Evidence map›Paper›PMID 42688401›Full record

ArticleFrontiers in artificial intelligence2026

AI-mediated intergenerational skill transfer: enhancing ESG literacy and sustainable consumption among aging populations.

Anuja Paul, Astle, Sujata Roy

Abstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Anuja PaulManipal Law School, Manipal Academy of Higher Education, Manipal, India.
AstleGitam School of Law, Gandhi Institute of Technology and Management, Visakhapatnam, India.
Sujata RoyThe West Bengal National University of Juridical Sciences, Kolkata, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As nations face two significant megatrends-demographic aging and digital transformation-there is a need to align technological innovation with overarching sustainability goals. This study examines the application of Artificial Intelligence (AI) to improve intergenerational skill transfer, thereby enabling aging populations to substantially contribute to the enhancement of Environmental, Social, and Governance (ESG) literacy and Sustainable Development Goal 12 (Responsible Consumption and Production). This examines the potential collaboration between AI-driven lifelong learning platforms and the engagement of elderly individuals as mentors in sustainability education. The study is guided by three primary inquiries: How can AI-assisted learning environments enhance the capacity of older individuals to transmit ESG-related skills? What impact do AI-personalized, senior-led sustainability programs have on the behaviors and attitudes of younger learners? What ethical and design issues are essential to ensuring that AI technologies equitably support older educators in sustainability transitions? This study uses a stepwise mixed-methods approach, beginning with a thorough assessment of AI applications in adult and intergenerational ESG education. Following this, quantitative data will be collected through interviews with elder participants using AI-assisted sustainability platforms, in conjunction with the qualitative analysis of pre- and post-intervention data to assess environmental attitudes among youth learners. The objectives of this study are to explore how AI-assisted learning environments enhance the capacity of elderly individuals to transmit ESG-related skills and to identify what ethical and design challenges are essential to ensure that AI technologies equitably support elderly individuals in transitioning to sustainability practices. This qualitative study addresses a research gap in AI for Good applications for inclusive environmental governance and digital aging. This study contributes to the expanding domains of AI for good, inclusive environmental governance, and digital aging by presenting a novel framework that integrates human knowledge with machine intelligence to foster sustainable futures.

Indexed as

ageingartificial intelligenceESGSDG 12skill transfer

Identifiers

PMID42688401
PMCPMC13534070

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.