AVIA 735
ARTICLE CRITIQUE ASSIGNMENT INSTRUCTIONS
OVERVIEW
Locating scholarly sources is key when gathering and understanding information for your
research, namely related to human factors and aviation automation for this course. This
assignment will help you become more comfortable when reviewing and selecting different types
of sources for your research.
INSTRUCTIONS
For this assignment, you will research and select 3 articles written five years ago or less that are
related to human factors and aviation automation. You will then summarize the articles, critique
of the articles (which may include how you may apply the conclusions and ideas presented) and
share a Biblical Worldview in a 2-3 page paper. All articles and books for this assignment must
be as current as possible and must be cited using current APA format. (CLO: A, B, C, F).
This assignment must:
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Utilize at least 3 scholarly, peer-reviewed articles.
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Be in current APA format.
•
Clearly and concisely describe the contents and organization of the assignment.
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Follow all guidance and instruction/feedback given.
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Be at least 2- pages in length, but not more than 3- pages (500- 750 words). Student must
include title, abstract, and reference pages; however, this does not count towards the
minimum 2- page length nor does it go against the maximum 3- pages in length.
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Be submitted.
The assignment should utilize formal, academic writing standards and adhere to current APA
formatting guidelines.
Note: Your assignment will be checked for originality via the Turnitin plagiarism tool.
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Article Critique: Part I Assignment
Mr. Stefano Bitjoka
Liberty University
AVIA735: Human Factors and Aviation Automation
Dr. Amber Wilson
9/3/2023
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Aptitude Requirements for Human Operators in Human–Automation Interaction
The “Aptitude Requirements for Human Operators in Human–Automation Interaction”
by Michael Brady seeks to identify individual differences that affect aptitude requirements for
jobs involving autonomous systems and human–automation interaction(Brady, 2022). The
research problem being addressed is in two stages. First, they conducted a literature review of
task demands, and operator states relevant to human–automation interaction. This review aimed
to develop a model that can be used to understand operators’ performance based on their
behaviors and individual states. They also conducted a meta-analysis to analyze the correlation
between various criteria and differences in the job demands of workers in an automated
environment.. Results suggest cognitive skills such as working memory are essential to
performance in a human–automation interaction (Brady, 2022).
Conclusion
During the article research, there were several statistical analysis errors found within the
research. Among data, it was determined that the automated system may fail to detect a problem
and appropriately intervene, have inappropriate trust in the automation, and cause a loss of
situation awareness (SA). Objective performance criteria for monitoring tasks often include
reaction time, detection rate (correctly identifying errors made by the automated agent), and false
alarms (incorrectly identifying an error when none was made (Karpinsky et al.,2018; Molloy &
Parasuraman,1996; Sato et al.,2019). Trust has implications for the operator’s effective use of
automation, affecting performance on monitoring and multitasking. Having too much trust in
automation between cognitive ability and job performance depends on job complexity. Attention
to detail might contribute to these performance decrements in an HAI context and inhibit reliance
on automation (Lin,2017).
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Lessons Lost: What We Learned About Automation in Aviation Can Be Applied to
Autonomous Vehicles
In “Lessons Lost: What we learned about automation in aviation in aviation can address
interaction,” V. Gawron seeks to address a series of research and accidents conducted before
2000, which can be related to commercial aviation automation with results from past lessons.
The research problem being addressed is whether NTSB has yet to apply all results from
automation in commercial aviation and self-driven automobiles. A case in point is the NTSB
investigation on the “Collision Between a Car Operating with Automated Vehicle Control
Systems and a Tractor-Semitrailer Truck Near Williston, Florida May 7, 2016″ (Executive
summary HAR1702). The economic growth rate has affected all areas of aviation. This research
points out the loss of talent with automation experience due to employee’s retirement or retired.
Although much work has been done since 2000, I am focusing on the research before 2000 since
this earlier work has not been well-known to current practitioners (Hart, 2019).
Conclusion
This article displayed essential points, and examples were given to explain the necessary needs for
lessons learned—the March 23, 2018, crash of a Tesla Model X near Mountain View, California. Tesla’s
advanced driver assistance systems, which are referred to as autopilot, were being used by the driver.
These include lane-keeping and traffic-aware cruise control. As the Tesla approached the paved gore
area dividing the main travel lanes of US-101 from the SH-85 exit ramp, it moved to the left and entered
the gore area” (NTSB, 2018a, Executive Summary HWY18FH011). Each lesson provides the aviation and
automobile communities with knowledge of all the challenges and errors of using automation—for
example, Automatic Flight Control Systems, Autopilot Systems, and Flight Management Systems.
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Dirk Pons and Jones Aust’s study compares human operators’ performance with that of
advanced technologies, such as AI, 3D scanning, and image processing, in the inspection of aero
engine blades. It also looks into how these technologies perform when compared to the
inspection capabilities of human workers. The article discusses a statistically analyzed
breakdown of human factors within the ram of accuracy, consistency, and time. According to the
Federal Aviation Authority (FAA) and the International Air Transport Association (IATA),
incorrect maintenance has started the event chain of every third accident and every fourth fatality
(Rankin, 2012). It is stated that 80% of maintenance errors and accounts are attributed to human
error. The research also shows that structural failures are the main reason for maintenancerelated incidents, mainly associated with the aircraft engines and subcomponents like the engine
blades. The article focuses on the visual inspection of the blades conducted by humans. The
research believes human operators could be more reliable, subjective, and consistent when
dealing with engine blade inspections. They presented an advanced robotic automation system to
inspect and repair engine parts such as shafts, fan blades, compressor blades, and turbine blades.
The robotic device was described as a snakelike robot that combines the function of a borescope
and is similar to endoscopic used by surgical doctors.
Conclusion
As a former aircraft systems inspector, it was interesting to read that replacing humans with
robotic systems is the future wave. Using 3D scanners to detect nicks, dents, or tears is a
significant investment. However, combining humans and machines would lower possible errors
during inspections of engine blades and subcomponents. The results of this article showed a
contribution to a better understanding of the quality management system and the abilities of both
humans and machines.
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Biblical Worldview
God is not a hardliner in using technology to further his purposes. In other words, he is not
entirely for or against technology. However, this does not mean God is okay with how we use
technology. “All things are lawful for me,” but not all things are helpful. “All things are lawful
for me,” but I will not be dominated by anything. (1 Cor. 6:12).
.
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References
Gawron, V. (2020). Lessons Lost: What We Learned About Automation in Aviation Can Be
Applied to Autonomous Vehicles. Ergonomics in Design, 28(4), 24–27.
https://doi.org/10.1177/1064804619872320.
Brady, Michael E., et al. “Aptitude Requirements for Human Operators in Human–Automation
Interaction.” 2022, https://doi.org/10.1027/2192-0923/a000210.
Aust, J., & Pons, D. (2022). Comparative Analysis of Human Operators and Advanced
Technologies in the
Visual Inspection of Aero Engine Blades. Applied Sciences,
12(4), 2250.
https://doi.org/10.3390/app12042250.
Hart C. (2019, March 11). Self-driving safety steps into the unknown. The Drive.
https://www.thedrive.com/tech/26896/self-driving-safety-steps-into-the-unknown
National Transportation Safety Board. (2016). The collision between a car operating with
automated vehicle control systems and a tractor-semitrailer truck near Williston, Florida,
May 7, 2016 (HAR1702).
https://www.ntsb.gov/investigations/AccidentReports/Pages/HAR1702.aspx
National Transportation Safety Board. (2018a). Preliminary report: Crash and post-crash fire of
electric-powered passenger vehicle (HWY18FH011 Preliminary).
https://www.ntsb.gov/investigations/AccidentReports/Pages/HWY18FH011preliminary.aspx
Rankin, W.L.; Shappell, S.; Wiegmann, D. Error and error reporting systems. Hum. Factors
Guide Aviat. Maint. Insp.; 2003; Available online:
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https://www.faa.gov/about/initiatives/maintenance_hf/training_tools/media/hf_guide.pdf
(accessed on November 17 2018).