First published 9/28/2026, 7:06:15 AM · Last updated 9/28/2026, 7:06:30 AM

A multi-dimensional evaluation algorithm for physical education teaching quality integrating explainable AI and adaptive analytic hierarchy process

This is a journal article published by Elsevier BV on 2026-12-01 describing a multi-dimensional evaluation algorithm for physical education teaching quality that integrates explainable AI and an adaptive analytic hierarchy process. The supplied source data contains only the article title, publisher, content type, and publication date with no additional event details.

Categories: technology, positive-progress

Generated scores

Scores are based on the cited reporting and use a 1–10 scale. Read the methodology.

Confidence: 6/10How certain the assessment is based on the clarity and evidence in the cited reporting.
Geographic reach: 1/10How widely the event reaches, from a local area (1) to worldwide impact (10).
Global importance: 2/10The event's broader significance, from limited importance (1) to major global importance (10).
Impact magnitude: 2/10The scale or severity of the real-world impact, from minor (1) to extreme (10).
Positivity: 6/10How harmful or beneficial the event is, from strongly harmful (1) to strongly positive (10).
Urgency: 1/10How time-sensitive the event is, from low urgency (1) to immediate attention needed (10).

Why it matters

The article presents a research method combining explainable AI and an analytic hierarchy process for evaluating physical education teaching quality.

Sources

A multi-dimensional evaluation algorithm for physical education teaching quality integrating explainable AI and adaptive analytic hierarchy process

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