Ai-enhanced Learning
Ai-enhanced Learning - Related Videos
10:06
An Introduction to Learning and Memory
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2023
Learning is the process of acquiring new information and memory is the retention or storage of that information. Different types of learning, such as non-associative and associative learning, and different types of memory, such as long-term and short-term memory, have been associated with human behaviors. Studying these components in detail helps behavioral scientists understand the neural mechanisms behind these two complex phenomena. JoVE's overview on learning and memory introduces common...
06:02
Visual Statistical Learning
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University The visual environment contains massive amounts of information involving the relations between objects in space and time; certain objects are more likely to appear in the vicinity of other objects. Learning these regularities can support a wide array of visual processing, including object recognition. Unsurprisingly, then, humans appear to learn these regularities automatically, quickly, and without conscious awareness. The name...
11:14
Learning and Memory: The Remember-Know Task
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2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California Our experience of memory is varied and complex. Sometimes we remember events in vivid detail, while other times we may only have a vague sense of familiarity. Memory researchers have made a distinction between memories that are recollected versus those that are familiar. A recollected item is one that is not only remembered, but carries with it details of the time at which it was learned or encoded.
05:49
Stress-Enhanced Fear Learning, a Robust Rodent Model of Post-Traumatic Stress Disorder
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Cited by 44 •
2018
Here we describe the detailed methodology required to conduct stress-enhanced fear learning (SEFL) experiments, a preclinical model of post-traumatic stress disorder, in both rats and mice. The model utilizes aspects of Pavlovian fear conditioning and freezing as an index of enhanced fear in rodents.
11:18
Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
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Cited by 18 •
2015
A method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process.
FAQs
Machine-learning models examine performance, interaction patterns, and responses to identify changing learning needs. Based on those observations, a system can modify content, task difficulty, sequencing, or feedback rather than presenting identical material to every learner. The resulting adjustments connect observable behavior with instructional decisions, although their usefulness depends on whether the collected data accurately represents learning.
These behavioral processes provide important ways to interpret how learning changes over time. Adaptive systems can help researchers examine whether differences in reinforcement, attention, motivation, or practice correspond with changes in performance or behavior. This perspective makes the technology useful not only for instruction, but also for studying the behavioral conditions associated with acquiring knowledge, skills, or behaviors.
A system may detect changes in responses or task performance without establishing that meaningful learning has occurred. Performance is therefore an observable signal, not a complete measure of learning. Keeping this distinction in view helps researchers interpret adaptive outcomes cautiously and prevents model-driven adjustments from being treated as definitive evidence that a learner has developed durable knowledge, skills, or behaviors.
Reliability depends heavily on the quality of behavioral data used by the model and on how transparently its decisions can be understood. Poor-quality observations may lead to inappropriate changes in content, difficulty, sequencing, or feedback. Transparent decision-making allows researchers and instructors to examine how observed behavior produced an instructional response and to identify limitations in the system.
A supported workflow begins by collecting information about performance, interactions, and responses. A machine-learning model then analyzes those observations to identify learning needs, after which the system adapts instructional features such as task difficulty or feedback. Researchers can examine resulting behavioral changes in relation to reinforcement, attention, motivation, or practice, while also evaluating data quality and model transparency.
It is useful when instruction or training needs to respond to differences in observed learning behavior. By analyzing performance and interaction patterns, a system can support individualized content, sequencing, difficulty, or feedback. In behavioral science, this application also creates a way to investigate how learning-related processes influence outcomes, rather than limiting the technology to content delivery alone.
Behavioral data can reveal patterns about how people respond, interact, and perform, so privacy requires careful attention when such information is collected and analyzed. Bias is another concern because model-based adaptations may reflect limitations in the data or decision process. Researchers should therefore consider privacy, potential bias, and transparency alongside the system's instructional or training outcomes.