Evidence map›Paper›PMID 42042239›Full record

ArticleJournal of Intelligence2026

Are We Helping Workers Reskill for the Future of Work? Using AI to Explore the Alignment of Online Course Offerings and Job Skill Requirements.

Makai A Ruffin, Margaret E Beier, Felix Y Wu, Nathaniel M Voss, Anoop A Javalagi, Harrison J Kell

Abstract read
In one paragraph

Article in Journal of 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
–field-weighted citation impact
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

6 authors.

Makai A RuffinDepartment of Psychological Sciences, Rice University, Houston, TX 77005, USA.ORCID 0000-0003-4101-9966
Margaret E BeierDepartment of Psychological Sciences, Rice University, Houston, TX 77005, USA.ORCID 0000-0001-5761-5610
Felix Y WuHuman Resources Research Organization (HumRRO), Alexandria, VA 22314, USA.ORCID 0000-0001-8008-6390
Nathaniel M VossHuman Resources Research Organization (HumRRO), Alexandria, VA 22314, USA.ORCID 0000-0002-1595-0987
Anoop A JavalagiHuman Resources Research Organization (HumRRO), Alexandria, VA 22314, USA.ORCID 0000-0002-4221-3456
Harrison J KellHuman Resources Research Organization (HumRRO), Alexandria, VA 22314, USA.ORCID 0000-0002-9401-5371

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Millions of workers and job seekers turn to online platforms to gain work-relevant skills to remain competitive for the future of work. However, little is known about whether the skills acquired in work-relevant online courses align with the skills required for 21st-century jobs. Drawing on literature on job and skill matching, this exploratory study examines the alignment between available online training and learning content and the skills demanded by jobs (i.e., training-skills demands fit) using artificial intelligence methods. A large language model (LLM; Claude Haiku 3.5) was instructed to evaluate which of the 35 basic and cross-functional skills from the Occupational Information Network (O*NET) could be acquired in a given course, which was based on 2549 course descriptions extracted from MIT OpenCourseWare. Linkages between online training and skills were broken down by job family and occupations with a bright outlook designation (i.e., occupations estimated to have 75,000 or more job openings between 2024 and 2034 across the United States). Results suggest that the skill of active learning (i.e., using new information for problem-solving; 88%,

Indexed as

artificial intelligencefuture of workreskillingtalent development

Identifiers

PMID42042239
PMCPMC13117798

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.