AI-POWERED CONTENT EVALUATION

This paper introduces an intelligent assessment method based on AI, designed to overcome the limitations of existing evaluation techniques. The method comprises three core modules: (1) Data Preprocessing, responsible for cleaning, labeling, and feature extraction of generated content; (2) Multi-dimensional Evaluation, which assesses content using pre-trained language models and deep learning across multiple angles; and (3) Dynamic Adaptive Module, integrating learner feedback to refine assessment results dynamically.

The multi-dimensional evaluation framework is foundational, covering four key dimensions: Knowledge Accuracy (consistency with domain knowledge), Logical Consistency (reasonableness of internal relationships), Alignment with Teaching Objectives (match to predefined objectives), and Learner Adaptability (dynamic adjustment based on feedback and cognitive levels). The final evaluation score is a weighted fusion of these dimensions, ensuring a holistic and nuanced assessment of educational content quality.

At the heart of our efficient and accurate multi-dimensional evaluation model is the strategic combination of pre-trained language models (PLMs) and Generative Adversarial Network (GAN) technology. PLMs, such as BERT, are employed to extract rich semantic features from the generated educational content. Each sentence is transformed into a d-dimensional vector representation, leveraging BERT's multi-layer Transformer encoder architecture, which incorporates multi-head self-attention mechanisms and feed-forward neural networks.

The GAN component further refines the assessment. It consists of a generator (G) that produces educational content and a discriminator (D) that distinguishes generated content from real content, providing an evaluation score. The discriminator's objective is to maximize its ability to correctly identify real vs. generated data, while the generator's goal is to produce content so realistic that it deceives the discriminator. This adversarial training mechanism significantly enhances the accuracy and robustness of the evaluation model.

A critical innovation of this method is the introduction of a dynamic adaptive module powered by a learner feedback mechanism. This feature ensures that the assessment model is not static but continuously refines its parameters based on real-world interaction and learner needs. Feedback data, such as satisfaction ratings or comprehension levels, is collected from learners.

This feedback is then utilized within a reinforcement learning framework to adjust the evaluation model's parameters. By minimizing a loss function (e.g., squared difference between predicted and true scores), the model dynamically learns to align its assessments more closely with effective learning outcomes. This continuous adjustment capability enhances personalization, ensuring the generated educational content remains highly relevant and effective for diverse learners and evolving educational contexts.

The proposed AI-based intelligent assessment method underwent rigorous experimental validation on both public (EDU-Content) and self-built datasets. On the public EDU-Content dataset, our method demonstrated superior performance across all evaluation indicators compared to Rule-Based, SVM-Based, LSTM-Based, and BERT-Based methods. Notably, it achieved 90.2% in Knowledge Accuracy and 85.7% in Learner Adaptability, significantly outperforming the next best method (BERT-Based).

Further experiments on a self-built dataset revealed the critical impact of the dynamic adaptive module. As the volume of learner feedback data increased (from 100 to 1000 points), both Learner Adaptability (LA) and Overall Score (OS) showed significant improvements, validating the effectiveness of the feedback mechanism. This confirms the method's wide applicability and robustness in real-world educational scenarios.