AI Literacy Education for Older Adults: Motivations, Challenges and Preferences

TL;DR

The study investigates AI literacy education for adults aged 50 and above, identifying strong motivation and perceived importance, while also noting challenges such as understanding and initial access. Using an online survey (N=103) with quantitative scales and qualitative analysis, the authors find a dominant preference for hands-on, practical learning and a strong favoring of Accommodating learning style. Design opportunities are proposed to teach AI through relevance, emphasize experiential learning, and leverage social learning, including peer and non-human peers. These findings offer empirical guidance for developing age-inclusive AI literacy curricula and highlight the need for diverse, real-world, and supportive learning environments to empower older adults to navigate AI ethically and effectively.

Abstract

As Artificial Intelligence (AI) becomes increasingly integrated into older adults' daily lives, equipping them with the knowledge and skills to understand and use AI is crucial. However, most research on AI literacy education has focused on students and children, leaving a gap in understanding the unique needs of older adults when learning about AI. To address this, we surveyed 103 older adults aged 50 and above (Mean = 64, SD = 7). Results revealed that they found it important and were motivated to learn about AI because they wish to harness the benefits and avoid the dangers of AI, seeing it as necessary to cope in the future. However, they expressed learning challenges such as difficulties in understanding and not knowing how to start learning AI. Particularly, a strong preference for hands-on learning was indicated. We discussed design opportunities to support AI literacy education for older adults.

Figures (4)

• Figure 1: Distribution of scores for Likert-scale survey questions on demographic information. The x-axis (bottom) shows the percentage distribution of participants across Likert-scale intervals, and the x-axis (top) indicates the mean score for each item. Colors represent different score intervals, ranging from light blue (1.0–1.5) to dark blue (4.5–5.0). The yellow circle on each bar marks the mean score for the corresponding item. The mean scores, from top to bottom, are 3.06, 3.34, 3.82, and 4.02.
• Figure 2: Distribution of scores for Likert-scale survey questions about participants' perceived importance of AI literacy education and motivation to learn AI (RQ1). The x-axis (bottom) shows the percentage distribution of participants across Likert-scale intervals, and the x-axis (top) indicates the mean score for each item. Colors represent different score intervals, ranging from light blue (1.0–1.5) to dark blue (4.5–5.0). The yellow circle on each bar marks the mean score for the corresponding item. The mean scores, from top to bottom, are 4.06, 4.13, 4.19, 4.24 and 4.27.
• Figure 3: Preferred learning style indicated by participants among options modeled using Kolb's Learning Theory. Colors represent different score intervals of participants' competency in digital literacy, ranging from light blue (1.0–1.5) to dark blue (4.5–5.0).
• Figure 4: Ways that participants used to gain information or learn about AI.