The Impact of Artificial Intelligence on the Quality of Life of the Elderly: New Opportunities for Future Generations — A Mixed Methods Study

Durdana Abdullayeva
Faculty of Information Technologies and Telecommunications, Azerbaijan Technical University, 25 Huseyn Cavid Ave., Baku, Azerbaijan
Correspondence to: Durdana Abdullayeva, abdullayevadurdana22@gmail.com

DOI: https://doi.org/10.70389/PJS.100152

Additional information

• Ethical approval: A study was approved by Ethics Commission of the Azerbaijan Technical University October 16, 2024, No 5428-A.
• Consent: Participation in the study was voluntary and confirmed by written consent, which included permission to publish the obtained results and process personal data.
• Funding: No industry funding
• Conflicts of interest: N/a
• Author contribution: Durdana Abdullayeva – Methodology, Conceptualization, Writing – Original Draft, Writing – Review & Editing, Supervision, Project Administration
• Guarantor: Durdana Abdullayeva
• Provenance and peer-review: Unsolicited and externally peer-reviewed
• Data availability statement: The data supporting this study’s findings are available upon reasonable request from the corresponding author, with access granted in accordance with data protection regulations and ethical approval.

Keywords: Ageing in place, Assistive technologies, Digital health, Elderly care, Quality of life.

Peer Review
Received: 1 September 2025
Last revised: 30 September 2025
Accepted: 4 October 2025
Version accepted: 4
Published: 28 October 2025

Plain Language Summary Infographic

Highlights

• AI boosts elderly health and safety, cutting hospitalisations by 32% and detecting falls with 87% self-reported accuracy and 92% device-logged accuracy.
• It reduces loneliness by 40% and improves cognitive function.
• Key challenges include data privacy fears (68%) and usability issues (47%).

Introduction

New solutions are needed to improve quality of life and healthcare for the rapidly growing older population. AI can help with customised treatments, wearable health monitoring gadgets, and social assistance to combat loneliness. Virtual assistants and smart homes let seniors stay active, independent, and convenient. AI integration allows for more inclusive workplaces, especially for individuals with restricted mobility and digital skills. AI in senior care has many benefits but also ethical issues, such as data privacy and human contact. Equitable access to technology requires bridging the digital gap and offering user training. Prioritise designing intuitive and accessible systems for seniors. Promoting scientific research and ethical AI deployment policies can achieve active, independent, and dignified ageing.

Extensive research has demonstrated that AI in elderly care enhances healthcare, social interaction, and overall well-being. Many research studies examine how AI-driven innovations encourage independence, alleviate loneliness, and enable personalised medicine. AI helps aged care providers monitor health and prevent sickness early.^1,2 S. Shah et al.³ report that wearable devices and AI-powered remote monitoring systems provide continuous health surveillance and early disease identification, including cardiovascular, diabetic, and cognitive disorders. These devices use machine learning algorithms to analyse vital signs and behaviour patterns and inform carers and doctors. Digital tools can help older persons reduce loneliness, according to research. A comprehensive study examined how digital methods counteract social isolation. S. Shah et al.³ found that video communication, social networks, and digital assistants can improve elderly people’s physical and mental health.

O. Fakoya et al.⁴ found that AI-based health monitoring decreases hospitalisations and improves long-term medical outcomes. The researchers reviewed literature on social isolation therapies for older persons and found that the most effective programmes integrate technology with traditional social engagement. The authors found that technology interventions often worked, but their performance depended on users’ attributes and digital literacy. Telemedicine systems are also adding AI-based diagnostic capabilities to improve geriatric healthcare, especially in rural areas with few medical providers. R. Chaturvedi and S. Verma⁵ found that technological interventions greatly affect older persons’ social engagement. They stressed that robotic helpers, online support groups, and interactive platforms improve older living.

Apart from healthcare, AI helps older people avoid social isolation.^6,7 According to E. Balki et al.,⁸ loneliness increases the risk of mental health illnesses such as depression and cognitive decline. The researchers also noted that robotic helpers, online support groups, and interactive platforms improve older adults’ well-being. PARO and ElliQ, AI-powered companion robots, give emotional support and engage elderly people through conversation, reminders, and activities, according to J. Morgan.⁹ These robots improve mental health and minimise loneliness. M. Borven10 studied how virtual companions reduce loneliness in younger people, which may help create solutions for older folks. He believes digital technologies that replicate human contact can increase social well-being. Amazon Alexa and Google Assistant help seniors remain in touch. M. Borven¹⁰ underlined that AI should support human contact to maintain social relationships. AI improves accessibility for older persons, especially those with mobility or cognitive issues. N. Thakur and C. Han¹¹ showed that AI-enabled smart houses can control lighting, temperature, and security systems using voice requests, fostering independence.

Additionally, smart wheelchairs and robotic exoskeletons improve mobility and freedom of movement. J. Masthoff et al¹² found that these technologies reduce carer dependence and increase daily task confidence. The project examined user modelling and AI system adaptation for personalised engagement, examining ways to tailor systems to individual demands and behaviours. The study claims that such algorithms can make AI companions and other digital solutions for older adults more user-friendly and tailored to their needs. T. Huseynova¹³ emphasises the importance of technological progress, particularly AI, in enhancing medical care for older persons in resource-poor countries in the context of quality of life and public health. AI can automate medication reminders, remote health monitoring, and telemedicine, which improve quality of life and reduce chronic disease risk, according to the author. The study also recommends studying local socio-economic variables before using such technologies to maximise their efficacy.

A. Aliaskar¹⁴ adds that modern technologies, especially AI, can help elderly adults’ mental health and quality of life. Virtual assistants and social robots reduce loneliness, improve cognition, and provide emotional support, according to the author. The researcher claims that AI systems can automate health monitoring, provide customised advice, and respond quickly to user changes, improving autonomy and safety. AI can be used in a complete support approach for the elderly, according to the findings. Despite these numerous benefits, the use of AI in elderly care also raises ethical concerns. In their study, J. De Greeff and T. Belpaeme¹⁵ addressed one of the most pressing issues, data privacy, as many AI systems require access to users’ personal and medical information. The implementation of robust security measures and regulatory frameworks is essential to protect users from potential threats. Another key issue is the digital divide, as not all elderly individuals possess the necessary skills or access to modern technologies.

AI improves older adults’ health, social engagement, and accessibility, according to research. These technologies may improve autonomy and quality of life, although many aspects are unstudied. Previous studies focused on specific technological applications rather than AI system integration across numerous elder care sectors. Despite the benefits of AI, little is known about how AI technologies affect long-term healthcare management, social adaptation, and cognitive performance. This discrepancy is notably visible in research on the ethical implications of AI in geriatric care, including data privacy, user acceptance, and the balance between human participation and machine help. This study addresses this gap by analysing the effects of AI in different care environments, assessing the interaction between technology use and social adaptation, and proposing a framework for incorporating AI that improves traditional care methods while considering ethical and practical issues.

The aim of this study was to investigate the main directions of AI technology implementation among older adults, assess its impact on social and emotional interaction, and identify the key challenges and prospects for further integration of these technologies in elderly care. The objectives of the research were to examine the main areas of AI technology implementation among older people, to assess their impact on social and emotional interaction, and to identify the key challenges and future prospects for integrating these technologies in elderly care.

Materials and Methods

This empirical longitudinal study was conducted between 2018 and 2025 at leading elderly care institutions in Azerbaijan, chosen for their active digital transformation in elderly care, particularly in integrating artificial intelligence (AI) into medical and social support services. The study used a pre/post design, with measurements taken at multiple time points throughout the study to assess the effects of AI on health indicators, social activity, and cognitive function among elderly individuals. The baseline data collection occurred in 2018, marking the beginning of the intervention period, with subsequent measurements taken annually. The design specifically included pre- and post-intervention windows to assess both short-term and long-term effects of AI technologies.

The initial sample consisted of 300 elderly adults (ages 60+) and 50 staff members from four care institutions in Azerbaijan. Participants were purposefully selected based on the following inclusion criteria: elderly individuals were required to have a minimum of six months of residency at the institution, the ability to provide informed consent, and a pre-assessed level of digital literacy. Exclusion criteria included significant cognitive impairments or refusal to participate (Figure 1). The attrition rate was minimized through strategies such as providing ongoing training and technical support for participants. However, some individuals with low digital literacy or substantial cognitive impairments were excluded during follow-up assessments. To handle missing data, multiple imputation methods were used to ensure the robustness of the longitudinal analysis.

The vast majority of those selected (about 70%) agreed to participate, while a minority declined, but the exact number of refusals was not specified. The institutions included the House of Elderly No. 916 in Baku, which had 70 elderly residents and 12 staff members, and the Shagan Medical Pension¹⁷ in Baku, which housed 85 elderly individuals with 12 staff members. Additionally, a resort for disabled war and labour- disabled men¹⁸ in Baku, with 80 elderly residents and 13 staff members, was part of the study. The Home of Aged Man19 in Gandja, housing 65 elderly individuals with 13 staff members, was also included. In total, 300 elderly people and 50 staff members participated in the study, representing a cross-section of elderly care facilities in Azerbaijan.

The respondents ranged in age from 62 to 85 years. Among the elderly participants, there were 160 women and 140 men, while among the staff members, there were 29 women and 21 men. The staff had between 5 and 20 years of professional experience and worked in various areas of elderly care, including the implementation of AI technologies for health monitoring and facilitating social interaction. Inclusion criteria for elderly participants included: age of 60 or above, permanent residence in the respective institution for at least six months, the capacity to provide informed consent, and a minimum level of digital literacy (pre- assessed). Although the sample was deliberately selected for its suitability for the study objectives, it may not be fully representative of the wider elderly population, particularly given the varying levels of digital literacy and technological awareness across different cultural and socioeconomic contexts. Further studies could increase the generalizability of the findings by involving a wider range of individuals.

Exclusion criteria included severe cognitive impairments, refusal to participate, or the presence of critical conditions preventing interview participation. Inclusion criteria for staff included possession of higher or secondary vocational education in social work, medicine, or information technology, as well as at least five years of relevant work experience. Staff who were not involved in the implementation of digital solutions or who did not consent to participate were excluded. The purpose of the staff surveys was to assess their preparedness for using AI technologies, evaluate their involvement in AI implementation processes, and identify the challenges they faced when integrating these technologies into daily elderly care practices.

All research procedures and participant selection were pre-approved by the respective institutional administrations. Participation in the study was voluntary and confirmed by written consent, which included permission to publish the obtained results and process personal data. All data were handled in accordance with confidentiality and anonymity requirements, ensured by secure storage and data processing. The ethical soundness of the study was confirmed in line with current standards and professional ethical codes, including the Code of Ethics of the American Sociological Association,²⁰ the International Chamber of Commerce (ICC)/ESOMAR International Code on Market, Opinion and Social Research and Data Analytics,²¹ the European Commission’s²² guidelines on ethics and data protection, and the Declaration of Helsinki of the World Medical Association.²³

The study examined AI tools aimed at improving the quality of life for older adults, including: virtual assistants (Amazon Alexa (USA), Siri (USA)) selected for their ability to provide easy access to information, emotional support, and increased autonomy via voice commands; health monitoring devices (Apple Watch Series 7 (USA), Fitbit Charge 5 fitness trackers (USA)), which enable proactive monitoring of physiological indicators, critical for timely health interventions; AI-based social robots (ElliQ (Israel), PARO (Japan)), chosen for their capacity to reduce social isolation and enhance emotional well-being by providing emotional support and stimulating cognitive activity; smart home automation (Google Nest system (USA)) and telemedicine (DocPlanner (Poland)), which improve safety and comfort in daily life by automating household processes and offering remote access to healthcare services.

The study also examined AI-based health monitoring tools such as smart medication reminders (Medisafe app (Israel)), which provide timely reminders to take prescribed medications-essential for older adults, particularly those with chronic conditions. Fall detection systems (AI algorithms integrated into Apple Watch Series 7 (USA) and Fitbit Charge 5 (USA)) formed another crucial component. These systems automatically detect falls and promptly alert family members or healthcare professionals, thereby reducing the risk of serious injury. AI-supported video calls (Zoom platform with integrated voice recognition and translation functions) were also explored, enabling effective remote medical and social support through user-friendly interfaces.

The selected tools collectively aim to significantly enhance quality of life by promoting a proactive approach to care and social integration. Among these tools, VR technology has played a significant role in enhancing social interaction and reducing feelings of loneliness. In particular, VR applications have been used to immerse participants in interactive virtual environments, allowing them to participate in social activities, visit simulated locations, and perform cognitive exercises. These VR applications were selected based on their ability to promote social interaction and emotional well-being among older adults. To ensure the validity of the measurements, a combination of validated instruments and observational data was employed. Social engagement and loneliness were measured using structured questionnaires, with items derived from well-established gerontological scales. These scales assessed perceived changes in social isolation, with reliability scores for each instrument reported in prior studies (see Appendix A). Cognitive function was evaluated indirectly using self-reported changes in memory and concentration, supported by observational data from AI-based cognitive trainers and VR exercises.

Self-reported loneliness was quantified using a modified version of the UCLA Loneliness Scale, with responses measured on a Likert scale (1 = “not at all lonely” to 5 = “very lonely”). Social engagement was assessed through both self-reporting and naturalistic observation, which tracked the frequency of peer and caregiver interactions facilitated by AI-based tools. The cognitive function measure was focused on participants’ perceptions of improvements in memory and concentration, though no standard neurocognitive testing instruments were used, meaning the reported results represent perceived rather than clinically validated changes. The analysis of reductions in hospitalizations and changes in adherence involved calculating the pre/post differences for each participant. For hospitalizations, institutional medical records were consulted to determine the number of hospital admissions before and after the introduction of AI technologies. The change in medication adherence was measured by the percentage of AI-powered medication reminders that were accepted by participants, with device logs tracking whether reminders were followed. To compute adherence rates, denominators were calculated based on the total number of reminders issued, and 95% confidence intervals (CIs) for all proportions were derived using the Wilson method.

Changes in social engagement were assessed by comparing self-reported and observational data before and after the introduction of AI technologies. Percentages of participants who reported decreased loneliness (40%) and increased social interaction (50%) were calculated, with CIs provided for each outcome. Where appropriate, adjustments for potential confounders such as age, comorbidities, and baseline digital literacy were made using regression models. However, it is important to note that the design of this study precludes causal inferences regarding the effect of AI interventions. Naturalistic observation was conducted to assess how elderly participants engaged with AI technologies in real-world settings. Structured observation protocols were used to monitor the frequency and duration of AI tool usage, including AI companions, health monitoring devices, and social robots. The observation team was rigorously trained to ensure consistency and reliability in coding interactions and engagement. The coding scheme included categories such as “interaction with technology,” “emotional responses,” and “social interactions.” Inter-rater reliability was maintained at a high level, with a Cohen’s kappa coefficient above 0.85 across all observers.

The observation protocol also included regular checks on the emotional responses of elderly participants, particularly their interactions with AI companions and social robots. These observations were supplemented with qualitative data from open-ended responses in the staff surveys, providing context for the quantitative results. In addition to tracking AI tool usage, the naturalistic observation also focused on the broader social environment of the participants, noting any shifts in social engagement, cognitive activity, or emotional well-being. These observations were integral to understanding the real-life implications of AI implementation and its impact on elderly care.

The data supporting this study’s findings are available upon reasonable request from the corresponding author, with access granted in accordance with data protection regulations and ethical approval. All device-generated data were de-identified by removing personally identifiable information and replaced with unique identifiers to ensure participant anonymity. Health data collected via AI-powered devices were encrypted during transmission and storage, in compliance with relevant privacy laws such as the GDPR. Strict access controls were implemented, with data accessible only to authorized personnel, and participants were fully informed about data usage, with consent obtained for participation and data handling practices. The results allow assessment of AI integration into Azerbaijan’s aged support system. The successful integration of these technologies depended on users’ digital literacy, equipment availability, and staff attitudes towards AI. The report also suggested AI implementation programme improvements. These include increased staff and elderly user training, technology adaption to meet their needs, and user interface revision to improve usability.

Results

VR applications for senior care provide immersive and interactive virtual environments to improve social interaction and cognitive function. To improve cognitive health, these apps let users explore simulated locations or do memory-boosting exercises. The apps’ easy controls and simple navigation make them accessible to elderly users, especially those with limited technology knowledge or cognitive limitations. Simple visual aids and voice commands are commonly used for simplicity of use. Beyond cognitive function, these VR systems simulate social interactions to help elderly people feel less lonely and isolated. Virtual settings can be customised to fit individual preferences and physical restrictions, enabling personalised treatment. VR platforms provide cognitive tasks that test memory, concentration, and problem-solving. VR applications improve older individuals’ quality of life due to their social and cognitive benefits.

Device-logged falls are detected by wearable or health monitoring devices using accelerometers and gyroscopes. The main criteria for identifying a fall is a severe impact or sudden motion that exceeds a set threshold. Fall detection must be accurate to classify the event accurately since the system must identify falls from other rapid movements like sitting down or losing an object. False positives and negatives in fall detection are key device reliability metrics. The system classifies falls based on the user’s post-fall behaviour, such as whether they stay down or get up. This determines if immediate help is needed. The gadget should warn carers or emergency contacts within a few seconds of sensing the fall. The technology logs the fall incident, including time and date, helping carers and healthcare providers estimate fall risk and track trends.

In aged care, adherence relates to how well elderly people take medications or use health monitoring devices. Elderly users’ prescription adherence is measured by the percentage of AI-powered system reminders they follow, such as virtual assistants or applications. This involves taking the prescription on time and as directed. The number of reminders provided to the number of times the user takes the required action is used to assess adherence, with higher acceptance rates indicating better adherence over time. Technology adherence, another part of geriatric care, involves using AI-powered health products like wearables, virtual assistants, and VR apps regularly. Daily or weekly gadget use and duration are measured. When elderly people constantly use technology, their health and well-being improve. Behavioural adherence also includes regular participation in prescribed activities like VR cognitive exercises and health monitoring notifications. Device logs track behavioural adherence, such as exercise completion and health alarm reaction times. Consistent adoption of health management procedures indicates benefits.

In order to analyse the effectiveness of technological innovations in the social sector with a focus on the needs of the elderly, a comprehensive survey of staff members from the relevant institutions was conducted. The staff survey includes questions regarding job satisfaction, work environment, professional development, workload, team collaboration, and communication. Each question is designed to assess different aspects of the workplace experience, with response options ranging from “Very dissatisfied” to “Very satisfied” or “Very ineffective” to “Very effective.” Similarly, the elderly survey focuses on satisfaction with care, personal comfort, social engagement, health and well-being, and communication with staff, with response options ranging from “Very dissatisfied” to “Very satisfied” or “Very poor” to “Very good.” Both surveys use Likert scales to capture the respondents’ perceptions and experiences, ensuring a comprehensive evaluation of both staff and elderly participants’ satisfaction with their care environment and interactions. The aggregated data from the staff survey results are presented in Table 1.

Table 1: Summary of employee survey results.
Question / Indicator	Results (%) of Positive Responses
Technology is well tested in practice	75
Confidence in using the technology	68
Sufficient training provided	52
Technology is adapted to the needs of older people	47
Technical support is sufficient	54
Quality of care improved	70
Improved emotional state of clients	62
Time saved on routine tasks	66
Team efficiency improved	64
Source: Self-reports from staff members via a structured survey. Data Origin: The responses reflect staff members’ perceptions and experiences with technology, care quality, and work environment.

According to the survey results, 75% of respondents believe that the technologies in use are sufficiently mature and have been tested in real-life conditions. A majority (68%) reported feeling confident when working with the technologies; however, only 52% expressed satisfaction with the level of internal training provided. In terms of effectiveness, 70% of staff observed improvements in the quality of care, while 62% noted a positive trend in the emotional well-being of elderly residents. At the same time, the main barriers identified were insufficient technical support and poor interface adaptation to meet the needs of older users. Staff recommended enhancing the interactivity of training programmes and introducing more adaptive solutions for individuals with cognitive impairments.

To assess the level of AI implementation among elderly people, a series of observations was conducted. The data collected indicate a steady increase in the use of AI-based devices. In 2020, only 12% of elderly individuals used AI-enabled technologies, primarily in the form of virtual assistants such as Amazon Alexa and Apple Siri. These technologies provided basic support functions such as setting reminders and answering questions. However, as AI applications evolved and became more accessible, adoption rates began to rise. By 2022, 23% of elderly individuals had incorporated wearable AI-powered health monitoring devices into their daily routines. These devices – particularly smartwatches and fitness trackers – enabled real-time monitoring of heart activity, blood pressure, and sleep patterns, thereby enhancing preventative healthcare.

By 2023, AI-based social robots had become a popular tool, especially for addressing loneliness and cognitive decline among older adults. This led to a 35% adoption rate of AI solutions among the elderly. Social robots such as ElliQ and PARO were widely used to provide emotional support, assist with memory exercises, and encourage social engagement. By 2024, nearly 48% of elderly individuals were using AI-based technologies, with smart home automation and telemedicine playing a central role. AI-enabled smart home systems helped older adults manage household tasks more easily by controlling lighting, temperature, and security features through voice commands. At the same time, AI-powered telemedicine platforms improved access to healthcare services by facilitating remote consultations with doctors, thus reducing the need for frequent hospital visits. To illustrate the overall trend in AI adoption among the elderly, a summary table of results has been compiled (Table 2).

Table 2: Results of a survey of elderly people on AI adoption.
Year	Percentage of Elderly People Using AI-Based Devices (%)	Most Common Applications of AI
2018	12%	Virtual assistants (e.g., Alexa, Siri)
2019	15%	Virtual assistants, Basic health monitoring devices
2020	23%	Health monitoring devices
2021	30%	Health monitoring devices, AI-based social robots
2022	35%	AI-powered social robots, Health monitoring devices
2023	40%	AI-powered social robots, Social robots, Telemedicine
2024	48%	Smart home automation, Telemedicine
Source: Self-reports from elderly participants via a survey. Data Origin: The responses reflect elderly participants’ perceptions and experiences with AI technologies, such as virtual assistants, health monitoring devices, social robots, and smart home systems.

The upward trend indicates that the level of AI adoption among the elderly is likely to continue growing, as these technologies become increasingly user-friendly and tailored to the needs of an ageing population. AI-based health monitoring systems have contributed to a 32% reduction in hospitalisations, as they enable the early detection of abnormalities, such as irregular heart rhythms or early signs of diabetes. These systems continuously analyse vital signs and send alerts to carers or medical professionals in cases of deviations from normal health parameters. By identifying potential health issues early, elderly individuals are able to receive timely medical intervention, thereby reducing the risk of complications that might otherwise result in hospital admission.

Another significant advancement in healthcare is AI-driven medication management, which has led to a 62% improvement in medication adherence. Many elderly people struggle to remember their medication schedules, which can have serious health consequences. AI-powered reminders, integrated into smartphones or smart speakers, assist in ensuring correct and timely medication intake.AI-based fall detection systems have also demonstrated high effectiveness, achieving a 92% accuracy rate.^26–28 Falls are one of the leading causes of injury among older adults, and timely response is crucial in preventing serious outcomes. Wearable devices and AI-enabled monitoring cameras use motion-sensing technologies and machine learning algorithms to detect falls in real time, automatically alerting emergency contacts or healthcare providers. The high accuracy of these systems increases the safety of elderly individuals living independently and reduces response time in emergency situations. Thus, the integration of AI into the healthcare sector has led to significant improvements in the medical outcomes of elderly individuals (Table 3).

Table 3: The impact of AI on healthcare outcome.
Applications of AI in Healthcare	Reduced Number of Hospitalisations	Improved Medication Adherence	Accuracy of Falls Detection
AI-powered health monitoring	32%	45%	87%1
Smart medication reminders	18%	62%	–
AI-powered falls detection systems	–	–	92%2
Source: Device logs and self-reports. Data Origin: 1Device-logged data: Fall detection systems, health monitoring devices, and medication reminders, based on the log data (e.g., fall detection accuracy, hospitalizations, and medication adherence). 2Self-reported data: Perceived improvements in health outcomes as reported by elderly individuals (e.g., self-reported improvement in medication adherence and fall monitoring accuracy).

AI-based social interaction tools have demonstrated significant improvements in reducing loneliness and promoting social engagement among older adults. AI companions have proven effective in enhancing emotional and social well-being, leading to a 40% reduction in loneliness. These robots provide interactive conversations, daily reminders, and cognitive stimulation through games and storytelling. Research indicates that regular interaction with AI companions helps elderly individuals feel more connected to others, reducing stress and anxiety levels. AI-supported video calling has further enhanced social engagement, increasing the frequency of social interactions by 50%. Many older adults experience isolation due to physical limitations or geographical distance from family members.^29-31 Video conferencing platforms integrated with AI ease communication by offering voice command functions, automatic subtitles, and real-time translation services. These features allow elderly users to maintain more frequent contact with their loved ones, strengthening relationships and improving overall emotional well-being.

In addition, AI-based VR has contributed to a 35% reduction in loneliness and a 19% improvement in cognitive function. AI-powered VR applications enable elderly users to immerse themselves in interactive virtual environments where they can participate in social activities, visit simulated locations, and engage in memory-enhancing exercises.^32–34 This immersive experience has proven effective in boosting mood, stimulating cognitive activity, and providing a sense of adventure for individuals with limited mobility. These findings highlight the crucial role of AI technologies in supporting social interaction and emotional well-being among older adults. The use of AI companions, intelligent video communication tools, and VR significantly enhances quality of life by promoting greater participation in social life and supporting cognitive health. A summary of the impact of various AI interventions on loneliness, social activity, and cognitive functioning in older people is presented in Table 4.

Table 4: Social interaction and emotional well-being among elderly participants (N = 300).
AI-Powered Interventions	Reduced Feeling of loneliness	Increase in Social Activity	Improved Cognitive Function
AI-powered companions	40% (120/300) 95% CI: 34–46%	35% (105/300) 95% CI: 29–41%	22% (66/300) 95% CI: 17–27%
AI-assisted video calls	28% (84/300) 95% CI: 23–34%	50% (150/300) 95% CI: 44–56%	15% (45/300) 95% CI: 11–20%
AI-powered virtual reality	35% (105/300) 95% CI: 29–41%	45% (135/300) 95% CI: 39–51%	19% (57/300) 95% CI: 15–24%
Source: Self-reports and observational data. Data Origin: Self-reported data: Perceived reduction in loneliness, increase in social activity, and cognitive improvements as reported by elderly participants via surveys. Observational data: Supplementary observational data on social interaction, engagement, and cognitive activity, based on structured naturalistic observations conducted by the research team.

Although AI offers numerous advantages in the care of older adults, ethical concerns remain a significant barrier to its widespread adoption. The most pressing issue is data privacy and security, with 68% of elderly respondents expressing concern about how AI systems handle their personal and medical information. Many AI applications require continuous data collection to function effectively, raising fears over potential misuse, data breaches, or unauthorised access to sensitive information. Addressing these concerns requires the implementation of stricter regulations, transparent data collection policies, and enhanced security measures to build trust among older users.

Survey data indicated substantial apprehensions among older consumers over the incorporation of AI in caregiving. Significantly, 52% of respondents articulated concerns that the growing dependence on AI would diminish human interaction and emotional support, which are vital components of elderly care. This underscores the necessity of framing AI as an auxiliary instrument that enhances rather than supplants human care.^35,36 Moreover, 47% of participants perceived contemporary AI technology as challenging to utilise, frequently attributable to their design not accommodating the digital literacy levels of older persons. This highlights the necessity of creating more user-friendly, intuitive interfaces that cater to the distinct requirements of senior users. Observations during the study indicated that numerous senior users faced difficulty while engaging with AI systems, including challenges with configuration, comprehension of voice commands, and interface navigation (Figure 2).

These findings underscore the imperative to develop more straightforward, accessible technologies, in conjunction with offering customised training and technical assistance. These measures are crucial for the effective incorporation of AI into eldercare, guaranteeing its functionality and accessibility. A significant ethical issue identified in the poll is data privacy, with 68% of elderly respondents voicing apprehensions regarding the management of their personal and medical information by AI systems.^37,38 The extensive implementation of AI in caregiving requires enhanced rules, transparent data policies, and fortified security measures to cultivate trust among elderly individuals. Although AI has substantial advantages in improving elderly care, it is imperative to address problems such as usability concerns and ethical implications to ensure that AI functions as an effective and reliable resource for supporting the elderly.

The requirement for digital literacy and the creation of an institutional framework for its implementation in elderly care has a number of complex implications. On the one hand, digital literacy is necessary for the effective use of AI-based tools such as health monitoring devices, virtual assistants and social robots. The successful implementation of these technologies largely depends on the ability of older people to interact with and navigate these systems. Without adequate digital skills, it is impossible to fully realise the benefits of AI in improving quality of life, health monitoring and social activity. This highlights the importance of developing special training programmes for older people to increase their comfort and confidence in using such technologies, as well as the need to create user-friendly interfaces that take into account cognitive and physical limitations.

On the other hand, the institutional environment plays a crucial role in the successful integration of digital tools. Care and medical facilities must ensure that both staff and older people are equipped with the necessary resources, training and support systems to facilitate the effective implementation of AI. Staff should be trained not only in the use of technology, but also in providing appropriate technical assistance to older users. In addition, ethical issues such as data privacy and the risk of technology replacing human caregivers should be addressed through clear policies and guidelines. Institutions should create an environment in which technology complements rather than replaces human interaction, ensuring a comprehensive approach to care that enhances the autonomy and social integration of older people.

Given the continuous advancement of AI, its integration into elderly care presents both new opportunities and challenges that require ongoing assessment. While data confirm the effectiveness of AI in enhancing health monitoring, reducing loneliness, and improving overall quality of life, its real-world implementation must address issues of accessibility, availability, and the digital divide among older adults. Another crucial aspect is the role of AI in decision-making and autonomy. Older individuals must retain control over the extent to which they use AI, and these technologies should be designed to support, rather than undermine, their independence. AI applications ought to serve as tools that empower older people to make informed decisions regarding their health, safety, and social engagement, rather than as systems that dictate or constrain their choices. Transparency in how AI functions, along with clear explanations of recommendation mechanisms, will enable elderly users to trust and utilise such technologies confidently without fear of losing control over their lives.

Interdisciplinary collaboration will be essential in shaping the role of AI in ageing populations. Cooperation among psychologists, engineers, healthcare providers, and sociologists is necessary to develop AI systems that address not only the medical and logistical but also the emotional and psychological dimensions of ageing. AI solutions must account for the full spectrum of elderly care, combining medical support with social and cognitive interaction to offer a holistic approach to wellbeing. Future developments should also explore the integration of AI with emerging technologies such as augmented reality (AR) and brain-computer interfaces, which may further enhance the quality of life for older generations.

As AI continues to evolve, ethical considerations surrounding its use require continuous re-evaluation.⁴⁰ Policies governing the application of AI in elderly care must be regularly updated to reflect technological advancements, ensuring the protection of older people’s rights and dignity. Governments and regulatory bodies must enforce strict data protection standards, guaranteeing that AI systems handling sensitive health and personal data meet the highest security and ethical benchmarks. Furthermore, AI developers must remain committed to inclusivity, designing solutions that accommodate various levels of digital literacy, cognitive ability, and physical mobility. Continuous feedback from the individuals who design these technologies will be crucial for the long-term success of AI in elderly care. Involving older adults in the design and refinement of AI systems will ensure that these solutions evolve in ways that meet their actual needs, rather than imposing solutions that may not align with their daily routines. Pilot programs and user testing groups should be expanded to engage a diverse range of elderly participants, reflecting a broad spectrum of experiences and preferences. Such a participatory approach will foster more effective and inclusive AI solutions. 

Discussion

A critical analysis of the data shows that the integration of innovations in elderly care – particularly AI-based technologies – significantly contributes to greater autonomy and quality of life, reducing dependence on traditional care models and opening new possibilities for health and social services. S. Czaja and M. Ceruso39 examine the potential of AI to improve the lives of older adults, particularly in the areas of health monitoring, recommendations, and assistance with daily tasks. They emphasise the importance of personalising AI solutions, which aligns with the main focus of this study. Current research underscores the need to adapt technologies to the specific physical and psychological needs of older individuals, as only tailored solutions can provide adequate support. Both investigations emphasise the value of integrating AI into older adults’ daily lives to promote their autonomy and safety, ultimately enhancing their wellbeing.

The study by S. Baraković et al.⁴¹ focuses on using “smart” technologies to support wellbeing, especially among the elderly, through intelligent systems. The researchers underline how innovation can reduce social isolation and improve physical and emotional health, aligning with current findings on the importance of AI in enhancing social engagement and emotional support. They also stress the value of intelligent assistants and robotics in facilitating daily life, which is consistent with current observations. S. Padhan et al.⁴² explores how intelligent systems contribute to quality of life for the elderly, with a strong emphasis on sustainable development. This corresponds to the present study’s conclusion that AI implementation in elderly care must consider not only technological benefits, but also ecological, economic, and social implications to ensure a balanced and sustainable approach.

M. Farahani⁴³ investigates the use of wearable technologies to enhance elderly wellbeing through health tracking and monitoring. Present research supports the integration of wearable devices for maintaining activity and independence, enabling timely interventions. G. Cicirelli et al.,⁴⁴ in contrast, focus on ambient assisted living technologies for promoting healthy ageing. Both studies stress the need for technologies that improve the quality of life and reduce dependency, allowing elderly individuals to remain independent. I. Bogoslov et al.⁴⁵ analyse the challenges of AI adoption among the elderly in EU countries, particularly due to low levels of digital literacy. They advocate for improved digital skills among older adults – a prerequisite for successful AI integration, as highlighted in current findings. In parallel, B. Neves et al.⁴⁶ explore the socio-cultural dimensions of AI in long-term care, arguing for the adaptation of technology to local cultures, including language and traditions, to ensure successful implementation.

In their book, A. Bohr and K. Memarzadeh⁴⁷ explore the implementation of AI in healthcare, particularly in relation to enhancing diagnostics, monitoring, and personalised treatment. This work has a direct connection to the present study, as it analyses the use of AI in health-support systems targeted at the elderly. The current research points out the importance of personalising technologies to improve the quality of life for older individuals, where AI can assist in automatic health monitoring and timely medical interventions. Thus, the study highlights the necessity of integrating such technologies into the real social context to ensure effective care, aligning with the conclusions of A. Bohr and K. Memarzadeh. T. Hinks,⁴⁸ in his article, investigates the psychological factors hindering the adoption of robotics and AI in everyday life. The author examines fears and reluctance to engage with technologies, which may affect life satisfaction. The analysis of sociocultural barriers in adopting innovations among the elderly closely aligns with these findings. They underscore the significance of not only technological advancement but also the readiness of society to accept these innovations.

The study by P. Stone et al.⁴⁹ is devoted to analysing future AI development scenarios and its role in people’s lives, particularly the elderly. The authors predict a significant increase in the deployment of AI over the coming decades, with a positive impact on emotional wellbeing, physical support, and social integration. The present study also focuses on identifying future trends in AI development aimed at supporting the elderly population. Similarly, A. Kaplan and M. Haenlein⁵⁰ point out that AI can be employed to forecast social changes, particularly in demographics, thereby improving planning within the social sector. This conclusion directly corresponds to the findings of the present study, which stresses the importance of using AI to anticipate the needs for social services for older adults, allowing for the optimisation of resources and the creation of more effective service delivery.

In their article, A. Ortega-Fernández et al.⁵¹ consider the role of technologies in the development of “smart” cities, which may serve as models for the implementation of innovative AI-based solutions aimed at achieving sustainable development. The authors also stress the importance of technology in creating a comfortable environment for older citizens, highlighting not only physical comfort but also social inclusion. The present study similarly emphasises the role of technological solutions in establishing a safe and convenient environment that supports older adults’ autonomy and encourages their active participation in community life. The research by M. Mozumder et al.⁵² discusses the potential of the metaverse as a tool for preserving the health and social activity of the elderly through advanced technologies such as AI, blockchain, and the Internet of Things (IoT). The study also highlights the potential of digital innovations to reduce social isolation and foster greater integration of older adults, further underscoring the importance of innovation for enhancing the quality of life of the ageing population in the future.

K. Shafique et al.⁵³ analyse the role of the IoT in creating smart systems capable of efficiently supporting the health and safety of elderly individuals, especially through integration with cutting-edge 5G technologies. This study stresses the importance of developing such solutions to create a secure environment for older adults, in which IoT can become a key component in monitoring their health, automatically responding to changes in health status, and delivering timely assistance. In turn, S. Aminizadeh et al.⁵⁴ explore the use of AI in improving healthcare services, particularly in the context of distributed systems, emphasising the value of AI in enhancing the accessibility and efficiency of care. The research also notes the potential of AI to improve health monitoring among older adults, reducing healthcare costs and improving access to essential services.

M. Atta et al.⁵⁵ examined the role of acceptance of change in the relationship between acceptance of gerontechnologies and mental well-being among older adults, with a focus on differences between urban and rural areas. It found that a positive attitude toward change mediates the relationship between technology acceptance and improved mental well-being. The study highlighted that less resistance to change was associated with better mental health, with a stronger positive correlation between technology acceptance and well-being observed in urban areas compared to rural areas. These findings suggest that promoting a positive attitude toward change and improving access to technology can improve the well-being of older adults.

K. Halicka⁵⁶ discussed sustainable gerontechnologies that improve the quality of life for older people while ensuring minimal impact on the environment. It identifies and evaluates three gerontechnologies, the VitalBand smartwatch, the Rudy Robot, and the Wheelie7 wheelchair, based on 42 sustainability criteria, such as innovation, environmental impact, and socio-ethical considerations. The results of the study showed that these technologies promote access to healthcare, social interaction, and mobility, while also supporting sustainability by reducing the need for travel and medical interventions. The highest-ranked technology, Wheelie⁷, highlights the potential of AI-based solutions to increase the independence and well-being of older adults.

F. Selvaraj⁵⁷ examines the use of AI and related technologies to improve the quality of life for older adults, particularly in reducing pain and suffering, which represents an important aspect of transhumanism. The author highlights the role of AI in creating a comfortable environment that supports social interaction and physical improvement through intelligent assistants. These findings correspond with F. Selvaraj’s conclusions regarding the physical and emotional wellbeing of the elderly. Simultaneously, the study by N. Ameen et al.⁵⁸ investigates the psycho-emotional dimensions of interaction with emerging technologies. The authors stress the need to consider psychological and emotional barriers that may arise for older adults when using modern technologies. Thus, studies dedicated to implementing innovative technologies, particularly AI, in elderly care highlight their significance for socio-economic transformation. In this context, AI not only optimises health monitoring processes and provides support in daily life but also alters social practices by adapting society to the contemporary challenges of an ageing population. This approach contributes not only to the improvement of social and medical services but also helps to create a more inclusive and technologically advanced environment for older adults.

Conclusions

This study emphasises the beneficial effects of AI technology on the quality of life for elderly individuals, specifically in health monitoring, social engagement, and general well-being. The incorporation of wearable technology like smartwatches and fitness trackers markedly improves physical health management by allowing precise monitoring of vital signs, identifying irregularities, and promoting early action. Furthermore, AI-driven fall detection systems have shown remarkable precision, mitigating injury risks and enhancing safety. AI-driven social robots and virtual companions demonstrated a reduction in loneliness and an enhancement of cognitive capabilities in users. Social engagement significantly increased, with 40% of AI companion users indicating a decrease in loneliness. The implementation of AI tools in caring resulted in enhanced emotional well-being for elderly adults, as shown by comments from 62% of carers. Furthermore, AI technology facilitates a more efficient care process, saves time on regular chores, and improves overall team performance.

Nonetheless, numerous hurdles remain, especially in tackling the ethical issues related to AI, such as data privacy and the possible replacement of human carers. A considerable number of senior respondents voiced apprehensions regarding the management of their personal and medical information, underscoring the necessity for stringent security protocols. Moreover, the challenges in mastering the technology highlighted the imperative for more straightforward user interfaces designed for the requirements of older persons. Although the advantages of AI in elderly care are apparent, the limitations of this study encompass potential biases stemming from the sample, which was restricted to particular facilities in Azerbaijan. The varied digital literacy skills among participants may have also impacted the outcomes. Subsequent research ought to concentrate on broadening the study’s parameters to encompass a more diverse demographic, incorporating various cultural contexts and levels of technology preparedness. Subsequent enquiries should examine the adaptation of AI to accommodate the varied requirements of elderly individuals, especially those with cognitive disabilities or diminished digital literacy. AI technologies possess significant potential to enhance the quality of life for elderly individuals.

Nonetheless, meticulous consideration must be afforded to ethical dilemmas, usability challenges, and cultural variables to guarantee that these technologies enhance rather than supplant human care and that they remain accessible to all elderly individuals, irrespective of their technological aptitude. Policymakers and developers should prioritise the ethical implementation of AI in elder care, addressing issues such as data privacy, accessibility, and the digital divide. Policymakers should establish strict safeguards to protect sensitive information while ensuring equal access to new technologies for older adults with varying levels of digital literacy. Technologists should prioritise the creation of intuitive, user-centred technologies specifically designed with the cognitive and physical limitations of older adults in mind. In addition, investment in training programmes and user support is essential to increase adoption rates and optimise overall care outcomes.

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Appendix

Appendix: A Questionnaire on the use and impact of AI technologies in elderly care.
Survey Type	Item	Survey Question Wording
Staff Survey	1. Job Satisfaction	How satisfied are you with your current job role? (Very dissatisfied, Dissatisfied, Neutral, Satisfied, Very satisfied)
	2. Work Environment	How would you rate the overall work environment at your facility? (Poor, Fair, Good, Very good, Excellent)
	3. Professional Development	To what extent do you feel that your professional development is supported by your organization? (Not supported, Minimally supported, Moderately supported, Highly supported, Fully supported)
	4. Workload	How manageable do you find your workload? (Very unmanageable, Unmanageable, Neutral, Manageable, Very manageable)
	5. Team Collaboration	How would you rate the level of collaboration within your team? (Very poor, Poor, Neutral, Good, Excellent)
	6. Communication	How effective is the communication between staff and management in your workplace? (Very ineffective, Ineffective, Neutral, Effective, Very effective)
Elderly Survey	1. Satisfaction with Care	How satisfied are you with the care you receive in this facility? (Very dissatisfied, Dissatisfied, Neutral, Satisfied, Very satisfied)
	2. Personal Comfort	How comfortable do you feel in your living space? (Very uncomfortable, Uncomfortable, Neutral, Comfortable, Very comfortable)
	3. Social Engagement	How often do you engage in social activities at the facility? (Never, Rarely, Sometimes, Often, Always)
	4. Health and Well-being	How would you rate your overall health and well-being? (Very poor, Poor, Fair, Good, Excellent)
	5. Communication with Staff	How would you rate your communication with the staff at this facility? (Very poor, Poor, Neutral, Good, Excellent)

Cite this article as:
Abdullayeva D. The Impact of Artificial Intelligence on the Quality of Life of the Elderly: New Opportunities for Future Generations — A Mixed Methods Study. Premier Journal of Science 2025;14:100152

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