Ai-assisted Instruction
Ai-assisted Instruction - Related Videos
10:26
Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
0 Views •
Cited by 1 •
2021
This protocol guides researchers and educators through implementation of the Problem-Solving before Instruction approach (PS-I) in an undergraduate statistics class. It also describes an embedded experimental evaluation of this implementation, where the efficacy of PS-I is measured in terms of learning and motivation in students with different cognitive and affective predispositions.
08:55
Using the Sleeve Technique in a Mouse Model of Aortic Transplantation - An Instructional Video
0 Views •
Cited by 6 •
2017
We present an orthotopic aortic transplantation model using the sleeve technique in mice. It is a very rapid anastomosis method, which can be employed in studies of vascular disease.
08:34
Robot-assisted Partial Splenectomy
0 Views •
2026
This protocol describes a robot-assisted partial splenectomy for a benign splenic tumor. The robotic approach optimizes spleen preservation by enhancing surgical precision, minimizing blood loss, and improving postoperative outcomes. Key surgical steps include meticulous splenic hilum dissection, selective vascular ligation, intraoperative ultrasonography, and controlled parenchymal transection.
05:25
An Experiment Using Functional Near-Infrared Spectroscopy and Robot-Assisted Multi-Joint Pointing Movements of the Lower Limb
0 Views •
Cited by 1 •
2024
It is estimated that 1 in 6 individuals worldwide will have a stroke in their lifetime, causing long-term disability, whose rehabilitation mechanisms are still poorly understood. This study proposes a protocol to evaluate brain activation by functional near-infrared spectroscopy (fNIRS) during a lower limb robotic therapy session.
05:39
Non-Intubated Video-Assisted Thoracoscopic Surgery
0 Views •
Cited by 4 •
2023
Here, we present a non-intubated protocol for performing video-assisted thoracoscopic surgery with preserved autonomic breathing.
FAQs
Adaptation depends on linking learner inputs to detectable performance patterns. The system can use those patterns to alter prompts or practice activities as needs change, rather than presenting identical instruction throughout training. This mechanism is useful when instruction must respond to ongoing performance, but its value still depends on whether the adjustments support the intended learning or behavior-focused goal.
Human oversight keeps automated recommendations tied to well-designed goals and meaningful behavior. People must judge whether a suggested prompt or activity addresses the target skill and whether observed changes matter in context. This review is important because a system may detect patterns in performance without establishing that its recommendations produce a useful, observable improvement.
Evaluation should focus on observable changes in behavior, not only on the system’s ability to analyze inputs or generate feedback. Researchers and educators can examine responses over time and ask whether instruction supports skill acquisition or other defined goals. This distinction separates technical activity, such as producing a prompt, from evidence that the learner’s behavior has improved.
A basic implementation begins by specifying learning or behavior-focused goals, then organizing a structured learning protocol around those goals. The system can receive learner inputs, analyze performance patterns, provide feedback, and adjust prompts or practice activities. Responses should be documented over time so human reviewers can compare the resulting behavior with the intended outcomes.
In behavior-focused training, the tools can support structured skill-acquisition activities while responding to changes in performance. Adaptive prompts and practice tasks may help keep instruction aligned with the learner’s current needs, and recorded responses provide a basis for reviewing progress. The approach is most relevant when training requires organized practice and outcomes can be observed over time.
Ai-assisted Instruction is relevant to behavioral research because it can help implement structured learning protocols and organize information about responses across time. This documentation may support evaluation of whether a training plan is producing meaningful, observable improvements. It does not replace interpretation: human oversight remains necessary to decide whether automated recommendations are appropriate for the behavioral goal.