveils that 49% of Gen Z believe AI will harm their critical thinking skills, which can lead to broader anxiety about dependency and cognitive overload in a way similar to how social media has affected this generation.
Diagram 2: Social Media Impact on Gen Z Well-being
Source: Digital Wellbeing (2025). n=1,000 UK participants aged 16–24.
3: Gen Z and the AI-Disrupted Labour Market
3.1 The Entry-Level Collapse
As of writing this analysis, AI disruption in the labour market is notable and expanding. Mass unemployment, especially youth unemployment, has been widely reported internationally. In Randstad's latest report, entitled “The Gen Z Workplace Blueprint: Future Focused, Fast Moving”, findings from job posting analysis of 11,250 global workers and 126 million job postings show that global postings for roles requiring 0-2 years of experience (meaning entry-level jobs) have fallen by an average of 29 percentage points since January 2024. The collapse of the entry-level job market is more salient in several positions, such as junior technology roles (down by 35%), logistics (down by 25%) and finance (down by 24%) (See Table 5).
Brynjolfsson et al. (2025) from the Stanford Digital Economy Lab analysed ADP payroll records covering millions of US workers. They found that early-career workers (aged 22 to 25) in AI-exposed occupations experienced 16% relative employment declines, controlling for firm-level shocks, while employment for experienced workers remained stable. The labour market adjustments occur primarily via employment rather than compensation, with employment changes concentrated in occupations where AI automates rather than augments labour (such as software development, customer service and clerical work).
Table 5: Entry-Level Job Posting Declines by Sector
Source: Randstad (2025) | (Brynjolfsson et al., 2025)
3.2 Gen Z Career Strategy in Response to AI Disruption
From the same Randstad report, we can see that Gen Z is actively adapting to the AI-induced changes in the labour market by altering their career behaviours and decisions. Table 6 presents Gen Z’s career behaviours and decisions for AI adoption, relative to those from older generations. As discussed in Table 1, 75% of Gen Z use AI to learn new skills. In addition, from Table 6, 55%, 50% and 46% (up from 40% in 2024) use AI for professional problem-solving, use AI for job search and are worried about AI impact on their occupational opportunities, respectively. Their AI-induced career behavioural adjustments not only imply that they are active and willing AI adopters, but also reflect the circumstance that this generation is mostly impacted by AI disruption in the labour market due to the loss of entry-level jobs.
Table 6: AI Adoption in the Workplace—Gen Z Relative to Older Generations
Source: Randstad (2025)
3.3 Shortened Job Tenure for Gen Z
Although media portrayals and general perceptions claim that Gen Z, unlike older generations, lack loyalty to an organisation due to laziness and low resilience, the Randstad report might suggest otherwise. Table 7 presents the job tenure and employment structure by generation. When comparing the first five years of career (instead of the current tenure), we can see that the average job tenure for Gen Z is 1.1 years, relative to 1.8 years for millennials, 2.8 years for Gen X and 2.9 years for Boomers. Here, we see the generational shift where early career job-taking patterns have constantly changed over the last few decades. There are almost half (45%) of Gen Z who are currently working full-time, and 52% are actively looking for a new role. While we cannot tell how many of those 52% who actively seek new roles already have a full-time position, at least the figures suggest that the majority of Gen Z choose to stay active in the labour market (either by finding jobs or already working full-time). Such figures dismiss the general perception that Gen Z are lazy to a certain degree. In fact, the change of the labour market structure, especially the AI disruption that causes the loss of entry-level jobs, may explain why staying active professionally nowadays seems far harder than that of previous generations. The data, moreover, shows that only 11% of Gen Z plan to stay in their current job long term. While this figure ostensibly shows that Gen Z has little loyalty to an organisation or workplace, in fact, the lack of available full-time entry-level positions might justify why many Gen Z members do not want to stay in their current job long term. This is because many might be working part-time or on a freelance and ad hoc basis. Therefore, staying in the current job does not offer any promising career path or financial security for Gen Z to display their loyalty at work.
Table 7: Job Tenure and Employment Structure by Generation
Source: Randstad (2025). Comparison figures reflect the same career stage (first 5 years), not current tenure.
4. Summary and Implications
We can see substantial behavioural changes between Gen Z and their older counterparts. This analysis finds that Gen Z is adapting to AI faster than institutional responses. Their well-being is constantly being jeopardised, caused by both the popularisation of AI and social media. Also, this generation is most vulnerable to AI-induced economic risks, since the labour market disruption mostly erases entry-level job opportunities.
In education, this analysis identifies a policy gap in institutional readiness, which lags AI adoption by a wide margin. Furthermore, the need for AI adoption in educational settings is compounding spatial and socio-economic inequalities. Regarding their well-being, this analysis shows that AI is disrupting rather than enriching Gen Z’s social and mental health. While there is research-based evidence identifying how (AI-integrated) social media are harming this generation, corresponding regulatory responses remain fragmented and not sufficiently effective. In the labour market, although Gen Z actively use AI for upskilling, they are the cohort whose job opportunities are technologically disrupted the most. There is a lack of policy responses on formal AI training access for the young generations, including Gen Z. Even if some jurisdictions may deliver policy responses on formalising AI training and upskilling opportunities for students, there remains an absence of any clear solution on how Gen Z, once sufficiently upskilled, can gain access to entry-level job opportunities.
This analysis was originally published on AI in Society at "Gen Z and AI: Education, Well-being, and the Labour Market".