Page 561 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
B. RELATED WORK Newer AI and machine learning techniques have
Make sure the related work you discuss should be models or transformed workload management solutions even further.
approaches that manage cognitive workload while your [action role=2] Multimodal data sets combine physiological,
real-time systems are quite diverse. An important target is environmental, and task-based metrics to train machine
integrated cognitive workload management models such as learning models of cognitive demand at high accuracy and
NASA-TLX and SWAT to measure workload using allow systems.
multidimensional measurements including time load, mental In autonomous vehicles, robotics, and virtual assistants, the
load, and physical load.
application of real-time AI-driven solutions has
These models are certainly appropriate for assessing demonstrated how juggling the workload can prevent
workload in a variety of environments, so they are cognitive overload and support decision-making. We also
significant in terms of contrasting the Jarvis Model base an artificial agent on this information, here we can
(especially feedback loops and cognitive load management). further enhance this by projecting workload rather than just
These models look to assess workload across diverse analysing it with machine learning and providing
environments, so they are relevant as we compare the Jarvis suggestions for the future in environments where it can be
Model, especially when we look at how this impacts challenging like slated environments.
cognitive load through feedback loops.
Multitasking applications, critical decision-making systems,
Another interesting comparison could be made with and human-AI collaboration frameworks could further
adaptive interfaces for cognitive workload control, which demonstrate their relevance to addressing cognitive
rely on user feedback and task complexity to adjust workload in the modern era.
dynamically. For example, in domains like Human-Computer
Interaction (HCI), real-time feedback loops are crucial for However, in high-risk domains such as aviation, air traffic
control, and emergency response, cognitive workload
identifying how cognitive workload is processed in real-
models are vital for safety and performance. Research
time (Sherdil & Finzi, 2023), and provide insight into how
indicates that cognitive overload in such environments
the implementation of the Jarvis Model may integrate or
causes errors, delays, and decreased efficiency. Existing
enhance such feedback loops from a performance
models have been embedded into systems that supervise
standpoint.
and adapt workload, adaptive automation in air traffic
Moreover, Knowledge load theory (CLT), extensively used in organizations, and decision-support methods in emergency
learning settings, offers additional insight into how task rooms.
difficulty can be amended to reduce cognitive load. This
concept has broadened into neuroeconomics and These domains are a superb match to the Jarvis Model and
physiological metrics (e.g., EEGs and fNIRS) employed to its use cases in real-time cognitive tuning.
monitor and modulate live cognitive states. This is even Research on explainable AI (XAI), for example, has shown
more needful if the Jarvis Model consumes physiological that reducing the “cognitional friction” experienced by users
data for workload assessment. by improving transparency and trust in AI systems is
beneficial. The Jarvis Model could be instrumental for the
Meanwhile, AI and machine learning-based workload team and human-AI workload management if it successfully
prediction–algorithms that use historical and real-time data incorporates solutions over time, ultimately enabling each
to adjust workload seamlessly on the fly are increasingly team to grow human-AI interactions in various domains that
applied in high-stakes situations such as autonomous need vehicle autonomy (e.g. autonomous vehicles, industrial
systems and robotics. These AI solutions provide valuable robotics, military operations, etc.).
lessons on how the Jarvis Model would incorporate the
mentioned real-time adaptability. For example, cognitive load theory, which describes how
much information our brains can take in and process at any
Dynamic VR and AR systems are calibrated on user workload
one time, establishes a framework upon which the Jarvis
and can give useful parallels on how cognitive workload Model’s unique aspects can be built. This was criticized as
may be managed in immersive or interactive systems.
relying on subjective measures or not useful in dynamic,
Autonomous systems: Adaptive systems and autonomous real-time environments. Improving on these shortcomings,
assistants are specific domains of on-the-fly workload the Jarvis Model could be the holy grail of an integrated
models that are still evolving, on-the-fly workload models solution for workload management across many
can help increase their practical applications in those environments and user needs.
domains.
One such developing aspect in workload research is the use
By comparing these models in various contexts, be it in
of cognitive workload models together with emotional
medical decision-making, military operations, or training
states. Research shows that emotions such as frustration,
simulations, you could contextualize the Jarvis Model within
anxiety, and overconfidence can strongly affect
the larger framework of cognitive workload solutions,
performance. Emotion-ware systems analyze multimodal
showcasing its advantages and potential improvement
data (e.g., facial expressions, voice tone, etc.) to identify a
points.
user’s emotional state and tailor the task management.

IJTSRD | Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies Page 551