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AI-Enabled Digital Literacy Support

Yeweon Kim et al.

Journal of Community Informatics2026https://doi.org/10.15353/joci.v22i2.6499article
ABDC C
Weight
0.50

What the paper says

As digital literacy support (DLS) programs and initiatives increasingly integrate artificial intelligence (AI) tools, they are gradually replacing human-led tech assistance or education provided by public service institutions (e.g., libraries, schools, community centers, non-profits). This study explores the readiness of DLS-seekers to accept and utilize AI-enabled DLS (AI-DLS), focusing on their perceptions of its benefits and barriers compared to human-led DLS, as well as their trust and ethical concerns about AI. We conducted interviews through community outreach facilitated by Marylanders Online––a state-funded digital equity initiative––targeting a group of Maryland residents who had used DLS. Our findings reveal a strong openness to AI-DLS, with DLS-seekers eager to stay current as well as engage in human-like interactions with conversational AI agents. They highly valued advanced, instant information from AI-DLS, along with the added advantages of confidential and linguistically diverse support, surpassing traditional human-led DLS options. Concerningly, they overlooked the ethical risks of AI in daily life, placing undue trust in its capabilities and underestimating potential vulnerabilities. We conclude by providing theoretical implications of this work and practical recommendations to optimize AI-DLS, drawing from the voices of DLS-seekers advocating for institutional interventions to ensure its broader accessibility and equitable implementation.

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https://doi.org/https://doi.org/10.15353/joci.v22i2.6499

Or copy a formatted citation

@article{yeweon2026,
  title        = {{AI-Enabled Digital Literacy Support}},
  author       = {Yeweon Kim et al.},
  journal      = {Journal of Community Informatics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.15353/joci.v22i2.6499},
}

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AI-Enabled Digital Literacy Support

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.