An interventional study of AI and digital health literacy program (E2NGAGE-Literacy) in Digital Health Literacy, AI Chatbot and Aged 60 Years or Older, sponsored by National University of Singapore. Not yet recruiting. Open to participants aged 60 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-10-22.
Sponsored by National University of Singapore · Not applicable, Interventional, and Other
The goal of this pilot study is to determine the feasibility and to learn if a training program called E2NGAGE-Literacy (Empowering Elders in Navigating Generative AI for Greater E-health Literacy) can potentially help older adults in Singapore use generative AI (GenAI) chatbots safely and confidently to find and evaluate health information.
The main questions it aims to answer are:
Researchers will compare two groups:
Participants in the E2NGAGE-Literacy group will:
Participants in the waitlist group will:
Artificial intelligence (AI) is rapidly reshaping how health information is produced, distributed and consumed worldwide. Across healthcare systems and consumer markets, AI-enabled technologies - including large language modelling (LLMs) embedded in search engines, conversational agents (e.g., chatbots), recommender algorithms on social media and automated translation tools - are increasingly integrated into everyday information ecosystems. These global trends increase access to plain-language health information and can offer fast, dialogic, concise and tailored medical knowledge, with potential to close gaps in digital health disparities.
Strengthening AI literacy and digital health literacy among older adults is essential to ensure effective and safe use of online health information. AI literacy is the set of competencies that allows one to effectively interact with AI systems (e.g., generate appropriate prompts), critically evaluate AI-generated responses and leverage them as tools to accomplish tasks proficiently and ethically. Digital health literacy extends these competencies to engaging with digital technologies, including GenAI and web-based searches, in effective, safe and helpful approaches to achieve health goals. Together, these literacies can help older adults better differentiate credible from misleading information, communicate more effectively with healthcare providers using information found online, use digital health platforms with confidence and safety, and reduce harmful self-care practices.
Current research on AI literacy among older adults has focused on understanding their experiences, motivation and preferences in learning AI. Despite calls to prioritise community-based digital literacy programs in public health policies and implementation research, tailored interventions on generative AI education programs among adults remain limited and exploratory. Moreover, existing programs for older adults focus on AI literacy and digital health literacy skills in silos and lack the integration of empowering older adults to leverage and apply AI-mediated information to their individual health context.
Based on the findings of the PI's ongoing trial on a digital health literacy intervention conducted among lower-educated older adults, participants showed great interest in learning the use of AI generative chatbots and gained skills on how to evaluate the credibility of online health information.
This study will pilot E2NGAGE-Literacy to explore its potential benefits in improving AI literacy and digital health literacy among community-dwelling older adults. A mixed methods design will be adopted. Sixty older adults aged 60 years and above with limited prior exposure to GenAI will be recruited from active ageing centres of the Tzu Chi Foundation.
The E2NGAGE-Literacy program will last 6 weeks and include weekly face-to-face group lessons, including online videos, quizzes and brief homework tasks. Weekly topics include, but are not limited to, the basics of GenAI chatbots, writing health-related prompts, critically evaluating AI-generated responses, and applying online health information.
Participants in the intervention group may receive additional support where needed. Participants in the waitlist control group will receive the same program after the study period. A subset of participants in the intervention group will join focus group discussions after the program to share their experiences and provide feedback.
Data will be collected at three timepoints: baseline (pre-test), post-test (week 7) and post-test (week 12) to monitor changes in AI literacy, digital health literacy, use of AI chatbots and web-based apps, and other pre-specified outcomes. Quantitative analyses will use repeated measures approaches, and qualitative data will be analysed thematically. Participants will also be invited to share their AI-chatbot transcripts with the research team for further analysis and qualitative data triangulation.
This pilot study will provide insights into the feasibility, acceptability and preliminary changes of E2NGAGE-Literacy as a potentially scalable, culturally responsive intervention to help older adults in Singapore engage with AI-generated health information safely and confidently.
National University of Singapore is the lead sponsor of 212 studies on the registry; 54 are open to participants now.
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Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
Participants in this arm will take part in the E2NGAGE-Literacy program during the study period.
Behavioral: AI and digital health literacy program (E2NGAGE-Literacy)
Participants in this arm will continue with the usual activities provided at their active ageing centres during the study period. They will not receive the E2NGAGE-Literacy program during the study period, and will receive the same program after completing the post-intervention questionnaire.
A 6-week program delivered through weekly 1.5-hour face-to-face group lessons, supported by online videos, quizzes and homework on using generative AI chatbots for health information. The programme will be delivered in English and Chinese. Additional support may be provided if needed.
Artificial intelligence literacy
Competency in artificial intelligence (AI) literacy will be measured using the 12-item Artificial Intelligence Literacy Scale (AILS) on a 7-point Likert scale. The AILS includes four domains: awareness (ability to identify and understand AI technology), usage (ability to apply and use AI technology to accomplish tasks proficiently), evaluation (ability to analyse, select and critically evaluate AI applications and their output) and ethics (ability to be aware of the responsibilities and risks associated with the use of AI technology). Internal consistency for the tool is α=0.88, with domains ranging from α=0.73 to 0.78.
Time frame: Baseline, week 7 (post-test) and week 12 (post-test)
Digital health literacy
Competency in digital health literacy will be measured using the 24-item Digital Health Literacy Instrument 2.0 (DHLI 2.0). The DHLI 2.0 includes eight domains: operational skills, navigation skills, information searching, evaluating reliability, determining relevance, generating content, using security measures, and protecting privacy. Internal consistency for the tool is α=0.94.
Time frame: Baseline, week 7 (post-test) and week 12 (post-test)
Actual use of AI chatbot apps and web-based apps
Measured using self-reported frequency of use and time spent on web browsers, social media platforms, generative AI chatbots and mobile health applications on participants' smartphones.
Time frame: Baseline, week 7 (post-test) and week 12 (post-test)
Health information orientation
The 8-item health information orientation scale will be used to measure health information-seeking behaviour style. This five-point Likert scale comprises two domains: information engagement (α=0.66) and information avoidance (α=0.67).
Time frame: Baseline, week 7 (post-test) and week 12 (post-test)
Motivated learning behaviour
Measured using an adapted 5-item Motivated Learning Behaviour scale on a 5-point Likert scale, assessing the extent to which participants are willing to work hard and persist in learning AI (α=0.92).
Time frame: Baseline, week 7 (post-test) and week 12 (post-test)
Perceived usefulness of GenAI chatbots
Measured using a 4-item adapted subscale from the Technology Acceptance Model 3 tool (α=0.92).
Time frame: Baseline, week 7 (post-test) and week 12 (post-test)
Perceived ease of use of GenAI chatbots
Measured using a 4-item adapted subscale from the Technology Acceptance Model 3 tool (α=0.93)
Time frame: Baseline, week 7 (post-test) and week 12 (post-test)
Trust in health-related information from AI
Measured using a 3-item adapted trust subscale on a 5-point Likert scale (α=0.91)
Time frame: Baseline, week 7 (post-test) and week 12 (post-test)
Participant recruitment
The speed or proportion at which this study successfully enrolls eligible individuals
Time frame: Baseline
Retention rate
The percentage of enrolled individuals who stay in a study and complete all required parts until the end
Time frame: End of the study (week 12)
Intervention attendance
The proportion of scheduled or offered weekly intervention sessions that participants actually attend
Time frame: 6-week intervention
Completion of outcome assessment
The proportion of enrolled subjects who completed the week 12 data collection
Time frame: Baseline, week 7 (post-test) and week 12 (post-test)
No study locations are listed for this record.
Plan to share: No — Individual participant data (IPD) will not be shared because this is a small pilot study involving community-dwelling older adults in Singapore. Sharing of IPD is not planned due to privacy, ethical, and institutional data protection requirements under Singapore's Personal Data Protection Act (PDPA) and the National University of Singapore's research data management policies. Only aggregate results will be reported in publications and presentations.
Start is estimated for Oct 2026; primary completion for Mar 2027, 5 months later.
How these figures are worked out, and which studies are left out of them.
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
This study is not yet recruiting, as verified in Oct 2026. You cannot join it, but the record below documents what was studied.
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