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I require a presentation of your dissertation topic, methodology, and target population for your research. NOTE: Topic: A Quantitative Study of
the Role of AI in Alleviating Loneliness among Adults in the United States.I need a ppt minimum of 6 slides inclusive of introduction of the topic, methodology and target population for the research topicNOTE: below is the attached topic which i have worked on.
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Topic: A Quantitative Study of the Role of AI in Alleviating Loneliness among Adults in
the United States
Proposed Topic
The topic I aim to study in the field of artificial intelligence (AI) and ethical processes
involves a quantitative study of the role of AI in alleviating loneliness among Adults in the
United States. Loneliness is the dissatisfaction with the quality or quantity of an individual’s
social relationship status (Taylor et al., 2023). It determines the quality of life (Jones et al., 2021)
and can lead to several physical and mental health illnesses, such as cardiovascular disease and
depression (O’Shea et al., 2021). It can also lead to death at a higher rate among older adults
compared to other leading mortality risks such as smoking and obesity (Dhakal et al., 2023).
Malani et al. (2023) reported that (34%) of adults aged 50–80 reported feeling isolated from
others (29% some of the time, 5% often) in 2023.
In 2021, the total number of older adults aged 65 and above in the United States is 48
million, expected to increase to about 88 million by 2050. This demographic increase has raised
significant concerns about how older adults could be cared for (Iciaszczyk, 2021). As a complex
technology, AI is one of the best ways of fostering meaningful physical human connections
(Berridge et al., 2023). Due to this, this proposed dissertation focuses on using AI as a
technology-assisted intervention in alleviating loneliness among adults in the United States. This
study will focus on the value alignment and fairness ethics of AI to encourage AI development,
which will uphold human morals, values, and fairness while simultaneously alleviating
loneliness (Grey et al., 2024). It will also focus on seeking adults’ opinions on what they want the
developed AI to do for them.
This present study is inspired by Zheng et al. (2023), as they suggested from their
findings that the integration of AI in emotions of loneliness, depression, and anxiety (EMOLDA) research has a great potential to encourage new insights and problem-solving solutions to
assist individuals facing loneliness, depression, and anxiety. They also further encourage further
research in the area of AI and loneliness, depression, and anxiety. Additionally, as stated above,
the present study will look into what adults want AI to do for them so their suggestions can be
incorporated into future AI designs to meet their expectations and needs. Berridge et al. (2023)
inspired this part of the research by encouraging further studies to focus on user-centered AI
design, not implementation, as desirability has more impact (Berridge et al., 2023).
Research Questions
RQ1: Is AI an effective means of alleviating loneliness among adults in the United States?
RQ2: What should be the focus of AI engineers and developers when building AI to alleviate
adult loneliness in the United States?
Methodology
This study will use a quantitative research method to investigate the use of AI in
alleviating loneliness among adults in the United States. Quantitative research involves
numerical representations of empirical phenomena to investigate a relation between a set of data
or variables to get a specific outcome (Apuke, 2017; Borgstede & Scholz, 2021). The instrument
that will be used is a questionnaire involving close-ended research questions on a 5-point Likert
scale to test participants’ opinions on using AI to alleviate their loneliness and what they desire
the AI to do for them. The questionnaire will be given to participants who can answer the
questions themselves, and I will help those who cannot, based on their age or health, to select
their opinions after reading the questions to them. The Likert scale will be tested for validity for
all research questions using Croblanch’s alpha. A descriptive statistics analysis of all participant
responses will be done using SPSS. Finally, results and interpretation will be presented to answer
the research questions.
Population & Sample Selection
The target population of the present study is adults aged 45 and above. The age selection
will start at 45, as research shows that 1 in 3 adults of age 45 feel lonely in the United States
(CDC, 2023). A total of 100 samples will be selected from the large target population in different
nursing homes, independent living facilities, and senior living communities in Ohio. The
inclusion criteria of the sample will involve adults aged 45 and above, feeling loneliness in
different nursing homes (Rodríguez-Martínez et al., 2024), independent living facilities (Jones et
al., 2021), and senior living communities in Ohio, in the United States. Ohio has over 9.1 million
adults, with more than 2 million being seniors. Its capital is Columbus, with a total area of
116.096 km2. IRB approval will be collected before data collection, and ethical protocols will be
implemented to ensure the validity of the data.
Theoretical Framework and Background
The Social Capital Theory will be employed as the theoretical framework of the present
research to study the role of AI in alleviating loneliness among adults in the United States. The
Social Capital Theory was first defined by Bourdieu (1985). It is a collective asset in the form of
shared norms, beliefs, values, trust, social relations, networks, and institutions to facilitate
collective and cooperative mutual benefits. The theory focuses on social relationships and their
major elements, including civic engagement, generalized trust, social networks, and norms of
reciprocity. The common types of social capital include horizontal and vertical; strong and weak;
bonding, bridging, and linking; and structure and cognitive (Bhandari & Yasunobu, 2009). Both
cognitive and structural Social Capital play a vital role in the smooth transition into the AI era
(Inaba & Togawa, 2019).
The Social Capital Theory is the best theory for the present investigation, as it directly
correlates positively with AI perception. This is because it focuses on individuals’ shared
meaning, interpretation, and representation with one another (Inaba & Togawa, 2019).
Ambagtsheer et al. (2024) discovered that the Social Capital Theory is the most commonly cited
in studies on the effectiveness of technology interventions in reducing social isolation and
loneliness among older people. The theory will serve as a lens to study the meaning,
interpretation, and representation of interaction between AI and adult to alleviate their loneliness.
This will involve asking participants how they want AI to communicate with them so AI
designers and engineers can incorporate it in their AI designs and development to alleviate
loneliness in adults.
Link to Program Goals and Courses
By emphasizing the role of ethics in AI development, this study focuses on how adults
want AI developed to alleviate their loneliness. This topic is connected to AI ethics of upholding
human morals, values, and fairness while simultaneously alleviating loneliness.
Practical Implications and Future Research
Conclusions from the present study will be practicable for industry experts, legislators,
and AI developers and engineers, as well as ethical standards on how AI can be developed to
alleviate adult loneliness while simultaneously upholding human morals, values, and fairness.
The research will also make recommendations for further applied studies implementing the
guidelines and outcomes of the present paper. Additional recommendations will include more
participants for a broader conclusion regarding the research focus.
The suggested topic on ethical considerations in AI decision-making algorithms will
uniquely contribute to the recently booming domain of artificial intelligence. The comprehensive
strategy will enable collaboration across disciplines and individuals. The study will also provide
the groundwork for future debates and progress on getting AI technology morally sound.
Timeline and Milestones
A comprehensive timeline will be developed that maps specific dates for each research
phase, from literature review, data collecting, analysis, and thesis writing. Feedback sessions and
regular progress reports provided by the advisor will be implemented accordingly and timely till
dissertation completion.
Financial Considerations
A tentative estimate of the funds required will be offered, encompassing costs for
research data collection, payments to the participants, travel to the field (if necessary),
software/tools, and dissemination activities.
Limitations and Delimitations
The limitation of the present study is that the research will focus specifically on Ohio in
the United States. The research data will be collected from adults in nursing homes, independent
living facilities, and senior living communities in Ohio and the United States.
References
Ambagtsheer, R.C., Borg, K., Townsin, L., Pinero de Plaza, M.A., O’Brien, L.M., Kunwar, R., &
Lawless, M.T. (2024). The effectiveness of technology-based interventions in reducing
social isolation and loneliness among community-dwelling older adults: a systematic review,
Archives of Gerontology and Geriatrics Plus. 1(1).
https://doi.org/10.1016/j.aggp.2024.100008.
Apuke, D. (2017). Quantitative Research Methods A Synopsis Approach. Arabian Journal of
Business and Management Review (Kuwait Chapter), 6 (10), 40-47. DOI:
10.12816/0040336
Berridge, C., Zhou, Y., Robillard, J. M., & Kaye, J. (2023). Communicating robots to reduce
loneliness among older adults: conceptual benefits and potential strategies. Frontiers in
psychology, 14. https://doi.org/10.3389/fpsyg.2023.1106633
Bhandari, H. & Yasunobu, K. (2009). What is Social Capital? A Comprehensive Review of the
Concept. Asian Journal of Social Science, 37(3), 480-510. doi:
10.1163/156853109×436847
Borgstede, M. & Scholz, M. (2021). Borgstede, M. & Scholz, M. (2021). Quantitative and
quantitative methods of collection and reproduction: a representative review. In front of Psychol,
12.
https://doi.org/10.3389/fpsyg.2021.605191
Centers for disease control and preventions. (2023). Health Risks of Social Isolation and
Loneliness. https://www.cdc.gov/emotional-wellbeing/socialconnectedness/loneliness.htm#:~:text=More%20than%201%20in%203,lonely%20in%20t
he%20United%20States.&text=Having%20a%20lower%20income%20(less,Being%20m
arginalized%20or%20discriminated%20against
Dhakal, U., Koumoutzis, A., & Vivoda, J.M. (2023). Better Together: Social Contact and
Loneliness among U.S. Older Adults during COVID-19. The Journals of Gerontology:
Series B, 78(2), 359-369, https://doi.org/10.1093/geronb/gbac136
Grey, E., Baber, F., Corbett, E. Ellis, D., Gillison, F., & Barnett, J. (2024). Using technology to
address loneliness and isolation among older adults: the role of social workers. BMC Public
Health 24. https://doi.org/10.1186/s12889-023-17386-w
Iciaszczyk, C. (2021). Nursing Homes and Loneliness among Older Adults in the United States.
MA Research Paper, 54. https://ir.lib.uwo.ca/sociology_masrp/54
Inaba, Y. & Togawa, K. (2019). Social capital in the creation of AI perception.
Behaviormetrika. https://doi.org/10.1007/s41237-020-00107-7
Jones, V.K., Hanus, M., Yan, C., Shadow, M.Y., Borón, J.B. & Bicudo, R.M. (2021) Reducing
loneliness among older adults: the role of voice personal assistants and social media. In front of.
Public Health, 9. doi: 10.3389/fpubh.2021.750736
Malani, P., Singer, D., Kirch, M., Solway, E., Roberts, S, Smith, E., Hutchens, L., Kullgren,
J. (2023). Loneliness trends among adults in the period 2018-2023. University of
Michigan National Survey on Healthy Aging. https://dx.doi.org/10.7302/7011
O’Shea, B. Q., Finlay, J. M., Kler, J., Joseph, C. A., & Kobayashi, L. C. (2021). Loneliness
among US Adults Aged ≥55 early in the COVID-19 Pandemic: Findings from the
COVID-19 Coping Study. Public health reports (Washington, D.C.: 1974), 136(6), 754764. https://doi.org/10.1177/00333549211029965
Rodríguez-Martínez, A., Amezcua-Aguilar, T., Cortés-Moreno, J., Jiménez-Delgado, J.J. (2024).
Evaluation of the effectiveness of communication interventions to reduce loneliness in older
adults as a mental health intervention. Healthcare, 12(1), 1-30
https://doi.org/10.3390/healthcare12010062
Taylor, H.O., Cudjoe, T.K., Bu, F., & Lim, M.H. (2023). The role of loneliness in human crime
research: Current knowledge and future directions. BMC Public Health, 23,
https://doi.org/10.1186/s12889-023-15967-3
Zheng, Q., Liu, F., Xu, S., Hue, J., Haixing Lu., & Liu, T. (2023). Artificial intelligence boosts
research into loneliness, depression and anxiety – Using Covid-19 as an opportunity. Journal of
Security and Resilience, 4 (2023), 396-409. https://doi.org/10.1016/j.jnlssr.2023.10.002
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