Evidence map›Paper›PMID 39670235›Full record

ArticleHardwareX2024

Low-cost desktop learning factory to support the teaching of artificial intelligence.

Eduardo Orozco, Paulo C Cárdenas, Jesús A López, Cinthia K Rodriguez

Abstract read
In one paragraph

Article in HardwareX, 2024. 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

4 authors.

Eduardo OrozcoDepartment of Automation and Electronics, Universidad Autónoma de Occidente, Cali, Colombia.
Paulo C CárdenasDepartment of Physics and Mathematics, Universidad Autónoma de Manizales, Manizales, Colombia.
Jesús A LópezDepartment of Automation and Electronics, Universidad Autónoma de Occidente, Cali, Colombia.
Cinthia K RodriguezDepartment of Automation and Electronics, Universidad Autónoma de Occidente, Cali, Colombia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The following document details low-cost hardware and open-source available software tools that can be combined to support active teaching methodologies like Problem-Based Learning (PBL) and incorporate work-oriented technological skills in students. This proposal presents a prototype of Open Educational Resources (OER) that integrates software and hardware tools for the specific purpose of facilitating instruction in Artificial Intelligence. The hardware consists of affordable electronic devices, including an Arduino board, servo motors, sensors, a relay and a motor, all integrated into a scaled conveyor belt. On the other hand, open software was used to implement an image classification program with different features (shape, color, size, among others). The exact construction steps, circuits, and code are presented in detail and should encourage other scientists to replicate the experimental setup, especially if they are looking for experimental teaching of artificial intelligence, since the system allows object classification using the machine learning paradigm to facilitate the teaching of artificial intelligence concepts with computer vision concepts.

Indexed as

Artificial intelligenceEducation k-12Machine learningTeaching strategy

Identifiers

PMID39670235
PMCPMC11636853

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.