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June 3, 2026Big Data and Cognitive Computing1 citationsOpen Access

Genetic Algorithm-Optimized MLPNN to Analyze Generative AI Tools' Impact on Academic Performance

A Genetic Algorithm-Optimized MLPNN to Analyze the Impact of Generative Artificial Intelligence Tools on Academic Performance—A Case Study

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Authors

LMLamyae MiaraMBMohammed El Mdeghri BenomarMBMaha Benjelloun

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Overview

Randomized trial evaluates generative AI tools' impact on academic performance in engineering students, suggesting limited predictive capacity.

Key Points

  • This study aims to analyze how generative AI tools affect academic performance using advanced modeling techniques.
  • Data collected from 294 engineering students through a structured questionnaire.
  • Initial statistical analysis indicated limited predictive accuracy regarding academic performance.
  • A hybrid model using genetic algorithms and multi-layer perceptron neural networks was developed.
  • The initial statistical model showed only 39% accuracy in predicting academic performance based on generative AI tools.
  • GA-optimized MLPNN revealed that individual GAIT-related variables have limited predictive capacity for academic outcomes.
  • The complexity of factors influencing academic success was not fully captured by the available data.

Cite This Study

Miara et al. (2026) studied this question.

synapsesocial.com/papers/6a1fc56bdee9eb8c0dce6e62https://doi.org/10.3390/bdcc10060174
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